diff --git "a/notebooks/lilabc_CT.ipynb" "b/notebooks/lilabc_CT.ipynb"
--- "a/notebooks/lilabc_CT.ipynb"
+++ "b/notebooks/lilabc_CT.ipynb"
@@ -17,14 +17,6 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/1962403499.py:1: DtypeWarning: Columns (7,8,9,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df = pd.read_csv(\"lila_image_urls_and_labels.csv\")\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -47,15 +39,15 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
        "      <th>image_id</th>\n",
        "      <th>sequence_id</th>\n",
        "      <th>location_id</th>\n",
        "      <th>frame_num</th>\n",
        "      <th>original_label</th>\n",
        "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
        "      <th>...</th>\n",
        "      <th>suborder</th>\n",
        "      <th>infraorder</th>\n",
@@ -73,15 +65,15 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f2160eb-5567-11e8-990e...</td>\n",
        "      <td>Caltech Camera Traps : 26</td>\n",
        "      <td>1</td>\n",
        "      <td>empty</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>10-04-2013 13:31:53</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -97,15 +89,15 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...</td>\n",
        "      <td>Caltech Camera Traps : 26</td>\n",
        "      <td>1</td>\n",
        "      <td>deer</td>\n",
        "      <td>odocoileus</td>\n",
-       "      <td>deer</td>\n",
-       "      <td>11-04-2013 18:37:07</td>\n",
        "      <td>...</td>\n",
        "      <td>ruminantia</td>\n",
        "      <td>NaN</td>\n",
@@ -121,15 +113,15 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...</td>\n",
        "      <td>Caltech Camera Traps : 38</td>\n",
        "      <td>2</td>\n",
        "      <td>cat</td>\n",
        "      <td>felis catus</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>05-09-2012 07:33:45</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -145,15 +137,15 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...</td>\n",
        "      <td>Caltech Camera Traps : 38</td>\n",
        "      <td>2</td>\n",
        "      <td>opossum</td>\n",
        "      <td>didelphis virginiana</td>\n",
-       "      <td>virginia opossum</td>\n",
-       "      <td>03-29-2012 02:34:13</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -169,15 +161,15 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5a1e5306-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f0a3ccf-5567-11e8-92fb...</td>\n",
        "      <td>Caltech Camera Traps : 33</td>\n",
        "      <td>2</td>\n",
        "      <td>empty</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>05-08-2012 19:23:36</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -192,60 +184,67 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 30 columns</p>\n",
+       "<p>5 rows × 32 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "           dataset_name                                                url   \n",
-       "0  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "1  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "2  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "3  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "4  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "\n",
-       "                                            image_id   \n",
-       "0  Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3...  \\\n",
+       "           dataset_name                                            url_gcp  \\\n",
+       "0  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "1  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "2  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "3  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "4  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                             url_aws  \\\n",
+       "0  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "1  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "2  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "3  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "4  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "\n",
+       "                                           url_azure  \\\n",
+       "0  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "1  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "2  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "3  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "4  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "\n",
+       "                                            image_id  \\\n",
+       "0  Caltech Camera Traps : 5968c0f9-23d2-11e8-a6a3...   \n",
        "1  Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...   \n",
        "2  Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...   \n",
        "3  Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...   \n",
        "4  Caltech Camera Traps : 5a1e5306-23d2-11e8-a6a3...   \n",
        "\n",
-       "                                         sequence_id   \n",
-       "0  Caltech Camera Traps : 6f2160eb-5567-11e8-990e...  \\\n",
+       "                                         sequence_id  \\\n",
+       "0  Caltech Camera Traps : 6f2160eb-5567-11e8-990e...   \n",
        "1  Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...   \n",
        "2  Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...   \n",
        "3  Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...   \n",
        "4  Caltech Camera Traps : 6f0a3ccf-5567-11e8-92fb...   \n",
        "\n",
-       "                 location_id  frame_num original_label       scientific_name   \n",
-       "0  Caltech Camera Traps : 26          1          empty                   NaN  \\\n",
+       "                 location_id  frame_num original_label       scientific_name  \\\n",
+       "0  Caltech Camera Traps : 26          1          empty                   NaN   \n",
        "1  Caltech Camera Traps : 26          1           deer            odocoileus   \n",
        "2  Caltech Camera Traps : 38          2            cat           felis catus   \n",
        "3  Caltech Camera Traps : 38          2        opossum  didelphis virginiana   \n",
        "4  Caltech Camera Traps : 33          2          empty                   NaN   \n",
        "\n",
-       "        common_name             datetime  ...    suborder infraorder   \n",
-       "0               NaN  10-04-2013 13:31:53  ...         NaN        NaN  \\\n",
-       "1              deer  11-04-2013 18:37:07  ...  ruminantia        NaN   \n",
-       "2               cat  05-09-2012 07:33:45  ...         NaN        NaN   \n",
-       "3  virginia opossum  03-29-2012 02:34:13  ...         NaN        NaN   \n",
-       "4               NaN  05-08-2012 19:23:36  ...         NaN        NaN   \n",
-       "\n",
-       "  superfamily       family    subfamily        tribe       genus   \n",
-       "0         NaN          NaN          NaN          NaN         NaN  \\\n",
-       "1         NaN     cervidae  capreolinae  odocoileini  odocoileus   \n",
-       "2         NaN      felidae      felinae          NaN       felis   \n",
-       "3         NaN  didelphidae  didelphinae   didelphini   didelphis   \n",
-       "4         NaN          NaN          NaN          NaN         NaN   \n",
+       "   ...    suborder infraorder superfamily       family    subfamily  \\\n",
+       "0  ...         NaN        NaN         NaN          NaN          NaN   \n",
+       "1  ...  ruminantia        NaN         NaN     cervidae  capreolinae   \n",
+       "2  ...         NaN        NaN         NaN      felidae      felinae   \n",
+       "3  ...         NaN        NaN         NaN  didelphidae  didelphinae   \n",
+       "4  ...         NaN        NaN         NaN          NaN          NaN   \n",
        "\n",
-       "                species subspecies variety  \n",
-       "0                   NaN        NaN     NaN  \n",
-       "1                   NaN        NaN     NaN  \n",
-       "2           felis catus        NaN     NaN  \n",
-       "3  didelphis virginiana        NaN     NaN  \n",
-       "4                   NaN        NaN     NaN  \n",
+       "         tribe       genus               species subspecies variety  \n",
+       "0          NaN         NaN                   NaN        NaN     NaN  \n",
+       "1  odocoileini  odocoileus                   NaN        NaN     NaN  \n",
+       "2          NaN       felis           felis catus        NaN     NaN  \n",
+       "3   didelphini   didelphis  didelphis virginiana        NaN     NaN  \n",
+       "4          NaN         NaN                   NaN        NaN     NaN  \n",
        "\n",
-       "[5 rows x 30 columns]"
+       "[5 rows x 32 columns]"
       ]
      },
      "execution_count": 2,
@@ -254,28 +253,29 @@
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../data/lila_image_urls_and_labels.csv\")\n",
+    "df = pd.read_csv(\"../data/lila_image_urls_and_labels.csv\", low_memory = False)\n",
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['dataset_name', 'url', 'image_id', 'sequence_id', 'location_id',\n",
-       "       'frame_num', 'original_label', 'scientific_name', 'common_name',\n",
-       "       'datetime', 'annotation_level', 'kingdom', 'phylum', 'subphylum',\n",
-       "       'superclass', 'class', 'subclass', 'infraclass', 'superorder', 'order',\n",
-       "       'suborder', 'infraorder', 'superfamily', 'family', 'subfamily', 'tribe',\n",
-       "       'genus', 'species', 'subspecies', 'variety'],\n",
+       "Index(['dataset_name', 'url_gcp', 'url_aws', 'url_azure', 'image_id',\n",
+       "       'sequence_id', 'location_id', 'frame_num', 'original_label',\n",
+       "       'scientific_name', 'common_name', 'datetime', 'annotation_level',\n",
+       "       'kingdom', 'phylum', 'subphylum', 'superclass', 'class', 'subclass',\n",
+       "       'infraclass', 'superorder', 'order', 'suborder', 'infraorder',\n",
+       "       'superfamily', 'family', 'subfamily', 'tribe', 'genus', 'species',\n",
+       "       'subspecies', 'variety'],\n",
        "      dtype='object')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -286,20 +286,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "annotation_level\n",
-       "sequence    12710844\n",
+       "sequence    12710778\n",
        "unknown      3382215\n",
-       "image         740789\n",
+       "image        3258163\n",
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,12 +312,115 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Annotation level indicates iimage vs sequence (or unknown), not analogous to `taxonomy_level` from lila-taxonomy-mapping_release.csv. It seems `original_label` may be the analogous column."
+    "Annotation level indicates iimage vs sequence (or unknown), not analogous to `taxonomy_level` from lila-taxonomy-mapping_release.csv. It seems `original_label` may be the analogous column.\n",
+    "\n",
+    "We'll likely want to pull out the image-level before doing any sequence checks and such since those should be \"clean\" images. Though we will want to label them with how many distinct species are in the image first.\n",
+    "\n",
+    "We now have 66 less sequence-level annotations and 2,517,374 more image-level! That's quite the update! The unknown count has not changed.\n",
+    "\n",
+    "### Check Dataset Counts\n",
+    "\n",
+    "1. Make sure we have all datasets expected.\n",
+    "2. Check which/how many datasets are labeled to the image level (and check for match to [Andrey's spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link))."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset_name\n",
+       "Snapshot Serengeti                            7261545\n",
+       "NACTI                                         3382215\n",
+       "Trail Camera Images of New Zealand Animals    2453840\n",
+       "SWG Camera Traps                              2039657\n",
+       "Idaho Camera Traps                            1551552\n",
+       "WCS Camera Traps                              1369953\n",
+       "Wellington Camera Traps                        270450\n",
+       "Channel Islands Camera Traps                   245644\n",
+       "Caltech Camera Traps                           243177\n",
+       "Island Conservation Camera Traps               128207\n",
+       "Orinoquia Camera Traps                         112267\n",
+       "Snapshot Mountain Zebra                         73606\n",
+       "Desert Lion Conservation Camera Traps           63468\n",
+       "Snapshot Karoo                                  38320\n",
+       "Snapshot Camdeboo                               30717\n",
+       "Snapshot Enonkishu                              30542\n",
+       "Missouri Camera Traps                           24673\n",
+       "Snapshot Kruger                                 10637\n",
+       "Snapshot Kgalagadi                              10402\n",
+       "ENA24                                           10284\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dataset_name.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset_name                                annotation_level\n",
+       "Caltech Camera Traps                        image                243177\n",
+       "Channel Islands Camera Traps                image                245644\n",
+       "Desert Lion Conservation Camera Traps       image                 63468\n",
+       "ENA24                                       image                 10284\n",
+       "Idaho Camera Traps                          sequence            1551552\n",
+       "Island Conservation Camera Traps            image                128207\n",
+       "Missouri Camera Traps                       sequence              23397\n",
+       "                                            image                  1276\n",
+       "NACTI                                       unknown             3382215\n",
+       "Orinoquia Camera Traps                      image                112267\n",
+       "SWG Camera Traps                            sequence            2039657\n",
+       "Snapshot Camdeboo                           sequence              30717\n",
+       "Snapshot Enonkishu                          sequence              30542\n",
+       "Snapshot Karoo                              sequence              38320\n",
+       "Snapshot Kgalagadi                          sequence              10402\n",
+       "Snapshot Kruger                             sequence              10637\n",
+       "Snapshot Mountain Zebra                     sequence              73606\n",
+       "Snapshot Serengeti                          sequence            7261545\n",
+       "Trail Camera Images of New Zealand Animals  image               2453840\n",
+       "WCS Camera Traps                            sequence            1369953\n",
+       "Wellington Camera Traps                     sequence             270450\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby([\"dataset_name\"]).annotation_level.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems all the unknown annotation level images are in NACTI (North American Camera Trap Images). At first glance I don't see annotation level information on HF or on [their LILA page](https://lila.science/datasets/nacti)--will require more looking.\n",
+    "\n",
+    "Desert Lion Conservation Camera Traps & Trail Camera Images of New Zealand Animals are _not_ included in the [Hugging Face dataset](https://huggingface.co/datasets/society-ethics/lila_camera_traps).\n",
+    "\n",
+    "There are definitely more in [Andrey's spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link) that aren't included here. We'll have him go through those too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -342,15 +445,15 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
        "      <th>image_id</th>\n",
        "      <th>sequence_id</th>\n",
        "      <th>location_id</th>\n",
        "      <th>frame_num</th>\n",
        "      <th>original_label</th>\n",
        "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
        "      <th>...</th>\n",
        "      <th>suborder</th>\n",
        "      <th>infraorder</th>\n",
@@ -366,41 +469,41 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>14570597</th>\n",
-       "      <td>Snapshot Camdeboo</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Camdeboo : CDB_S1/B04/B04_R1/CDB_S1_B...</td>\n",
-       "      <td>Snapshot Camdeboo : CDB_S1#B04#1#126</td>\n",
-       "      <td>Snapshot Camdeboo : B04</td>\n",
-       "      <td>1</td>\n",
-       "      <td>monkeyvervet</td>\n",
-       "      <td>chlorocebus pygerythrus</td>\n",
-       "      <td>vervet monkey</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>12061234</th>\n",
+       "      <td>Snapshot Serengeti</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Snapshot Serengeti : S8/N06/N06_R2/S8_N06_R2_I...</td>\n",
+       "      <td>Snapshot Serengeti : SER_S8#N06#2#628</td>\n",
+       "      <td>Snapshot Serengeti : N06</td>\n",
+       "      <td>3</td>\n",
+       "      <td>hartebeest</td>\n",
+       "      <td>alcelaphus buselaphus</td>\n",
        "      <td>...</td>\n",
-       "      <td>haplorhini</td>\n",
-       "      <td>simiiformes</td>\n",
+       "      <td>ruminantia</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>cercopithecidae</td>\n",
-       "      <td>cercopithecinae</td>\n",
-       "      <td>cercopithecini</td>\n",
-       "      <td>chlorocebus</td>\n",
-       "      <td>chlorocebus pygerythrus</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>bovidae</td>\n",
+       "      <td>antilopinae</td>\n",
+       "      <td>alcelaphini</td>\n",
+       "      <td>alcelaphus</td>\n",
+       "      <td>alcelaphus buselaphus</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13334717</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S10/C05/C05_R1/S10_C05_R1...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S10#C05#1#719</td>\n",
-       "      <td>Snapshot Serengeti : C05</td>\n",
-       "      <td>3</td>\n",
+       "      <th>6586492</th>\n",
+       "      <td>Idaho Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Idaho Camera Traps : loc_0067_im_006951</td>\n",
+       "      <td>Idaho Camera Traps : loc_0067_seq_006947</td>\n",
+       "      <td>Idaho Camera Traps : 67</td>\n",
+       "      <td>0</td>\n",
        "      <td>empty</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>01-18-2015 09:23:50</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -414,41 +517,41 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11278580</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S7/T10/T10_R3/S7_T10_R3_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S7#T10#3#325</td>\n",
-       "      <td>Snapshot Serengeti : T10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>hartebeest</td>\n",
-       "      <td>alcelaphus buselaphus</td>\n",
-       "      <td>hartebeest</td>\n",
-       "      <td>11-17-2013 11:54:56</td>\n",
+       "      <th>2786883</th>\n",
+       "      <td>NACTI</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>NACTI : FL-28_09_01_2016_FL-28_0059198.JPG</td>\n",
+       "      <td>NACTI : unknown</td>\n",
+       "      <td>NACTI : archbold_FL-28</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>unidentified bird</td>\n",
+       "      <td>aves</td>\n",
        "      <td>...</td>\n",
-       "      <td>ruminantia</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>antilopinae</td>\n",
-       "      <td>alcelaphini</td>\n",
-       "      <td>alcelaphus</td>\n",
-       "      <td>alcelaphus buselaphus</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9459516</th>\n",
+       "      <th>13855386</th>\n",
        "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S5/G07/G07_R1/S5_G07_R1_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S5#G07#1#859</td>\n",
-       "      <td>Snapshot Serengeti : G07</td>\n",
-       "      <td>3</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Snapshot Serengeti : S10/O12/O12_R1/S10_O12_R1...</td>\n",
+       "      <td>Snapshot Serengeti : SER_S10#O12#1#104</td>\n",
+       "      <td>Snapshot Serengeti : O12</td>\n",
+       "      <td>1</td>\n",
        "      <td>empty</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>06-05-2012 13:24:53</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -462,41 +565,41 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2969662</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : FL-33_07_06_2016_FL-33_0018047.JPG</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Archbold, FL</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>domestic cow</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>9992570</th>\n",
+       "      <td>Snapshot Serengeti</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Snapshot Serengeti : S5/U12/U12_R1/S5_U12_R1_I...</td>\n",
+       "      <td>Snapshot Serengeti : SER_S5#U12#1#63</td>\n",
+       "      <td>Snapshot Serengeti : U12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>zebra</td>\n",
+       "      <td>equus quagga</td>\n",
        "      <td>...</td>\n",
-       "      <td>ruminantia</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>bovinae</td>\n",
-       "      <td>bovini</td>\n",
-       "      <td>bos</td>\n",
-       "      <td>bos taurus</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>equidae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>equus</td>\n",
+       "      <td>equus quagga</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1361914</th>\n",
+       "      <th>2900006</th>\n",
        "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : CA-45_08_03_2015_CA-45_0007619.jpg</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>NACTI : FL-31_01_21_2016_FL-31_0036063.jpg</td>\n",
        "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Lebec, California</td>\n",
+       "      <td>NACTI : archbold_FL-31</td>\n",
        "      <td>-1</td>\n",
        "      <td>bos taurus</td>\n",
        "      <td>bos taurus</td>\n",
-       "      <td>domestic cow</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>ruminantia</td>\n",
        "      <td>NaN</td>\n",
@@ -510,46 +613,22 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>560812</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : 2016_Unit074_Ivan093_img0754.jpg</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : San Juan Mntns, Colorado</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>cervus elaphus</td>\n",
-       "      <td>cervus elaphus</td>\n",
-       "      <td>red deer</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>ruminantia</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>cervidae</td>\n",
-       "      <td>cervinae</td>\n",
-       "      <td>cervini</td>\n",
-       "      <td>cervus</td>\n",
-       "      <td>cervus elaphus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6040643</th>\n",
-       "      <td>Idaho Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/idah...</td>\n",
-       "      <td>Idaho Camera Traps : loc_0026_im_007478</td>\n",
-       "      <td>Idaho Camera Traps : loc_0026_seq_006588</td>\n",
-       "      <td>Idaho Camera Traps : 26</td>\n",
+       "      <th>16472694</th>\n",
+       "      <td>SWG Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>SWG Camera Traps : d98b4691-8c29-11eb-85d7-000...</td>\n",
+       "      <td>SWG Camera Traps : fad4a034-8c29-11eb-8cda-000...</td>\n",
+       "      <td>SWG Camera Traps : loc_0890</td>\n",
        "      <td>0</td>\n",
-       "      <td>empty</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>02-18-2016 06:10:00</td>\n",
+       "      <td>unidentified_murid</td>\n",
+       "      <td>muridae</td>\n",
        "      <td>...</td>\n",
+       "      <td>myomorpha</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>muroidea</td>\n",
+       "      <td>muridae</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -558,171 +637,207 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1855600</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : FL-07_08_16_2016_FL-07_0242508.JPG</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Archbold, FL</td>\n",
+       "      <th>18408513</th>\n",
+       "      <td>Trail Camera Images of New Zealand Animals</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Trail Camera Images of New Zealand Animals : E...</td>\n",
+       "      <td>Trail Camera Images of New Zealand Animals : u...</td>\n",
+       "      <td>Trail Camera Images of New Zealand Animals : E...</td>\n",
        "      <td>-1</td>\n",
-       "      <td>equus africanus</td>\n",
-       "      <td>equus africanus</td>\n",
-       "      <td>african wild ass</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>robin</td>\n",
+       "      <td>petroica australis</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>equidae</td>\n",
+       "      <td>petroicidae</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>equus</td>\n",
-       "      <td>equus africanus</td>\n",
+       "      <td>petroica</td>\n",
+       "      <td>petroica australis</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9890853</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S5/Q12/Q12_R4/S5_Q12_R4_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S5#Q12#4#343</td>\n",
-       "      <td>Snapshot Serengeti : Q12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>empty</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>11-09-2012 18:46:47</td>\n",
+       "      <th>17944687</th>\n",
+       "      <td>Trail Camera Images of New Zealand Animals</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Trail Camera Images of New Zealand Animals : E...</td>\n",
+       "      <td>Trail Camera Images of New Zealand Animals : u...</td>\n",
+       "      <td>Trail Camera Images of New Zealand Animals : E...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>mouse</td>\n",
+       "      <td>mus</td>\n",
        "      <td>...</td>\n",
+       "      <td>myomorpha</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>muroidea</td>\n",
+       "      <td>muridae</td>\n",
+       "      <td>murinae</td>\n",
+       "      <td>murini</td>\n",
+       "      <td>mus</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674082</th>\n",
+       "      <td>NACTI</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>NACTI : CA-06_08_10_2016_CA-06_0015357.JPG</td>\n",
+       "      <td>NACTI : unknown</td>\n",
+       "      <td>NACTI : lebec_CA-06</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>bos taurus</td>\n",
+       "      <td>bos taurus</td>\n",
+       "      <td>...</td>\n",
+       "      <td>ruminantia</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>bovidae</td>\n",
+       "      <td>bovinae</td>\n",
+       "      <td>bovini</td>\n",
+       "      <td>bos</td>\n",
+       "      <td>bos taurus</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10 rows × 30 columns</p>\n",
+       "<p>10 rows × 32 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                dataset_name   \n",
-       "14570597   Snapshot Camdeboo  \\\n",
-       "13334717  Snapshot Serengeti   \n",
-       "11278580  Snapshot Serengeti   \n",
-       "9459516   Snapshot Serengeti   \n",
-       "2969662                NACTI   \n",
-       "1361914                NACTI   \n",
-       "560812                 NACTI   \n",
-       "6040643   Idaho Camera Traps   \n",
-       "1855600                NACTI   \n",
-       "9890853   Snapshot Serengeti   \n",
-       "\n",
-       "                                                        url   \n",
-       "14570597  https://lilablobssc.blob.core.windows.net/snap...  \\\n",
-       "13334717  https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "11278580  https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "9459516   https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "2969662   https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "1361914   https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "560812    https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "6040643   https://lilablobssc.blob.core.windows.net/idah...   \n",
-       "1855600   https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "9890853   https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "\n",
-       "                                                   image_id   \n",
-       "14570597  Snapshot Camdeboo : CDB_S1/B04/B04_R1/CDB_S1_B...  \\\n",
-       "13334717  Snapshot Serengeti : S10/C05/C05_R1/S10_C05_R1...   \n",
-       "11278580  Snapshot Serengeti : S7/T10/T10_R3/S7_T10_R3_I...   \n",
-       "9459516   Snapshot Serengeti : S5/G07/G07_R1/S5_G07_R1_I...   \n",
-       "2969662          NACTI : FL-33_07_06_2016_FL-33_0018047.JPG   \n",
-       "1361914          NACTI : CA-45_08_03_2015_CA-45_0007619.jpg   \n",
-       "560812             NACTI : 2016_Unit074_Ivan093_img0754.jpg   \n",
-       "6040643             Idaho Camera Traps : loc_0026_im_007478   \n",
-       "1855600          NACTI : FL-07_08_16_2016_FL-07_0242508.JPG   \n",
-       "9890853   Snapshot Serengeti : S5/Q12/Q12_R4/S5_Q12_R4_I...   \n",
-       "\n",
-       "                                       sequence_id   \n",
-       "14570597      Snapshot Camdeboo : CDB_S1#B04#1#126  \\\n",
-       "13334717    Snapshot Serengeti : SER_S10#C05#1#719   \n",
-       "11278580     Snapshot Serengeti : SER_S7#T10#3#325   \n",
-       "9459516      Snapshot Serengeti : SER_S5#G07#1#859   \n",
-       "2969662                            NACTI : unknown   \n",
-       "1361914                            NACTI : unknown   \n",
-       "560812                             NACTI : unknown   \n",
-       "6040643   Idaho Camera Traps : loc_0026_seq_006588   \n",
-       "1855600                            NACTI : unknown   \n",
-       "9890853      Snapshot Serengeti : SER_S5#Q12#4#343   \n",
-       "\n",
-       "                               location_id  frame_num   original_label   \n",
-       "14570597           Snapshot Camdeboo : B04          1     monkeyvervet  \\\n",
-       "13334717          Snapshot Serengeti : C05          3            empty   \n",
-       "11278580          Snapshot Serengeti : T10          2       hartebeest   \n",
-       "9459516           Snapshot Serengeti : G07          3            empty   \n",
-       "2969662               NACTI : Archbold, FL         -1       bos taurus   \n",
-       "1361914          NACTI : Lebec, California         -1       bos taurus   \n",
-       "560812    NACTI : San Juan Mntns, Colorado         -1   cervus elaphus   \n",
-       "6040643            Idaho Camera Traps : 26          0            empty   \n",
-       "1855600               NACTI : Archbold, FL         -1  equus africanus   \n",
-       "9890853           Snapshot Serengeti : Q12          1            empty   \n",
-       "\n",
-       "                  scientific_name       common_name             datetime  ...   \n",
-       "14570597  chlorocebus pygerythrus     vervet monkey                  NaN  ...  \\\n",
-       "13334717                      NaN               NaN  01-18-2015 09:23:50  ...   \n",
-       "11278580    alcelaphus buselaphus        hartebeest  11-17-2013 11:54:56  ...   \n",
-       "9459516                       NaN               NaN  06-05-2012 13:24:53  ...   \n",
-       "2969662                bos taurus      domestic cow                  NaN  ...   \n",
-       "1361914                bos taurus      domestic cow                  NaN  ...   \n",
-       "560812             cervus elaphus          red deer                  NaN  ...   \n",
-       "6040643                       NaN               NaN  02-18-2016 06:10:00  ...   \n",
-       "1855600           equus africanus  african wild ass                  NaN  ...   \n",
-       "9890853                       NaN               NaN  11-09-2012 18:46:47  ...   \n",
-       "\n",
-       "            suborder   infraorder superfamily           family   \n",
-       "14570597  haplorhini  simiiformes         NaN  cercopithecidae  \\\n",
-       "13334717         NaN          NaN         NaN              NaN   \n",
-       "11278580  ruminantia          NaN         NaN          bovidae   \n",
-       "9459516          NaN          NaN         NaN              NaN   \n",
-       "2969662   ruminantia          NaN         NaN          bovidae   \n",
-       "1361914   ruminantia          NaN         NaN          bovidae   \n",
-       "560812    ruminantia          NaN         NaN         cervidae   \n",
-       "6040643          NaN          NaN         NaN              NaN   \n",
-       "1855600          NaN          NaN         NaN          equidae   \n",
-       "9890853          NaN          NaN         NaN              NaN   \n",
-       "\n",
-       "                subfamily           tribe        genus   \n",
-       "14570597  cercopithecinae  cercopithecini  chlorocebus  \\\n",
-       "13334717              NaN             NaN          NaN   \n",
-       "11278580      antilopinae     alcelaphini   alcelaphus   \n",
-       "9459516               NaN             NaN          NaN   \n",
-       "2969662           bovinae          bovini          bos   \n",
-       "1361914           bovinae          bovini          bos   \n",
-       "560812           cervinae         cervini       cervus   \n",
-       "6040643               NaN             NaN          NaN   \n",
-       "1855600               NaN             NaN        equus   \n",
-       "9890853               NaN             NaN          NaN   \n",
-       "\n",
-       "                          species subspecies variety  \n",
-       "14570597  chlorocebus pygerythrus        NaN     NaN  \n",
-       "13334717                      NaN        NaN     NaN  \n",
-       "11278580    alcelaphus buselaphus        NaN     NaN  \n",
-       "9459516                       NaN        NaN     NaN  \n",
-       "2969662                bos taurus        NaN     NaN  \n",
-       "1361914                bos taurus        NaN     NaN  \n",
-       "560812             cervus elaphus        NaN     NaN  \n",
-       "6040643                       NaN        NaN     NaN  \n",
-       "1855600           equus africanus        NaN     NaN  \n",
-       "9890853                       NaN        NaN     NaN  \n",
-       "\n",
-       "[10 rows x 30 columns]"
+       "                                        dataset_name  \\\n",
+       "12061234                          Snapshot Serengeti   \n",
+       "6586492                           Idaho Camera Traps   \n",
+       "2786883                                        NACTI   \n",
+       "13855386                          Snapshot Serengeti   \n",
+       "9992570                           Snapshot Serengeti   \n",
+       "2900006                                        NACTI   \n",
+       "16472694                            SWG Camera Traps   \n",
+       "18408513  Trail Camera Images of New Zealand Animals   \n",
+       "17944687  Trail Camera Images of New Zealand Animals   \n",
+       "674082                                         NACTI   \n",
+       "\n",
+       "                                                    url_gcp  \\\n",
+       "12061234  https://storage.googleapis.com/public-datasets...   \n",
+       "6586492   https://storage.googleapis.com/public-datasets...   \n",
+       "2786883   https://storage.googleapis.com/public-datasets...   \n",
+       "13855386  https://storage.googleapis.com/public-datasets...   \n",
+       "9992570   https://storage.googleapis.com/public-datasets...   \n",
+       "2900006   https://storage.googleapis.com/public-datasets...   \n",
+       "16472694  https://storage.googleapis.com/public-datasets...   \n",
+       "18408513  https://storage.googleapis.com/public-datasets...   \n",
+       "17944687  https://storage.googleapis.com/public-datasets...   \n",
+       "674082    https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                                    url_aws  \\\n",
+       "12061234  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "6586492   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "2786883   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "13855386  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "9992570   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "2900006   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "16472694  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "18408513  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "17944687  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "674082    http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "\n",
+       "                                                  url_azure  \\\n",
+       "12061234  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "6586492   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "2786883   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "13855386  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "9992570   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "2900006   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "16472694  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "18408513  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "17944687  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "674082    https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "\n",
+       "                                                   image_id  \\\n",
+       "12061234  Snapshot Serengeti : S8/N06/N06_R2/S8_N06_R2_I...   \n",
+       "6586492             Idaho Camera Traps : loc_0067_im_006951   \n",
+       "2786883          NACTI : FL-28_09_01_2016_FL-28_0059198.JPG   \n",
+       "13855386  Snapshot Serengeti : S10/O12/O12_R1/S10_O12_R1...   \n",
+       "9992570   Snapshot Serengeti : S5/U12/U12_R1/S5_U12_R1_I...   \n",
+       "2900006          NACTI : FL-31_01_21_2016_FL-31_0036063.jpg   \n",
+       "16472694  SWG Camera Traps : d98b4691-8c29-11eb-85d7-000...   \n",
+       "18408513  Trail Camera Images of New Zealand Animals : E...   \n",
+       "17944687  Trail Camera Images of New Zealand Animals : E...   \n",
+       "674082           NACTI : CA-06_08_10_2016_CA-06_0015357.JPG   \n",
+       "\n",
+       "                                                sequence_id  \\\n",
+       "12061234              Snapshot Serengeti : SER_S8#N06#2#628   \n",
+       "6586492            Idaho Camera Traps : loc_0067_seq_006947   \n",
+       "2786883                                     NACTI : unknown   \n",
+       "13855386             Snapshot Serengeti : SER_S10#O12#1#104   \n",
+       "9992570                Snapshot Serengeti : SER_S5#U12#1#63   \n",
+       "2900006                                     NACTI : unknown   \n",
+       "16472694  SWG Camera Traps : fad4a034-8c29-11eb-8cda-000...   \n",
+       "18408513  Trail Camera Images of New Zealand Animals : u...   \n",
+       "17944687  Trail Camera Images of New Zealand Animals : u...   \n",
+       "674082                                      NACTI : unknown   \n",
+       "\n",
+       "                                                location_id  frame_num  \\\n",
+       "12061234                           Snapshot Serengeti : N06          3   \n",
+       "6586492                             Idaho Camera Traps : 67          0   \n",
+       "2786883                              NACTI : archbold_FL-28         -1   \n",
+       "13855386                           Snapshot Serengeti : O12          1   \n",
+       "9992570                            Snapshot Serengeti : U12          1   \n",
+       "2900006                              NACTI : archbold_FL-31         -1   \n",
+       "16472694                        SWG Camera Traps : loc_0890          0   \n",
+       "18408513  Trail Camera Images of New Zealand Animals : E...         -1   \n",
+       "17944687  Trail Camera Images of New Zealand Animals : E...         -1   \n",
+       "674082                                  NACTI : lebec_CA-06         -1   \n",
+       "\n",
+       "              original_label        scientific_name  ...    suborder  \\\n",
+       "12061234          hartebeest  alcelaphus buselaphus  ...  ruminantia   \n",
+       "6586492                empty                    NaN  ...         NaN   \n",
+       "2786883    unidentified bird                   aves  ...         NaN   \n",
+       "13855386               empty                    NaN  ...         NaN   \n",
+       "9992570                zebra           equus quagga  ...         NaN   \n",
+       "2900006           bos taurus             bos taurus  ...  ruminantia   \n",
+       "16472694  unidentified_murid                muridae  ...   myomorpha   \n",
+       "18408513               robin     petroica australis  ...         NaN   \n",
+       "17944687               mouse                    mus  ...   myomorpha   \n",
+       "674082            bos taurus             bos taurus  ...  ruminantia   \n",
+       "\n",
+       "         infraorder superfamily       family    subfamily        tribe  \\\n",
+       "12061234        NaN         NaN      bovidae  antilopinae  alcelaphini   \n",
+       "6586492         NaN         NaN          NaN          NaN          NaN   \n",
+       "2786883         NaN         NaN          NaN          NaN          NaN   \n",
+       "13855386        NaN         NaN          NaN          NaN          NaN   \n",
+       "9992570         NaN         NaN      equidae          NaN          NaN   \n",
+       "2900006         NaN         NaN      bovidae      bovinae       bovini   \n",
+       "16472694        NaN    muroidea      muridae          NaN          NaN   \n",
+       "18408513        NaN         NaN  petroicidae          NaN          NaN   \n",
+       "17944687        NaN    muroidea      muridae      murinae       murini   \n",
+       "674082          NaN         NaN      bovidae      bovinae       bovini   \n",
+       "\n",
+       "               genus                species subspecies variety  \n",
+       "12061234  alcelaphus  alcelaphus buselaphus        NaN     NaN  \n",
+       "6586492          NaN                    NaN        NaN     NaN  \n",
+       "2786883          NaN                    NaN        NaN     NaN  \n",
+       "13855386         NaN                    NaN        NaN     NaN  \n",
+       "9992570        equus           equus quagga        NaN     NaN  \n",
+       "2900006          bos             bos taurus        NaN     NaN  \n",
+       "16472694         NaN                    NaN        NaN     NaN  \n",
+       "18408513    petroica     petroica australis        NaN     NaN  \n",
+       "17944687         mus                    NaN        NaN     NaN  \n",
+       "674082           bos             bos taurus        NaN     NaN  \n",
+       "\n",
+       "[10 rows x 32 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -731,9 +846,16 @@
     "df.sample(10)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe that we also now get multiple URL options; `url_aws` will likely be best/fastest for use with [`distributed-downloader`](https://github.com/Imageomics/distributed-downloader) to get the images."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -741,42 +863,44 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 16833848 entries, 0 to 16833847\n",
-      "Data columns (total 30 columns):\n",
+      "RangeIndex: 19351156 entries, 0 to 19351155\n",
+      "Data columns (total 32 columns):\n",
       " #   Column            Non-Null Count     Dtype \n",
       "---  ------            --------------     ----- \n",
-      " 0   dataset_name      16833848 non-null  object\n",
-      " 1   url               16833848 non-null  object\n",
-      " 2   image_id          16833848 non-null  object\n",
-      " 3   sequence_id       16833848 non-null  object\n",
-      " 4   location_id       16833848 non-null  object\n",
-      " 5   frame_num         16833848 non-null  int64 \n",
-      " 6   original_label    16833848 non-null  object\n",
-      " 7   scientific_name   7675398 non-null   object\n",
-      " 8   common_name       7675398 non-null   object\n",
-      " 9   datetime          13000606 non-null  object\n",
-      " 10  annotation_level  16833848 non-null  object\n",
-      " 11  kingdom           7675398 non-null   object\n",
-      " 12  phylum            7659814 non-null   object\n",
-      " 13  subphylum         7657475 non-null   object\n",
-      " 14  superclass        79 non-null        object\n",
-      " 15  class             7657475 non-null   object\n",
-      " 16  subclass          7040121 non-null   object\n",
-      " 17  infraclass        7039950 non-null   object\n",
-      " 18  superorder        6997230 non-null   object\n",
-      " 19  order             7279843 non-null   object\n",
-      " 20  suborder          5781039 non-null   object\n",
-      " 21  infraorder        508734 non-null    object\n",
-      " 22  superfamily       446568 non-null    object\n",
-      " 23  family            7177009 non-null   object\n",
-      " 24  subfamily         5790398 non-null   object\n",
-      " 25  tribe             5157365 non-null   object\n",
-      " 26  genus             6978606 non-null   object\n",
-      " 27  species           6516375 non-null   object\n",
-      " 28  subspecies        21298 non-null     object\n",
-      " 29  variety           1480 non-null      object\n",
-      "dtypes: int64(1), object(29)\n",
-      "memory usage: 3.8+ GB\n"
+      " 0   dataset_name      19351156 non-null  object\n",
+      " 1   url_gcp           19351156 non-null  object\n",
+      " 2   url_aws           19351156 non-null  object\n",
+      " 3   url_azure         19351156 non-null  object\n",
+      " 4   image_id          19351156 non-null  object\n",
+      " 5   sequence_id       19351156 non-null  object\n",
+      " 6   location_id       19351156 non-null  object\n",
+      " 7   frame_num         19351156 non-null  int64 \n",
+      " 8   original_label    19351156 non-null  object\n",
+      " 9   scientific_name   10192703 non-null  object\n",
+      " 10  common_name       10192703 non-null  object\n",
+      " 11  datetime          15404050 non-null  object\n",
+      " 12  annotation_level  19351156 non-null  object\n",
+      " 13  kingdom           10192703 non-null  object\n",
+      " 14  phylum            10177119 non-null  object\n",
+      " 15  subphylum         10141160 non-null  object\n",
+      " 16  superclass        79 non-null        object\n",
+      " 17  class             10174780 non-null  object\n",
+      " 18  subclass          9022382 non-null   object\n",
+      " 19  infraclass        9021471 non-null   object\n",
+      " 20  superorder        8812501 non-null   object\n",
+      " 21  order             9796763 non-null   object\n",
+      " 22  suborder          7289300 non-null   object\n",
+      " 23  infraorder        510351 non-null    object\n",
+      " 24  superfamily       1805446 non-null   object\n",
+      " 25  family            9693066 non-null   object\n",
+      " 26  subfamily         7662399 non-null   object\n",
+      " 27  tribe             6614011 non-null   object\n",
+      " 28  genus             9445408 non-null   object\n",
+      " 29  species           7521712 non-null   object\n",
+      " 30  subspecies        74052 non-null     object\n",
+      " 31  variety           2050 non-null      object\n",
+      "dtypes: int64(1), object(31)\n",
+      "memory usage: 4.6+ GB\n"
      ]
     }
    ],
@@ -784,24 +908,37 @@
     "df.info(show_counts = True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The overall dataset has grown by about 3 million images, we'll see how much of this is non-empty. I'm encouraged by the number of non-null `scientific_name` values seeming to also grow by about 3 million; most of these also seem to have genus now.\n",
+    "\n",
+    "We'll definitely want to check on the scientifc name choices where genus and species aren't available, similarly for other ranks, as it is guarunteed as much as kingdom (which is hopefully aligned with all non-empty images).\n",
+    "\n",
+    "No licensing info, we'll get that from HF or the datasets themselves (Andrey can check this; most seem to be [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/))."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dataset_name              18\n",
-       "url                 16731838\n",
-       "image_id            16731838\n",
-       "sequence_id          4772756\n",
-       "location_id             6129\n",
+       "dataset_name              20\n",
+       "url_gcp             19249146\n",
+       "url_aws             19249146\n",
+       "url_azure           19249146\n",
+       "image_id            19249146\n",
+       "sequence_id          4772758\n",
+       "location_id             9931\n",
        "frame_num              11005\n",
-       "original_label          1121\n",
-       "scientific_name          821\n",
-       "common_name              902\n",
-       "datetime             6572613\n",
+       "original_label          1227\n",
+       "scientific_name          908\n",
+       "common_name              999\n",
+       "datetime             8559012\n",
        "annotation_level           3\n",
        "kingdom                    1\n",
        "phylum                     2\n",
@@ -811,21 +948,21 @@
        "subclass                   3\n",
        "infraclass                 2\n",
        "superorder                 5\n",
-       "order                     53\n",
+       "order                     58\n",
        "suborder                  17\n",
        "infraorder                 9\n",
        "superfamily               12\n",
-       "family                   159\n",
-       "subfamily                 69\n",
+       "family                   187\n",
+       "subfamily                 71\n",
        "tribe                     46\n",
-       "genus                    476\n",
-       "species                  667\n",
-       "subspecies                 8\n",
+       "genus                    538\n",
+       "species                  739\n",
+       "subspecies                12\n",
        "variety                    1\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -838,7 +975,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have 667 unique species indicated (matches lila-taxonomy-mapping_release.csv after dropping humans, seems to have two more distinct genera though)."
+    "We have 739 unique species indicated, though the 908 unique `scientific_name` values is likely more indicative of the diversity.\n",
+    "\n",
+    "Interesting also to note that there are duplicate URLs here; these would be the indicators of multiple species in an image as they correspond to the number of unique image IDs. We'll check this out once we remove the images labeled as \"empty\"."
    ]
   },
   {
@@ -868,15 +1007,15 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
        "      <th>image_id</th>\n",
        "      <th>sequence_id</th>\n",
        "      <th>location_id</th>\n",
        "      <th>frame_num</th>\n",
        "      <th>original_label</th>\n",
        "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
        "      <th>...</th>\n",
        "      <th>suborder</th>\n",
        "      <th>infraorder</th>\n",
@@ -893,15 +1032,15 @@
        "  <tbody>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>0 rows × 30 columns</p>\n",
+       "<p>0 rows × 32 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "Empty DataFrame\n",
-       "Columns: [dataset_name, url, image_id, sequence_id, location_id, frame_num, original_label, scientific_name, common_name, datetime, annotation_level, kingdom, phylum, subphylum, superclass, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, subfamily, tribe, genus, species, subspecies, variety]\n",
+       "Columns: [dataset_name, url_gcp, url_aws, url_azure, image_id, sequence_id, location_id, frame_num, original_label, scientific_name, common_name, datetime, annotation_level, kingdom, phylum, subphylum, superclass, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, subfamily, tribe, genus, species, subspecies, variety]\n",
        "Index: []\n",
        "\n",
-       "[0 rows x 30 columns]"
+       "[0 rows x 32 columns]"
       ]
      },
      "execution_count": 10,
@@ -923,108 +1062,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_cleaned = df.loc[df.original_label != \"empty\"]"
+    "df_cleaned = df.loc[df.original_label != \"empty\"].copy()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We started with 16,833,848 entries, and are left with 8,448,597 after removing all labeled as `empty`, so about half the images. \n",
-    "\n",
-    "Note that there are still about 2 million that don't have the species label, 1.5 million that are missing genus designation."
+    "## Save the Reduced Data (no more \"empty\" labels)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 8448597 entries, 1 to 16833847\n",
-      "Data columns (total 2 columns):\n",
-      " #   Column   Non-Null Count    Dtype \n",
-      "---  ------   --------------    ----- \n",
-      " 0   genus    6978606 non-null  object\n",
-      " 1   species  6516375 non-null  object\n",
-      "dtypes: object(2)\n",
-      "memory usage: 193.4+ MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df_cleaned[['genus', 'species']].info(show_counts = True)"
+    "df_cleaned.to_csv(\"../data/lila_image_urls_and_labels.csv\", index = False)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 14,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "genus      476\n",
-       "species    667\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df_cleaned[['genus', 'species']].nunique()"
+    "Let's check where we are with annotations now that we've removed all the images labeled as empty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Series([], Name: count, dtype: int64)"
+       "dataset_name                                annotation_level\n",
+       "Caltech Camera Traps                        image                243177\n",
+       "Channel Islands Camera Traps                image                245644\n",
+       "Desert Lion Conservation Camera Traps       image                 63468\n",
+       "ENA24                                       image                 10284\n",
+       "Idaho Camera Traps                          sequence            1551552\n",
+       "Island Conservation Camera Traps            image                128207\n",
+       "Missouri Camera Traps                       sequence              23397\n",
+       "                                            image                  1276\n",
+       "NACTI                                       unknown             3382215\n",
+       "Orinoquia Camera Traps                      image                112267\n",
+       "SWG Camera Traps                            sequence            2039657\n",
+       "Snapshot Camdeboo                           sequence              30717\n",
+       "Snapshot Enonkishu                          sequence              30542\n",
+       "Snapshot Karoo                              sequence              38320\n",
+       "Snapshot Kgalagadi                          sequence              10402\n",
+       "Snapshot Kruger                             sequence              10637\n",
+       "Snapshot Mountain Zebra                     sequence              73606\n",
+       "Snapshot Serengeti                          sequence            7261545\n",
+       "Trail Camera Images of New Zealand Animals  image               2453840\n",
+       "WCS Camera Traps                            sequence            1369953\n",
+       "Wellington Camera Traps                     sequence             270450\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_cleaned.loc[df_cleaned['genus'].isna(), 'species'].value_counts()"
+    "df.groupby([\"dataset_name\"]).annotation_level.value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All entries missing `genus` are also missing `species`, so we'll drop all entries with null `genus`."
+    "We started with 19,351,156 entries, and are left with 10,965,902 after removing all labeled as `empty`, so more than half the images now; it's an increase of about 2.5M from the last version. \n",
+    "\n",
+    "Note that there are still about 3.4 million that don't have the species label, 1.5 million that are missing genus designation. 10,192,703 of them have scientific and common name, though! That's nearly all of them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_genusSpecies = df_cleaned.dropna(subset = \"genus\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1032,213 +1153,227 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 6978606 entries, 1 to 16833847\n",
-      "Data columns (total 30 columns):\n",
-      " #   Column            Non-Null Count    Dtype \n",
-      "---  ------            --------------    ----- \n",
-      " 0   dataset_name      6978606 non-null  object\n",
-      " 1   url               6978606 non-null  object\n",
-      " 2   image_id          6978606 non-null  object\n",
-      " 3   sequence_id       6978606 non-null  object\n",
-      " 4   location_id       6978606 non-null  object\n",
-      " 5   frame_num         6978606 non-null  int64 \n",
-      " 6   original_label    6978606 non-null  object\n",
-      " 7   scientific_name   6978606 non-null  object\n",
-      " 8   common_name       6978606 non-null  object\n",
-      " 9   datetime          4022446 non-null  object\n",
-      " 10  annotation_level  6978606 non-null  object\n",
-      " 11  kingdom           6978606 non-null  object\n",
-      " 12  phylum            6978606 non-null  object\n",
-      " 13  subphylum         6978606 non-null  object\n",
-      " 14  superclass        59 non-null       object\n",
-      " 15  class             6978606 non-null  object\n",
-      " 16  subclass          6792311 non-null  object\n",
-      " 17  infraclass        6792252 non-null  object\n",
-      " 18  superorder        6755335 non-null  object\n",
-      " 19  order             6978606 non-null  object\n",
-      " 20  suborder          5679493 non-null  object\n",
-      " 21  infraorder        508660 non-null   object\n",
-      " 22  superfamily       346788 non-null   object\n",
-      " 23  family            6978606 non-null  object\n",
-      " 24  subfamily         5789159 non-null  object\n",
-      " 25  tribe             5157009 non-null  object\n",
-      " 26  genus             6978606 non-null  object\n",
-      " 27  species           6516375 non-null  object\n",
-      " 28  subspecies        21298 non-null    object\n",
-      " 29  variety           1480 non-null     object\n",
-      "dtypes: int64(1), object(29)\n",
-      "memory usage: 1.6+ GB\n"
+      "Index: 10965902 entries, 1 to 19351155\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column            Non-Null Count     Dtype \n",
+      "---  ------            --------------     ----- \n",
+      " 0   dataset_name      10965902 non-null  object\n",
+      " 1   url_gcp           10965902 non-null  object\n",
+      " 2   url_aws           10965902 non-null  object\n",
+      " 3   url_azure         10965902 non-null  object\n",
+      " 4   image_id          10965902 non-null  object\n",
+      " 5   sequence_id       10965902 non-null  object\n",
+      " 6   location_id       10965902 non-null  object\n",
+      " 7   frame_num         10965902 non-null  int64 \n",
+      " 8   original_label    10965902 non-null  object\n",
+      " 9   scientific_name   10192703 non-null  object\n",
+      " 10  common_name       10192703 non-null  object\n",
+      " 11  datetime          7620269 non-null   object\n",
+      " 12  annotation_level  10965902 non-null  object\n",
+      " 13  kingdom           10192703 non-null  object\n",
+      " 14  phylum            10177119 non-null  object\n",
+      " 15  subphylum         10141160 non-null  object\n",
+      " 16  superclass        79 non-null        object\n",
+      " 17  class             10174780 non-null  object\n",
+      " 18  subclass          9022382 non-null   object\n",
+      " 19  infraclass        9021471 non-null   object\n",
+      " 20  superorder        8812501 non-null   object\n",
+      " 21  order             9796763 non-null   object\n",
+      " 22  suborder          7289300 non-null   object\n",
+      " 23  infraorder        510351 non-null    object\n",
+      " 24  superfamily       1805446 non-null   object\n",
+      " 25  family            9693066 non-null   object\n",
+      " 26  subfamily         7662399 non-null   object\n",
+      " 27  tribe             6614011 non-null   object\n",
+      " 28  genus             9445408 non-null   object\n",
+      " 29  species           7521712 non-null   object\n",
+      " 30  subspecies        74052 non-null     object\n",
+      " 31  variety           2050 non-null      object\n",
+      "dtypes: int64(1), object(31)\n",
+      "memory usage: 2.7+ GB\n"
      ]
     }
    ],
    "source": [
-    "df_genusSpecies.info(show_counts = True)"
+    "df_cleaned.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dataset_name             18\n",
-       "url                 6901819\n",
-       "image_id            6901819\n",
-       "sequence_id         1046985\n",
-       "location_id            6017\n",
-       "frame_num              6068\n",
-       "original_label          972\n",
-       "scientific_name         754\n",
-       "common_name             815\n",
-       "datetime            2576256\n",
-       "annotation_level          3\n",
-       "kingdom                   1\n",
-       "phylum                    2\n",
-       "subphylum                 2\n",
-       "superclass                1\n",
-       "class                     5\n",
-       "subclass                  2\n",
-       "infraclass                2\n",
-       "superorder                5\n",
-       "order                    49\n",
-       "suborder                 14\n",
-       "infraorder                7\n",
-       "superfamily               9\n",
-       "family                  152\n",
-       "subfamily                69\n",
-       "tribe                    46\n",
-       "genus                   476\n",
-       "species                 667\n",
-       "subspecies                8\n",
-       "variety                   1\n",
+       "dataset_name              20\n",
+       "url_gcp             10864013\n",
+       "url_aws             10864013\n",
+       "url_azure           10864013\n",
+       "image_id            10864013\n",
+       "sequence_id          1412018\n",
+       "location_id             9848\n",
+       "frame_num              11005\n",
+       "original_label          1226\n",
+       "scientific_name          908\n",
+       "common_name              999\n",
+       "datetime             5479009\n",
+       "annotation_level           3\n",
+       "kingdom                    1\n",
+       "phylum                     2\n",
+       "subphylum                  5\n",
+       "superclass                 1\n",
+       "class                      8\n",
+       "subclass                   3\n",
+       "infraclass                 2\n",
+       "superorder                 5\n",
+       "order                     58\n",
+       "suborder                  17\n",
+       "infraorder                 9\n",
+       "superfamily               12\n",
+       "family                   187\n",
+       "subfamily                 71\n",
+       "tribe                     46\n",
+       "genus                    538\n",
+       "species                  739\n",
+       "subspecies                12\n",
+       "variety                    1\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_genusSpecies.nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This leaves us with 476 unique genera and 667 unique species among the remaining 6,978,606 entries in the 18 datasets.\n",
-    "\n",
-    "There are about 77,000 non-unique URLs. Does this mean there are duplicated entries for images or are they sequences? This is the same number of unique `image_id`, so unclear."
+    "df_cleaned.nunique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "False    6825717\n",
-       "True      152889\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "phylum\n",
+      "chordata      10166819\n",
+      "arthropoda       10300\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "class\n",
+      "mammalia        9089968\n",
+      "aves            1068301\n",
+      "reptilia           8416\n",
+      "insecta            7856\n",
+      "amphibia            134\n",
+      "malacostraca         79\n",
+      "arachnida            20\n",
+      "diplopoda             6\n",
+      "Name: count, dtype: int64\n"
+     ]
     }
    ],
    "source": [
-    "df_genusSpecies.duplicated(subset = ['url'], keep = False).value_counts()"
+    "print(df_cleaned.phylum.value_counts())\n",
+    "print()\n",
+    "print(df_cleaned[\"class\"].value_counts())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tracks with the rough estimate, as we see a total of 152,889 entries that are duplicates of each other, with a total of 76,787 that have been duplicated (see below)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False    6901819\n",
-       "True       76787\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_genusSpecies.duplicated(subset = ['url'], keep = 'first').value_counts()"
+    "We have 10,965,902 total - 10,864,013 unique URLs, suggesting at most 101,889 images have more than one species in them. That's only 1% of our images here and even smaller at the scale we're looking for the next ToL dataset. It is interesting to note though and we should explore this more.\n",
+    "\n",
+    "I'm curious about the single \"variety\", since I thought that was more of a plant label and these are all animals.\n",
+    "\n",
+    "All images are in Animalia, as expected; we have 2 phyla represented and 8 classes:\n",
+    " - Predominantly Chordata, and within that phylum, Mammalia is the vast majority, though aves is about 10%.\n",
+    " - Note that not every image with a phylum label has a class label.\n",
+    " - Insecta, malacostraca, arachnida, and diplopoda are all in the class Arthropoda.\n",
+    "\n",
+    "### Label Multi-Species Images\n",
+    "We'll go by both the URL and image ID, which do seem to correspond to the same images (for uniqueness)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "False    6901819\n",
-       "True       76787\n",
-       "Name: count, dtype: int64"
+       "dataset_name           13\n",
+       "url_gcp             99682\n",
+       "url_aws             99682\n",
+       "url_azure           99682\n",
+       "image_id            99682\n",
+       "sequence_id         30668\n",
+       "location_id           514\n",
+       "frame_num             488\n",
+       "original_label        167\n",
+       "scientific_name       133\n",
+       "common_name           143\n",
+       "datetime            42962\n",
+       "annotation_level        2\n",
+       "kingdom                 1\n",
+       "phylum                  2\n",
+       "subphylum               4\n",
+       "superclass              1\n",
+       "class                   6\n",
+       "subclass                3\n",
+       "infraclass              2\n",
+       "superorder              4\n",
+       "order                  27\n",
+       "suborder                9\n",
+       "infraorder              5\n",
+       "superfamily             5\n",
+       "family                 54\n",
+       "subfamily              23\n",
+       "tribe                  21\n",
+       "genus                  90\n",
+       "species                92\n",
+       "subspecies              6\n",
+       "variety                 1\n",
+       "multi_species           1\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_genusSpecies.duplicated(subset = ['url', 'image_id'], keep = 'first').value_counts()"
+    "df_cleaned[\"multi_species\"] = df_cleaned.duplicated(subset = [\"url_aws\", \"image_id\"], keep = False)\n",
+    "\n",
+    "df_cleaned.loc[df_cleaned[\"multi_species\"]].nunique()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These match the duplicated `image_id`s as well. Perhaps this is more than one animal in the frame? In which case it is important to mark all the potential duplicates as duplicates, not just the instances after the image's first occurence."
+    "We've got just under 100K images that have multiple species. We can figure out how many each of them have, and then move on to looking at images per sequence and other labeling info."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/1632903565.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_genusSpecies['url_dupe'] = df_genusSpecies.duplicated(subset = ['url'], keep = False)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df_genusSpecies['url_dupe'] = df_genusSpecies.duplicated(subset = ['url'], keep = False)"
+    "multi_sp_imgs = list(df_cleaned.loc[df_cleaned[\"multi_species\"], \"image_id\"].unique())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1263,17 +1398,16 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
        "      <th>image_id</th>\n",
        "      <th>sequence_id</th>\n",
        "      <th>location_id</th>\n",
        "      <th>frame_num</th>\n",
        "      <th>original_label</th>\n",
        "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
        "      <th>...</th>\n",
-       "      <th>infraorder</th>\n",
        "      <th>superfamily</th>\n",
        "      <th>family</th>\n",
        "      <th>subfamily</th>\n",
@@ -1282,383 +1416,231 @@
        "      <th>species</th>\n",
        "      <th>subspecies</th>\n",
        "      <th>variety</th>\n",
-       "      <th>url_dupe</th>\n",
+       "      <th>multi_species</th>\n",
+       "      <th>num_species</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>15355844</th>\n",
-       "      <td>SWG Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/swg-...</td>\n",
-       "      <td>SWG Camera Traps : cebffb73-8c29-11eb-92ab-000...</td>\n",
-       "      <td>SWG Camera Traps : e42a22f0-8c29-11eb-9c41-000...</td>\n",
-       "      <td>SWG Camera Traps : loc_0263</td>\n",
-       "      <td>2</td>\n",
-       "      <td>large_antlered_muntjac</td>\n",
-       "      <td>muntiacus vuquangensis</td>\n",
-       "      <td>large-antlered muntjac</td>\n",
-       "      <td>06-24-2019 19:36:35</td>\n",
+       "      <th>1</th>\n",
+       "      <td>Caltech Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...</td>\n",
+       "      <td>Caltech Camera Traps : 26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>deer</td>\n",
+       "      <td>odocoileus</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>cervidae</td>\n",
-       "      <td>cervinae</td>\n",
-       "      <td>muntiacini</td>\n",
-       "      <td>muntiacus</td>\n",
-       "      <td>muntiacus vuquangensis</td>\n",
+       "      <td>capreolinae</td>\n",
+       "      <td>odocoileini</td>\n",
+       "      <td>odocoileus</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15098255</th>\n",
-       "      <td>SWG Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/swg-...</td>\n",
-       "      <td>SWG Camera Traps : cc2b9e0b-8c29-11eb-bd63-000...</td>\n",
-       "      <td>SWG Camera Traps : 2801caa6-8c2a-11eb-9118-000...</td>\n",
-       "      <td>SWG Camera Traps : loc_0512</td>\n",
+       "      <th>2</th>\n",
+       "      <td>Caltech Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...</td>\n",
+       "      <td>Caltech Camera Traps : 38</td>\n",
        "      <td>2</td>\n",
-       "      <td>ferret_badger</td>\n",
-       "      <td>melogale</td>\n",
-       "      <td>ferret badger</td>\n",
-       "      <td>07-08-2020 21:03:55</td>\n",
+       "      <td>cat</td>\n",
+       "      <td>felis catus</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>felidae</td>\n",
+       "      <td>felinae</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>mustelidae</td>\n",
-       "      <td>helictidinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>melogale</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>felis</td>\n",
+       "      <td>felis catus</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15750309</th>\n",
-       "      <td>SWG Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/swg-...</td>\n",
-       "      <td>SWG Camera Traps : d29fd7c0-8c29-11eb-9d65-000...</td>\n",
-       "      <td>SWG Camera Traps : 39b3e643-8c2a-11eb-b05f-000...</td>\n",
-       "      <td>SWG Camera Traps : loc_0588</td>\n",
-       "      <td>7</td>\n",
-       "      <td>stump_tailed_macaque</td>\n",
-       "      <td>macaca arctoides</td>\n",
-       "      <td>stump-tailed macaque</td>\n",
-       "      <td>08-26-2019 10:52:49</td>\n",
-       "      <td>...</td>\n",
-       "      <td>simiiformes</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>cercopithecidae</td>\n",
-       "      <td>cercopithecinae</td>\n",
-       "      <td>papionini</td>\n",
-       "      <td>macaca</td>\n",
-       "      <td>macaca arctoides</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>812622</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : CA-17_07_01_2016_CA-17_0038090.JPG</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Lebec, California</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>domestic cow</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>3</th>\n",
+       "      <td>Caltech Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...</td>\n",
+       "      <td>Caltech Camera Traps : 38</td>\n",
+       "      <td>2</td>\n",
+       "      <td>opossum</td>\n",
+       "      <td>didelphis virginiana</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>bovinae</td>\n",
-       "      <td>bovini</td>\n",
-       "      <td>bos</td>\n",
-       "      <td>bos taurus</td>\n",
+       "      <td>didelphidae</td>\n",
+       "      <td>didelphinae</td>\n",
+       "      <td>didelphini</td>\n",
+       "      <td>didelphis</td>\n",
+       "      <td>didelphis virginiana</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2050788</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : FL-12_08_04_2016_FL-12_0071339.JPG</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Archbold, FL</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>domestic cow</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>bovinae</td>\n",
-       "      <td>bovini</td>\n",
-       "      <td>bos</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12610859</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S9/F07/F07_R2/S9_F07_R2_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S9#F07#2#50</td>\n",
-       "      <td>Snapshot Serengeti : F07</td>\n",
+       "      <th>5</th>\n",
+       "      <td>Caltech Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 5a096955-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 70096335-5567-11e8-a99a...</td>\n",
+       "      <td>Caltech Camera Traps : 36</td>\n",
        "      <td>1</td>\n",
-       "      <td>elephant</td>\n",
-       "      <td>loxodonta africana</td>\n",
-       "      <td>african bush elephant</td>\n",
-       "      <td>10-16-2014 09:44:07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>elephantidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>loxodonta</td>\n",
-       "      <td>loxodonta africana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2463379</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : FL-21_07_29_2015_FL-21_0010466.jpg</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Archbold, FL</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>domestic cow</td>\n",
+       "      <td>car</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>bovinae</td>\n",
-       "      <td>bovini</td>\n",
-       "      <td>bos</td>\n",
-       "      <td>bos taurus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1296206</th>\n",
-       "      <td>NACTI</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/nact...</td>\n",
-       "      <td>NACTI : CA-42_10_06_2016_CA-42_0022200.JPG</td>\n",
-       "      <td>NACTI : unknown</td>\n",
-       "      <td>NACTI : Lebec, California</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>lynx rufus</td>\n",
-       "      <td>lynx rufus</td>\n",
-       "      <td>bobcat</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>felidae</td>\n",
-       "      <td>felinae</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>lynx</td>\n",
-       "      <td>lynx rufus</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4026992</th>\n",
-       "      <td>WCS Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/wcs-...</td>\n",
-       "      <td>WCS Camera Traps : 098d7290-92d5-11e9-bb41-000...</td>\n",
-       "      <td>WCS Camera Traps : bol-017-d0050-90</td>\n",
-       "      <td>WCS Camera Traps : 650</td>\n",
-       "      <td>12</td>\n",
-       "      <td>tayassu pecari</td>\n",
-       "      <td>tayassu pecari</td>\n",
-       "      <td>white-lipped peccary</td>\n",
-       "      <td>08-28-2012 11:19:47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>tayassuidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>tayassu</td>\n",
-       "      <td>tayassu pecari</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4754539</th>\n",
-       "      <td>WCS Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/wcs-...</td>\n",
-       "      <td>WCS Camera Traps : 8f8a4dee-92d5-11e9-a3af-000...</td>\n",
-       "      <td>WCS Camera Traps : unknown</td>\n",
-       "      <td>WCS Camera Traps : 4326</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>tinamus major</td>\n",
-       "      <td>tinamus major</td>\n",
-       "      <td>great tinamou</td>\n",
-       "      <td>07-31-2013 05:43:18</td>\n",
+       "      <th>6</th>\n",
+       "      <td>Caltech Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 59b93a3b-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 7013c982-5567-11e8-be89...</td>\n",
+       "      <td>Caltech Camera Traps : 61</td>\n",
+       "      <td>2</td>\n",
+       "      <td>rabbit</td>\n",
+       "      <td>leporidae</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>leporidae</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>tinamidae</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>tinamus</td>\n",
-       "      <td>tinamus major</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10 rows × 31 columns</p>\n",
+       "<p>5 rows × 34 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                dataset_name   \n",
-       "15355844    SWG Camera Traps  \\\n",
-       "15098255    SWG Camera Traps   \n",
-       "15750309    SWG Camera Traps   \n",
-       "812622                 NACTI   \n",
-       "2050788                NACTI   \n",
-       "12610859  Snapshot Serengeti   \n",
-       "2463379                NACTI   \n",
-       "1296206                NACTI   \n",
-       "4026992     WCS Camera Traps   \n",
-       "4754539     WCS Camera Traps   \n",
-       "\n",
-       "                                                        url   \n",
-       "15355844  https://lilablobssc.blob.core.windows.net/swg-...  \\\n",
-       "15098255  https://lilablobssc.blob.core.windows.net/swg-...   \n",
-       "15750309  https://lilablobssc.blob.core.windows.net/swg-...   \n",
-       "812622    https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "2050788   https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "12610859  https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "2463379   https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "1296206   https://lilablobssc.blob.core.windows.net/nact...   \n",
-       "4026992   https://lilablobssc.blob.core.windows.net/wcs-...   \n",
-       "4754539   https://lilablobssc.blob.core.windows.net/wcs-...   \n",
-       "\n",
-       "                                                   image_id   \n",
-       "15355844  SWG Camera Traps : cebffb73-8c29-11eb-92ab-000...  \\\n",
-       "15098255  SWG Camera Traps : cc2b9e0b-8c29-11eb-bd63-000...   \n",
-       "15750309  SWG Camera Traps : d29fd7c0-8c29-11eb-9d65-000...   \n",
-       "812622           NACTI : CA-17_07_01_2016_CA-17_0038090.JPG   \n",
-       "2050788          NACTI : FL-12_08_04_2016_FL-12_0071339.JPG   \n",
-       "12610859  Snapshot Serengeti : S9/F07/F07_R2/S9_F07_R2_I...   \n",
-       "2463379          NACTI : FL-21_07_29_2015_FL-21_0010466.jpg   \n",
-       "1296206          NACTI : CA-42_10_06_2016_CA-42_0022200.JPG   \n",
-       "4026992   WCS Camera Traps : 098d7290-92d5-11e9-bb41-000...   \n",
-       "4754539   WCS Camera Traps : 8f8a4dee-92d5-11e9-a3af-000...   \n",
-       "\n",
-       "                                                sequence_id   \n",
-       "15355844  SWG Camera Traps : e42a22f0-8c29-11eb-9c41-000...  \\\n",
-       "15098255  SWG Camera Traps : 2801caa6-8c2a-11eb-9118-000...   \n",
-       "15750309  SWG Camera Traps : 39b3e643-8c2a-11eb-b05f-000...   \n",
-       "812622                                      NACTI : unknown   \n",
-       "2050788                                     NACTI : unknown   \n",
-       "12610859               Snapshot Serengeti : SER_S9#F07#2#50   \n",
-       "2463379                                     NACTI : unknown   \n",
-       "1296206                                     NACTI : unknown   \n",
-       "4026992                 WCS Camera Traps : bol-017-d0050-90   \n",
-       "4754539                          WCS Camera Traps : unknown   \n",
-       "\n",
-       "                          location_id  frame_num          original_label   \n",
-       "15355844  SWG Camera Traps : loc_0263          2  large_antlered_muntjac  \\\n",
-       "15098255  SWG Camera Traps : loc_0512          2           ferret_badger   \n",
-       "15750309  SWG Camera Traps : loc_0588          7    stump_tailed_macaque   \n",
-       "812622      NACTI : Lebec, California         -1              bos taurus   \n",
-       "2050788          NACTI : Archbold, FL         -1              bos taurus   \n",
-       "12610859     Snapshot Serengeti : F07          1                elephant   \n",
-       "2463379          NACTI : Archbold, FL         -1              bos taurus   \n",
-       "1296206     NACTI : Lebec, California         -1              lynx rufus   \n",
-       "4026992        WCS Camera Traps : 650         12          tayassu pecari   \n",
-       "4754539       WCS Camera Traps : 4326         -1           tinamus major   \n",
-       "\n",
-       "                 scientific_name             common_name             datetime   \n",
-       "15355844  muntiacus vuquangensis  large-antlered muntjac  06-24-2019 19:36:35  \\\n",
-       "15098255                melogale           ferret badger  07-08-2020 21:03:55   \n",
-       "15750309        macaca arctoides    stump-tailed macaque  08-26-2019 10:52:49   \n",
-       "812622                bos taurus            domestic cow                  NaN   \n",
-       "2050788               bos taurus            domestic cow                  NaN   \n",
-       "12610859      loxodonta africana   african bush elephant  10-16-2014 09:44:07   \n",
-       "2463379               bos taurus            domestic cow                  NaN   \n",
-       "1296206               lynx rufus                  bobcat                  NaN   \n",
-       "4026992           tayassu pecari    white-lipped peccary  08-28-2012 11:19:47   \n",
-       "4754539            tinamus major           great tinamou  07-31-2013 05:43:18   \n",
-       "\n",
-       "          ...   infraorder superfamily           family        subfamily   \n",
-       "15355844  ...          NaN         NaN         cervidae         cervinae  \\\n",
-       "15098255  ...          NaN         NaN       mustelidae     helictidinae   \n",
-       "15750309  ...  simiiformes         NaN  cercopithecidae  cercopithecinae   \n",
-       "812622    ...          NaN         NaN          bovidae          bovinae   \n",
-       "2050788   ...          NaN         NaN          bovidae          bovinae   \n",
-       "12610859  ...          NaN         NaN     elephantidae              NaN   \n",
-       "2463379   ...          NaN         NaN          bovidae          bovinae   \n",
-       "1296206   ...          NaN         NaN          felidae          felinae   \n",
-       "4026992   ...          NaN         NaN      tayassuidae              NaN   \n",
-       "4754539   ...          NaN         NaN        tinamidae              NaN   \n",
-       "\n",
-       "               tribe      genus                 species subspecies variety   \n",
-       "15355844  muntiacini  muntiacus  muntiacus vuquangensis        NaN     NaN  \\\n",
-       "15098255         NaN   melogale                     NaN        NaN     NaN   \n",
-       "15750309   papionini     macaca        macaca arctoides        NaN     NaN   \n",
-       "812622        bovini        bos              bos taurus        NaN     NaN   \n",
-       "2050788       bovini        bos              bos taurus        NaN     NaN   \n",
-       "12610859         NaN  loxodonta      loxodonta africana        NaN     NaN   \n",
-       "2463379       bovini        bos              bos taurus        NaN     NaN   \n",
-       "1296206          NaN       lynx              lynx rufus        NaN     NaN   \n",
-       "4026992          NaN    tayassu          tayassu pecari        NaN     NaN   \n",
-       "4754539          NaN    tinamus           tinamus major        NaN     NaN   \n",
+       "           dataset_name                                            url_gcp  \\\n",
+       "1  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "2  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "3  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "5  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "6  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                             url_aws  \\\n",
+       "1  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "2  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "3  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "5  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "6  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "\n",
+       "                                           url_azure  \\\n",
+       "1  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "2  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "3  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "5  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "6  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "\n",
+       "                                            image_id  \\\n",
+       "1  Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...   \n",
+       "2  Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...   \n",
+       "3  Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...   \n",
+       "5  Caltech Camera Traps : 5a096955-23d2-11e8-a6a3...   \n",
+       "6  Caltech Camera Traps : 59b93a3b-23d2-11e8-a6a3...   \n",
        "\n",
-       "         url_dupe  \n",
-       "15355844    False  \n",
-       "15098255    False  \n",
-       "15750309    False  \n",
-       "812622      False  \n",
-       "2050788     False  \n",
-       "12610859     True  \n",
-       "2463379     False  \n",
-       "1296206     False  \n",
-       "4026992     False  \n",
-       "4754539     False  \n",
+       "                                         sequence_id  \\\n",
+       "1  Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...   \n",
+       "2  Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...   \n",
+       "3  Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...   \n",
+       "5  Caltech Camera Traps : 70096335-5567-11e8-a99a...   \n",
+       "6  Caltech Camera Traps : 7013c982-5567-11e8-be89...   \n",
        "\n",
-       "[10 rows x 31 columns]"
+       "                 location_id  frame_num original_label       scientific_name  \\\n",
+       "1  Caltech Camera Traps : 26          1           deer            odocoileus   \n",
+       "2  Caltech Camera Traps : 38          2            cat           felis catus   \n",
+       "3  Caltech Camera Traps : 38          2        opossum  didelphis virginiana   \n",
+       "5  Caltech Camera Traps : 36          1            car                   NaN   \n",
+       "6  Caltech Camera Traps : 61          2         rabbit             leporidae   \n",
+       "\n",
+       "   ... superfamily       family    subfamily        tribe       genus  \\\n",
+       "1  ...         NaN     cervidae  capreolinae  odocoileini  odocoileus   \n",
+       "2  ...         NaN      felidae      felinae          NaN       felis   \n",
+       "3  ...         NaN  didelphidae  didelphinae   didelphini   didelphis   \n",
+       "5  ...         NaN          NaN          NaN          NaN         NaN   \n",
+       "6  ...         NaN    leporidae          NaN          NaN         NaN   \n",
+       "\n",
+       "                species subspecies variety multi_species num_species  \n",
+       "1                   NaN        NaN     NaN         False         NaN  \n",
+       "2           felis catus        NaN     NaN         False         NaN  \n",
+       "3  didelphis virginiana        NaN     NaN         False         NaN  \n",
+       "5                   NaN        NaN     NaN         False         NaN  \n",
+       "6                   NaN        NaN     NaN         False         NaN  \n",
+       "\n",
+       "[5 rows x 34 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_genusSpecies.sample(10)"
+    "for img in multi_sp_imgs:\n",
+    "    df_cleaned.loc[df_cleaned[\"image_id\"] == img, \"num_species\"] = df_cleaned.loc[df_cleaned[\"image_id\"] == img].shape[0]\n",
+    "    \n",
+    "df_cleaned.head()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 31,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
+   "source": [
+    "#### Save this to CSV now we got those counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_cleaned.to_csv(\"../data/lila_image_urls_and_labels.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1679,17 +1661,16 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
        "      <th>image_id</th>\n",
        "      <th>sequence_id</th>\n",
        "      <th>location_id</th>\n",
        "      <th>frame_num</th>\n",
        "      <th>original_label</th>\n",
        "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
        "      <th>...</th>\n",
-       "      <th>infraorder</th>\n",
        "      <th>superfamily</th>\n",
        "      <th>family</th>\n",
        "      <th>subfamily</th>\n",
@@ -1698,25 +1679,25 @@
        "      <th>species</th>\n",
        "      <th>subspecies</th>\n",
        "      <th>variety</th>\n",
-       "      <th>url_dupe</th>\n",
+       "      <th>multi_species</th>\n",
+       "      <th>num_species</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>495</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 70139a99-5567-11e8-bb56...</td>\n",
        "      <td>Caltech Camera Traps : 61</td>\n",
        "      <td>3</td>\n",
        "      <td>badger</td>\n",
        "      <td>taxidea taxus</td>\n",
-       "      <td>badger</td>\n",
-       "      <td>10-20-2013 23:01:55</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>mustelidae</td>\n",
        "      <td>taxidiinae</td>\n",
        "      <td>NaN</td>\n",
@@ -1725,22 +1706,22 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>496</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 70139a99-5567-11e8-bb56...</td>\n",
        "      <td>Caltech Camera Traps : 61</td>\n",
        "      <td>3</td>\n",
        "      <td>coyote</td>\n",
        "      <td>canis latrans</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>10-20-2013 23:01:55</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>canidae</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1749,22 +1730,22 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2645</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5968bf89-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3...</td>\n",
        "      <td>Caltech Camera Traps : 43</td>\n",
        "      <td>1</td>\n",
        "      <td>squirrel</td>\n",
        "      <td>sciurus</td>\n",
-       "      <td>squirrel</td>\n",
-       "      <td>07-24-2011 16:29:50</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>sciuridae</td>\n",
        "      <td>sciurinae</td>\n",
        "      <td>sciurini</td>\n",
@@ -1773,22 +1754,22 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2646</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 5968bf89-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3...</td>\n",
        "      <td>Caltech Camera Traps : 43</td>\n",
        "      <td>1</td>\n",
        "      <td>bobcat</td>\n",
        "      <td>lynx rufus</td>\n",
-       "      <td>bobcat</td>\n",
-       "      <td>07-24-2011 16:29:50</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>felidae</td>\n",
        "      <td>felinae</td>\n",
        "      <td>NaN</td>\n",
@@ -1797,118 +1778,22 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14220</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59a49aa6-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 70139a99-5567-11e8-bb56...</td>\n",
-       "      <td>Caltech Camera Traps : 61</td>\n",
-       "      <td>1</td>\n",
-       "      <td>badger</td>\n",
-       "      <td>taxidea taxus</td>\n",
-       "      <td>badger</td>\n",
-       "      <td>10-20-2013 23:01:53</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>mustelidae</td>\n",
-       "      <td>taxidiinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>taxidea</td>\n",
-       "      <td>taxidea taxus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14221</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
        "      <td>Caltech Camera Traps : 59a49aa6-23d2-11e8-a6a3...</td>\n",
        "      <td>Caltech Camera Traps : 70139a99-5567-11e8-bb56...</td>\n",
        "      <td>Caltech Camera Traps : 61</td>\n",
        "      <td>1</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>10-20-2013 23:01:53</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45115</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59a7bb76-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 70139a35-5567-11e8-b800...</td>\n",
-       "      <td>Caltech Camera Traps : 61</td>\n",
-       "      <td>1</td>\n",
-       "      <td>badger</td>\n",
-       "      <td>taxidea taxus</td>\n",
-       "      <td>badger</td>\n",
-       "      <td>10-20-2013 23:01:49</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>mustelidae</td>\n",
-       "      <td>taxidiinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>taxidea</td>\n",
-       "      <td>taxidea taxus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45116</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59a7bb76-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 70139a35-5567-11e8-b800...</td>\n",
-       "      <td>Caltech Camera Traps : 61</td>\n",
-       "      <td>1</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>10-20-2013 23:01:49</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50215</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59f5fd71-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 70139a35-5567-11e8-b800...</td>\n",
-       "      <td>Caltech Camera Traps : 61</td>\n",
-       "      <td>2</td>\n",
        "      <td>badger</td>\n",
        "      <td>taxidea taxus</td>\n",
-       "      <td>badger</td>\n",
-       "      <td>10-20-2013 23:01:50</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>mustelidae</td>\n",
        "      <td>taxidiinae</td>\n",
        "      <td>NaN</td>\n",
@@ -1917,2778 +1802,176 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50216</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59f5fd71-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 70139a35-5567-11e8-b800...</td>\n",
-       "      <td>Caltech Camera Traps : 61</td>\n",
-       "      <td>2</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>10-20-2013 23:01:50</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10 rows × 31 columns</p>\n",
+       "<p>5 rows × 34 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "               dataset_name   \n",
-       "495    Caltech Camera Traps  \\\n",
+       "               dataset_name  \\\n",
+       "495    Caltech Camera Traps   \n",
        "496    Caltech Camera Traps   \n",
        "2645   Caltech Camera Traps   \n",
        "2646   Caltech Camera Traps   \n",
        "14220  Caltech Camera Traps   \n",
-       "14221  Caltech Camera Traps   \n",
-       "45115  Caltech Camera Traps   \n",
-       "45116  Caltech Camera Traps   \n",
-       "50215  Caltech Camera Traps   \n",
-       "50216  Caltech Camera Traps   \n",
        "\n",
-       "                                                     url   \n",
-       "495    https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "496    https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "2645   https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "2646   https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "14220  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "14221  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "45115  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "45116  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "50215  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "50216  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "\n",
-       "                                                image_id   \n",
-       "495    Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3...  \\\n",
+       "                                                 url_gcp  \\\n",
+       "495    https://storage.googleapis.com/public-datasets...   \n",
+       "496    https://storage.googleapis.com/public-datasets...   \n",
+       "2645   https://storage.googleapis.com/public-datasets...   \n",
+       "2646   https://storage.googleapis.com/public-datasets...   \n",
+       "14220  https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                                 url_aws  \\\n",
+       "495    http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "496    http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "2645   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "2646   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "14220  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "\n",
+       "                                               url_azure  \\\n",
+       "495    https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "496    https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "2645   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "2646   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "14220  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "\n",
+       "                                                image_id  \\\n",
+       "495    Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3...   \n",
        "496    Caltech Camera Traps : 5a1cafe6-23d2-11e8-a6a3...   \n",
        "2645   Caltech Camera Traps : 5968bf89-23d2-11e8-a6a3...   \n",
        "2646   Caltech Camera Traps : 5968bf89-23d2-11e8-a6a3...   \n",
        "14220  Caltech Camera Traps : 59a49aa6-23d2-11e8-a6a3...   \n",
-       "14221  Caltech Camera Traps : 59a49aa6-23d2-11e8-a6a3...   \n",
-       "45115  Caltech Camera Traps : 59a7bb76-23d2-11e8-a6a3...   \n",
-       "45116  Caltech Camera Traps : 59a7bb76-23d2-11e8-a6a3...   \n",
-       "50215  Caltech Camera Traps : 59f5fd71-23d2-11e8-a6a3...   \n",
-       "50216  Caltech Camera Traps : 59f5fd71-23d2-11e8-a6a3...   \n",
        "\n",
-       "                                             sequence_id   \n",
-       "495    Caltech Camera Traps : 70139a99-5567-11e8-bb56...  \\\n",
+       "                                             sequence_id  \\\n",
+       "495    Caltech Camera Traps : 70139a99-5567-11e8-bb56...   \n",
        "496    Caltech Camera Traps : 70139a99-5567-11e8-bb56...   \n",
        "2645   Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3...   \n",
        "2646   Caltech Camera Traps : 6f0b1bdc-5567-11e8-8df3...   \n",
        "14220  Caltech Camera Traps : 70139a99-5567-11e8-bb56...   \n",
-       "14221  Caltech Camera Traps : 70139a99-5567-11e8-bb56...   \n",
-       "45115  Caltech Camera Traps : 70139a35-5567-11e8-b800...   \n",
-       "45116  Caltech Camera Traps : 70139a35-5567-11e8-b800...   \n",
-       "50215  Caltech Camera Traps : 70139a35-5567-11e8-b800...   \n",
-       "50216  Caltech Camera Traps : 70139a35-5567-11e8-b800...   \n",
        "\n",
-       "                     location_id  frame_num original_label scientific_name   \n",
-       "495    Caltech Camera Traps : 61          3         badger   taxidea taxus  \\\n",
+       "                     location_id  frame_num original_label scientific_name  \\\n",
+       "495    Caltech Camera Traps : 61          3         badger   taxidea taxus   \n",
        "496    Caltech Camera Traps : 61          3         coyote   canis latrans   \n",
        "2645   Caltech Camera Traps : 43          1       squirrel         sciurus   \n",
        "2646   Caltech Camera Traps : 43          1         bobcat      lynx rufus   \n",
        "14220  Caltech Camera Traps : 61          1         badger   taxidea taxus   \n",
-       "14221  Caltech Camera Traps : 61          1         coyote   canis latrans   \n",
-       "45115  Caltech Camera Traps : 61          1         badger   taxidea taxus   \n",
-       "45116  Caltech Camera Traps : 61          1         coyote   canis latrans   \n",
-       "50215  Caltech Camera Traps : 61          2         badger   taxidea taxus   \n",
-       "50216  Caltech Camera Traps : 61          2         coyote   canis latrans   \n",
-       "\n",
-       "      common_name             datetime  ... infraorder superfamily   \n",
-       "495        badger  10-20-2013 23:01:55  ...        NaN         NaN  \\\n",
-       "496        coyote  10-20-2013 23:01:55  ...        NaN         NaN   \n",
-       "2645     squirrel  07-24-2011 16:29:50  ...        NaN         NaN   \n",
-       "2646       bobcat  07-24-2011 16:29:50  ...        NaN         NaN   \n",
-       "14220      badger  10-20-2013 23:01:53  ...        NaN         NaN   \n",
-       "14221      coyote  10-20-2013 23:01:53  ...        NaN         NaN   \n",
-       "45115      badger  10-20-2013 23:01:49  ...        NaN         NaN   \n",
-       "45116      coyote  10-20-2013 23:01:49  ...        NaN         NaN   \n",
-       "50215      badger  10-20-2013 23:01:50  ...        NaN         NaN   \n",
-       "50216      coyote  10-20-2013 23:01:50  ...        NaN         NaN   \n",
        "\n",
-       "           family   subfamily     tribe    genus        species subspecies   \n",
-       "495    mustelidae  taxidiinae       NaN  taxidea  taxidea taxus        NaN  \\\n",
-       "496       canidae         NaN       NaN    canis  canis latrans        NaN   \n",
-       "2645    sciuridae   sciurinae  sciurini  sciurus            NaN        NaN   \n",
-       "2646      felidae     felinae       NaN     lynx     lynx rufus        NaN   \n",
-       "14220  mustelidae  taxidiinae       NaN  taxidea  taxidea taxus        NaN   \n",
-       "14221     canidae         NaN       NaN    canis  canis latrans        NaN   \n",
-       "45115  mustelidae  taxidiinae       NaN  taxidea  taxidea taxus        NaN   \n",
-       "45116     canidae         NaN       NaN    canis  canis latrans        NaN   \n",
-       "50215  mustelidae  taxidiinae       NaN  taxidea  taxidea taxus        NaN   \n",
-       "50216     canidae         NaN       NaN    canis  canis latrans        NaN   \n",
-       "\n",
-       "      variety url_dupe  \n",
-       "495       NaN     True  \n",
-       "496       NaN     True  \n",
-       "2645      NaN     True  \n",
-       "2646      NaN     True  \n",
-       "14220     NaN     True  \n",
-       "14221     NaN     True  \n",
-       "45115     NaN     True  \n",
-       "45116     NaN     True  \n",
-       "50215     NaN     True  \n",
-       "50216     NaN     True  \n",
-       "\n",
-       "[10 rows x 31 columns]"
+       "       ... superfamily      family   subfamily     tribe    genus  \\\n",
+       "495    ...         NaN  mustelidae  taxidiinae       NaN  taxidea   \n",
+       "496    ...         NaN     canidae         NaN       NaN    canis   \n",
+       "2645   ...         NaN   sciuridae   sciurinae  sciurini  sciurus   \n",
+       "2646   ...         NaN     felidae     felinae       NaN     lynx   \n",
+       "14220  ...         NaN  mustelidae  taxidiinae       NaN  taxidea   \n",
+       "\n",
+       "             species subspecies variety multi_species num_species  \n",
+       "495    taxidea taxus        NaN     NaN          True         2.0  \n",
+       "496    canis latrans        NaN     NaN          True         2.0  \n",
+       "2645             NaN        NaN     NaN          True         2.0  \n",
+       "2646      lynx rufus        NaN     NaN          True         2.0  \n",
+       "14220  taxidea taxus        NaN     NaN          True         2.0  \n",
+       "\n",
+       "[5 rows x 34 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "duplicated_urls = df_genusSpecies.loc[df_genusSpecies.url_dupe]\n",
-    "duplicated_urls.head(10)"
+    "df_cleaned.loc[df_cleaned[\"multi_species\"]].head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From this small sample, it looks like they are distinguished by their frame number (`frame_num`) and the animal itself--there is more than one animal (of a different species) in frame so it duplicates the image information because there is an entry for each species of animal in the image."
+    "How many different species do we generally have when we have multiple species in an image?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "num_species\n",
+       "2.0    195134\n",
+       "3.0      6069\n",
+       "4.0       368\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cleaned.num_species.value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have 1,046,985 distinct sequence IDs for the 6,901,819 unique image IDs, suggesting an average of 6 images per sequence?"
+    "We have 97,567 images with 2 different species (most multi-species instances), 2,023 with 3 different species, and 92 with 4.\n",
+    "\n",
+    "We will want to dedicate some more time to exploring some of these taxonomic counts, but we'll first look at the number of unique taxa (by Linnean 7-rank (`unique_7_tuple`) and then by all taxonomic labels (`unique_taxa`) available). We'll compare these to the number of unique scientific and common names, then perhaps add a count of number of creatures based on one of those labels. At that point we may save another copy of this CSV and start a new analysis notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([['Caltech Camera Traps',\n",
-       "        'https://lilablobssc.blob.core.windows.net/caltech-unzipped/cct_images/5a0b016f-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "        'Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3-ec086b02610b',\n",
-       "        'Caltech Camera Traps : 6f27ed66-5567-11e8-9e92-dca9047ef277',\n",
-       "        'Caltech Camera Traps : 26', 1, 'deer', 'odocoileus', 'deer',\n",
-       "        '11-04-2013 18:37:07', 'image', 'animalia', 'chordata',\n",
-       "        'vertebrata', nan, 'mammalia', 'theria', 'placentalia',\n",
-       "        'laurasiatheria', 'artiodactyla', 'ruminantia', nan, nan,\n",
-       "        'cervidae', 'capreolinae', 'odocoileini', 'odocoileus', nan, nan,\n",
-       "        nan, False]], dtype=object)"
+       "annotation_level\n",
+       "sequence    5133907\n",
+       "image       2919136\n",
+       "unknown     2912859\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_genusSpecies.head(1).values"
+    "df_cleaned.annotation_level.value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The end of the URL `cct_images/...jpg` matches the `file` indicator in the model results from LILA BC for MegaDetector. We will use agreement about the animal's precense from both [v5a and v5b models](https://lila.science/megadetector-results-for-camera-trap-datasets/) (v4 for Snapshot Serengeti) to determine appropriate images from a sequence to keep (only one per sequence to avoid testing on the same animal (instance) multiple times). \n",
+    "We've got ~3M labeled to the image and another 3M unknonwn labeling (all from NACTI, which Andrey will check on), leaving ~5M labeled only to at the sequence level. This _should_ give Jianyang something to work with to start exploring near-duplicate de-duplication. \n",
     "\n",
-    "First, let's remove the images that have multiple animals, as indicated by the `url_dupe` column."
+    "Let's update the non-multi species images to show 1 in the `num_species` column, then move on to checking the taxonomy strings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
-       "      <th>image_id</th>\n",
-       "      <th>sequence_id</th>\n",
-       "      <th>location_id</th>\n",
-       "      <th>frame_num</th>\n",
-       "      <th>original_label</th>\n",
-       "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>...</th>\n",
-       "      <th>infraorder</th>\n",
-       "      <th>superfamily</th>\n",
-       "      <th>family</th>\n",
-       "      <th>subfamily</th>\n",
-       "      <th>tribe</th>\n",
-       "      <th>genus</th>\n",
-       "      <th>species</th>\n",
-       "      <th>subspecies</th>\n",
-       "      <th>variety</th>\n",
-       "      <th>url_dupe</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...</td>\n",
-       "      <td>Caltech Camera Traps : 26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>deer</td>\n",
-       "      <td>odocoileus</td>\n",
-       "      <td>deer</td>\n",
-       "      <td>11-04-2013 18:37:07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>cervidae</td>\n",
-       "      <td>capreolinae</td>\n",
-       "      <td>odocoileini</td>\n",
-       "      <td>odocoileus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>05-09-2012 07:33:45</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felidae</td>\n",
-       "      <td>felinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felis</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>opossum</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>virginia opossum</td>\n",
-       "      <td>03-29-2012 02:34:13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>didelphidae</td>\n",
-       "      <td>didelphinae</td>\n",
-       "      <td>didelphini</td>\n",
-       "      <td>didelphis</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a217843-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 70115511-5567-11e8-a1fd...</td>\n",
-       "      <td>Caltech Camera Traps : 41</td>\n",
-       "      <td>1</td>\n",
-       "      <td>deer</td>\n",
-       "      <td>odocoileus</td>\n",
-       "      <td>deer</td>\n",
-       "      <td>09-29-2012 18:14:45</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>cervidae</td>\n",
-       "      <td>capreolinae</td>\n",
-       "      <td>odocoileini</td>\n",
-       "      <td>odocoileus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a2176e7-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f011019-5567-11e8-a650...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>dog</td>\n",
-       "      <td>canis familiaris</td>\n",
-       "      <td>domestic dog</td>\n",
-       "      <td>11-29-2011 17:28:26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis familiaris</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           dataset_name                                                url   \n",
-       "1  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "2  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "3  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "7  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "8  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "\n",
-       "                                            image_id   \n",
-       "1  Caltech Camera Traps : 5a0b016f-23d2-11e8-a6a3...  \\\n",
-       "2  Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...   \n",
-       "3  Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...   \n",
-       "7  Caltech Camera Traps : 5a217843-23d2-11e8-a6a3...   \n",
-       "8  Caltech Camera Traps : 5a2176e7-23d2-11e8-a6a3...   \n",
-       "\n",
-       "                                         sequence_id   \n",
-       "1  Caltech Camera Traps : 6f27ed66-5567-11e8-9e92...  \\\n",
-       "2  Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...   \n",
-       "3  Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...   \n",
-       "7  Caltech Camera Traps : 70115511-5567-11e8-a1fd...   \n",
-       "8  Caltech Camera Traps : 6f011019-5567-11e8-a650...   \n",
-       "\n",
-       "                 location_id  frame_num original_label       scientific_name   \n",
-       "1  Caltech Camera Traps : 26          1           deer            odocoileus  \\\n",
-       "2  Caltech Camera Traps : 38          2            cat           felis catus   \n",
-       "3  Caltech Camera Traps : 38          2        opossum  didelphis virginiana   \n",
-       "7  Caltech Camera Traps : 41          1           deer            odocoileus   \n",
-       "8  Caltech Camera Traps : 38          2            dog      canis familiaris   \n",
-       "\n",
-       "        common_name             datetime  ... infraorder superfamily   \n",
-       "1              deer  11-04-2013 18:37:07  ...        NaN         NaN  \\\n",
-       "2               cat  05-09-2012 07:33:45  ...        NaN         NaN   \n",
-       "3  virginia opossum  03-29-2012 02:34:13  ...        NaN         NaN   \n",
-       "7              deer  09-29-2012 18:14:45  ...        NaN         NaN   \n",
-       "8      domestic dog  11-29-2011 17:28:26  ...        NaN         NaN   \n",
-       "\n",
-       "        family    subfamily        tribe       genus               species   \n",
-       "1     cervidae  capreolinae  odocoileini  odocoileus                   NaN  \\\n",
-       "2      felidae      felinae          NaN       felis           felis catus   \n",
-       "3  didelphidae  didelphinae   didelphini   didelphis  didelphis virginiana   \n",
-       "7     cervidae  capreolinae  odocoileini  odocoileus                   NaN   \n",
-       "8      canidae          NaN          NaN       canis      canis familiaris   \n",
-       "\n",
-       "  subspecies variety url_dupe  \n",
-       "1        NaN     NaN    False  \n",
-       "2        NaN     NaN    False  \n",
-       "3        NaN     NaN    False  \n",
-       "7        NaN     NaN    False  \n",
-       "8        NaN     NaN    False  \n",
-       "\n",
-       "[5 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_genusSpecies = df_genusSpecies.loc[~df_genusSpecies.url_dupe]\n",
-    "dedupe_genusSpecies.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's quickly check our stats on this subset (eg, `speices`/`genus` values)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 6825717 entries, 1 to 16833847\n",
-      "Data columns (total 31 columns):\n",
-      " #   Column            Non-Null Count    Dtype \n",
-      "---  ------            --------------    ----- \n",
-      " 0   dataset_name      6825717 non-null  object\n",
-      " 1   url               6825717 non-null  object\n",
-      " 2   image_id          6825717 non-null  object\n",
-      " 3   sequence_id       6825717 non-null  object\n",
-      " 4   location_id       6825717 non-null  object\n",
-      " 5   frame_num         6825717 non-null  int64 \n",
-      " 6   original_label    6825717 non-null  object\n",
-      " 7   scientific_name   6825717 non-null  object\n",
-      " 8   common_name       6825717 non-null  object\n",
-      " 9   datetime          3871189 non-null  object\n",
-      " 10  annotation_level  6825717 non-null  object\n",
-      " 11  kingdom           6825717 non-null  object\n",
-      " 12  phylum            6825717 non-null  object\n",
-      " 13  subphylum         6825717 non-null  object\n",
-      " 14  superclass        39 non-null       object\n",
-      " 15  class             6825717 non-null  object\n",
-      " 16  subclass          6640843 non-null  object\n",
-      " 17  infraclass        6640804 non-null  object\n",
-      " 18  superorder        6603873 non-null  object\n",
-      " 19  order             6825717 non-null  object\n",
-      " 20  suborder          5592452 non-null  object\n",
-      " 21  infraorder        506319 non-null   object\n",
-      " 22  superfamily       344989 non-null   object\n",
-      " 23  family            6825717 non-null  object\n",
-      " 24  subfamily         5701678 non-null  object\n",
-      " 25  tribe             5073268 non-null  object\n",
-      " 26  genus             6825717 non-null  object\n",
-      " 27  species           6365985 non-null  object\n",
-      " 28  subspecies        20249 non-null    object\n",
-      " 29  variety           1141 non-null     object\n",
-      " 30  url_dupe          6825717 non-null  bool  \n",
-      "dtypes: bool(1), int64(1), object(29)\n",
-      "memory usage: 1.6+ GB\n"
-     ]
-    }
-   ],
-   "source": [
-    "dedupe_genusSpecies.info(show_counts = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dataset_name             18\n",
-       "url                 6825717\n",
-       "image_id            6825717\n",
-       "sequence_id         1021256\n",
-       "location_id            6017\n",
-       "frame_num              6068\n",
-       "original_label          972\n",
-       "scientific_name         754\n",
-       "common_name             815\n",
-       "datetime            2549108\n",
-       "annotation_level          3\n",
-       "genus                   476\n",
-       "species                 667\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols = list(dedupe_genusSpecies.columns[:11])\n",
-    "cols.append('genus')\n",
-    "cols.append('species')\n",
-    "dedupe_genusSpecies[cols].nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "All datasets are still represented, all URLs and `image_id`s are unique, and we still have 667 distinct species. \n",
-    "\n",
-    "We should probably drop the null `species` values since we have such a large number of images and prefer to keep it more specific."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
-       "      <th>image_id</th>\n",
-       "      <th>sequence_id</th>\n",
-       "      <th>location_id</th>\n",
-       "      <th>frame_num</th>\n",
-       "      <th>original_label</th>\n",
-       "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>...</th>\n",
-       "      <th>infraorder</th>\n",
-       "      <th>superfamily</th>\n",
-       "      <th>family</th>\n",
-       "      <th>subfamily</th>\n",
-       "      <th>tribe</th>\n",
-       "      <th>genus</th>\n",
-       "      <th>species</th>\n",
-       "      <th>subspecies</th>\n",
-       "      <th>variety</th>\n",
-       "      <th>url_dupe</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>05-09-2012 07:33:45</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felidae</td>\n",
-       "      <td>felinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felis</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>opossum</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>virginia opossum</td>\n",
-       "      <td>03-29-2012 02:34:13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>didelphidae</td>\n",
-       "      <td>didelphinae</td>\n",
-       "      <td>didelphini</td>\n",
-       "      <td>didelphis</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a2176e7-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f011019-5567-11e8-a650...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>dog</td>\n",
-       "      <td>canis familiaris</td>\n",
-       "      <td>domestic dog</td>\n",
-       "      <td>11-29-2011 17:28:26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis familiaris</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 598de824-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f8257b0-5567-11e8-b82e...</td>\n",
-       "      <td>Caltech Camera Traps : 57</td>\n",
-       "      <td>1</td>\n",
-       "      <td>raccoon</td>\n",
-       "      <td>procyon lotor</td>\n",
-       "      <td>raccoon</td>\n",
-       "      <td>12-25-2013 21:48:54</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>procyonidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>procyon</td>\n",
-       "      <td>procyon lotor</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a1cb0d8-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f18c3eb-5567-11e8-8b81...</td>\n",
-       "      <td>Caltech Camera Traps : 46</td>\n",
-       "      <td>2</td>\n",
-       "      <td>opossum</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>virginia opossum</td>\n",
-       "      <td>04-19-2012 22:13:23</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>didelphidae</td>\n",
-       "      <td>didelphinae</td>\n",
-       "      <td>didelphini</td>\n",
-       "      <td>didelphis</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            dataset_name                                                url   \n",
-       "2   Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "3   Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "8   Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "10  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "14  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "\n",
-       "                                             image_id   \n",
-       "2   Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...  \\\n",
-       "3   Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...   \n",
-       "8   Caltech Camera Traps : 5a2176e7-23d2-11e8-a6a3...   \n",
-       "10  Caltech Camera Traps : 598de824-23d2-11e8-a6a3...   \n",
-       "14  Caltech Camera Traps : 5a1cb0d8-23d2-11e8-a6a3...   \n",
-       "\n",
-       "                                          sequence_id   \n",
-       "2   Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...  \\\n",
-       "3   Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...   \n",
-       "8   Caltech Camera Traps : 6f011019-5567-11e8-a650...   \n",
-       "10  Caltech Camera Traps : 6f8257b0-5567-11e8-b82e...   \n",
-       "14  Caltech Camera Traps : 6f18c3eb-5567-11e8-8b81...   \n",
-       "\n",
-       "                  location_id  frame_num original_label       scientific_name   \n",
-       "2   Caltech Camera Traps : 38          2            cat           felis catus  \\\n",
-       "3   Caltech Camera Traps : 38          2        opossum  didelphis virginiana   \n",
-       "8   Caltech Camera Traps : 38          2            dog      canis familiaris   \n",
-       "10  Caltech Camera Traps : 57          1        raccoon         procyon lotor   \n",
-       "14  Caltech Camera Traps : 46          2        opossum  didelphis virginiana   \n",
-       "\n",
-       "         common_name             datetime  ... infraorder superfamily   \n",
-       "2                cat  05-09-2012 07:33:45  ...        NaN         NaN  \\\n",
-       "3   virginia opossum  03-29-2012 02:34:13  ...        NaN         NaN   \n",
-       "8       domestic dog  11-29-2011 17:28:26  ...        NaN         NaN   \n",
-       "10           raccoon  12-25-2013 21:48:54  ...        NaN         NaN   \n",
-       "14  virginia opossum  04-19-2012 22:13:23  ...        NaN         NaN   \n",
-       "\n",
-       "         family    subfamily       tribe      genus               species   \n",
-       "2       felidae      felinae         NaN      felis           felis catus  \\\n",
-       "3   didelphidae  didelphinae  didelphini  didelphis  didelphis virginiana   \n",
-       "8       canidae          NaN         NaN      canis      canis familiaris   \n",
-       "10  procyonidae          NaN         NaN    procyon         procyon lotor   \n",
-       "14  didelphidae  didelphinae  didelphini  didelphis  didelphis virginiana   \n",
-       "\n",
-       "   subspecies variety url_dupe  \n",
-       "2         NaN     NaN    False  \n",
-       "3         NaN     NaN    False  \n",
-       "8         NaN     NaN    False  \n",
-       "10        NaN     NaN    False  \n",
-       "14        NaN     NaN    False  \n",
-       "\n",
-       "[5 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_species = dedupe_genusSpecies.loc[dedupe_genusSpecies.species.notna()]\n",
-    "dedupe_species.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Have any of our other stats changed significantly?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 6365985 entries, 2 to 16833847\n",
-      "Data columns (total 13 columns):\n",
-      " #   Column            Non-Null Count    Dtype \n",
-      "---  ------            --------------    ----- \n",
-      " 0   dataset_name      6365985 non-null  object\n",
-      " 1   url               6365985 non-null  object\n",
-      " 2   image_id          6365985 non-null  object\n",
-      " 3   sequence_id       6365985 non-null  object\n",
-      " 4   location_id       6365985 non-null  object\n",
-      " 5   frame_num         6365985 non-null  int64 \n",
-      " 6   original_label    6365985 non-null  object\n",
-      " 7   scientific_name   6365985 non-null  object\n",
-      " 8   common_name       6365985 non-null  object\n",
-      " 9   datetime          3529288 non-null  object\n",
-      " 10  annotation_level  6365985 non-null  object\n",
-      " 11  genus             6365985 non-null  object\n",
-      " 12  species           6365985 non-null  object\n",
-      "dtypes: int64(1), object(12)\n",
-      "memory usage: 680.0+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "dedupe_species[cols].info(show_counts = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dataset_name             18\n",
-       "url                 6365985\n",
-       "image_id            6365985\n",
-       "sequence_id          948156\n",
-       "location_id            6000\n",
-       "frame_num              6065\n",
-       "original_label          874\n",
-       "scientific_name         673\n",
-       "common_name             729\n",
-       "datetime            2245289\n",
-       "annotation_level          3\n",
-       "genus                   452\n",
-       "species                 667\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_species[cols].nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We lost 17 locations, 24 genera, and 86 common names, but all datasets are still represented."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['https://lilablobssc.blob.core.windows.net/caltech-unzipped/cct_images/59b93afb-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "       'https://lilablobssc.blob.core.windows.net/caltech-unzipped/cct_images/59641f56-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "       'https://lilablobssc.blob.core.windows.net/caltech-unzipped/cct_images/5a2176e7-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "       'https://lilablobssc.blob.core.windows.net/caltech-unzipped/cct_images/598de824-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "       'https://lilablobssc.blob.core.windows.net/caltech-unzipped/cct_images/5a1cb0d8-23d2-11e8-a6a3-ec086b02610b.jpg'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#some sample images\n",
-    "dedupe_species['url'].head().values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Save a Species Label CSV\n",
-    "\n",
-    "Dataset with all images that have labels down to the species level. We will get some stats on this to determine how to truncate to just one instance of each animal per sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dedupe_species.to_csv(\"../data/lila_image_urls_and_labels_species.csv\", index = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Species Stats\n",
-    "\n",
-    "Let's get some statistics on this data to help narrow it down:\n",
-    "- Do we have instances of common name matching scientific name? There are more unique instances of `common_name` than `species` or `scientific_name`.\n",
-    "- What is the minimum number of instances of a particular species? We'll balance the datset to have the smallest number available (assuming 20+ images).\n",
-    "- For later evaluation, what's the distribution of time of day (will be more meaningful for the finalized species dataset)?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/1464183993.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  dedupe_species['common_species_match'] = list(dedupe_species.common_name == dedupe_species.species)\n"
-     ]
-    }
-   ],
-   "source": [
-    "dedupe_species['common_species_match'] = list(dedupe_species.common_name == dedupe_species.species)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/2828595366.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  dedupe_species['common_sciName_match'] = list(dedupe_species.common_name == dedupe_species.scientific_name)\n"
-     ]
-    }
-   ],
-   "source": [
-    "dedupe_species['common_sciName_match'] = list(dedupe_species.common_name == dedupe_species.scientific_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "common_species_match\n",
-       "False    6365985\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_species.common_species_match.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "common_sciName_match\n",
-       "False    6365985\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_species.common_sciName_match.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Common name is not filled with scientific name or species values for any of these images.\n",
-    "\n",
-    "Now, let's check the number of each species. Then maybe check scientific name and common name since we have more of these."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'felis catus': 40604,\n",
-       " 'didelphis virginiana': 19810,\n",
-       " 'canis familiaris': 9804,\n",
-       " 'procyon lotor': 49930,\n",
-       " 'canis latrans': 40265,\n",
-       " 'urocyon cinereoargenteus': 19794,\n",
-       " 'lynx rufus': 34954,\n",
-       " 'taxidea taxus': 48,\n",
-       " 'puma concolor': 23495,\n",
-       " 'sus scrofa': 379915,\n",
-       " 'bos taurus': 2062964,\n",
-       " 'sciurus carolinensis': 27465,\n",
-       " 'tamias striatus': 311,\n",
-       " 'marmota monax': 206,\n",
-       " 'meleagris gallopavo': 4791,\n",
-       " 'odocoileus virginianus': 4850,\n",
-       " 'sylvilagus floridanus': 255,\n",
-       " 'homo sapiens': 254612,\n",
-       " 'mephitis mephitis': 11782,\n",
-       " 'vulpes vulpes': 2632,\n",
-       " 'sciurus niger': 340,\n",
-       " 'equus caballus': 1320,\n",
-       " 'corvus brachyrhynchos': 895,\n",
-       " 'gallus gallus': 7993,\n",
-       " 'ursus americanus': 32777,\n",
-       " 'pecari tajacu': 36536,\n",
-       " 'mazama americana': 7974,\n",
-       " 'nasua narica': 3254,\n",
-       " 'rattus praetor': 696,\n",
-       " 'leopardus pardalis': 15847,\n",
-       " 'tamiasciurus hudsonicus': 4647,\n",
-       " 'didelphis marsupialis': 2915,\n",
-       " 'tinamus major': 2794,\n",
-       " 'ovis ammon': 2365,\n",
-       " 'cervus elaphus': 186592,\n",
-       " 'lepus europaeus': 693,\n",
-       " 'apodemus sylvaticus': 1329,\n",
-       " 'dasyprocta coibae': 1354,\n",
-       " 'martes americana': 1433,\n",
-       " 'odocoileus hemionus': 85395,\n",
-       " 'lepus americanus': 13868,\n",
-       " 'erethizon dorsatum': 556,\n",
-       " 'marmota flaviventris': 309,\n",
-       " 'alces alces': 13779,\n",
-       " 'equus ferus': 2732,\n",
-       " 'mustela erminea': 34,\n",
-       " 'perisoreus canadensis': 77,\n",
-       " 'troglodytes aedon': 4,\n",
-       " 'cyanocitta stelleri': 3,\n",
-       " 'dendragapus obscurus': 8,\n",
-       " 'junco hyemalis': 5,\n",
-       " 'cervus canadensis': 156314,\n",
-       " 'otospermophilus beecheyi': 33866,\n",
-       " 'callipepla californica': 2275,\n",
-       " 'lepus californicus': 1150,\n",
-       " 'neogale frenata': 98,\n",
-       " 'meles meles': 157,\n",
-       " 'lontra canadensis': 557,\n",
-       " 'dasypus novemcinctus': 12280,\n",
-       " 'equus africanus': 2880,\n",
-       " 'psophia leucoptera': 8214,\n",
-       " 'tayassu pecari': 99601,\n",
-       " 'dasyprocta punctata': 15862,\n",
-       " 'cuniculus paca': 15529,\n",
-       " 'sciurus spadiceus': 1681,\n",
-       " 'tapirus terrestris': 9761,\n",
-       " 'mitu tuberosum': 12367,\n",
-       " 'geotrygon montana': 649,\n",
-       " 'nasua nasua': 4449,\n",
-       " 'eira barbara': 2879,\n",
-       " 'penelope jacquacu': 4176,\n",
-       " 'alouatta sara': 23,\n",
-       " 'atelocynus microtis': 1900,\n",
-       " 'procyon cancrivorus': 700,\n",
-       " 'aramides cajaneus': 1020,\n",
-       " 'panthera onca': 9438,\n",
-       " 'myrmecophaga tridactyla': 2661,\n",
-       " 'pilherodius pileatus': 16,\n",
-       " 'hydrochoerus hydrochaeris': 587,\n",
-       " 'odontophorus gujanensis': 100,\n",
-       " 'crypturellus cinereus': 111,\n",
-       " 'sylvilagus brasiliensis': 4693,\n",
-       " 'mesembrinibis cayennensis': 93,\n",
-       " 'turdus ignobilis': 1,\n",
-       " 'dasypus kappleri': 587,\n",
-       " 'eurypyga helias': 17,\n",
-       " 'priodontes maximus': 851,\n",
-       " 'tamandua tetradactyla': 1657,\n",
-       " 'agamia agami': 3,\n",
-       " 'tigrisoma lineatum': 25,\n",
-       " 'cochlearius cochlearius': 7,\n",
-       " 'crypturellus atrocapillus': 1,\n",
-       " 'crypturellus undulatus': 1,\n",
-       " 'herpailurus yagouaroundi': 249,\n",
-       " 'speothos venaticus': 34,\n",
-       " 'lontra longicaudis': 8,\n",
-       " 'neomorphus geoffroyi': 12,\n",
-       " 'nyctidromus albicollis': 1,\n",
-       " 'harpia harpyja': 1,\n",
-       " 'leopardus wiedii': 1001,\n",
-       " 'buteogallus urubitinga': 876,\n",
-       " 'cairina moschata': 37,\n",
-       " 'tinamus guttatus': 51,\n",
-       " 'formicarius analis': 187,\n",
-       " 'ardea alba': 3,\n",
-       " 'oressochen jubatus': 110,\n",
-       " 'molothrus oryzivorus': 2,\n",
-       " 'coragyps atratus': 835,\n",
-       " 'crypturellus erythropus': 2,\n",
-       " 'anhima cornuta': 33,\n",
-       " 'vanellus cayanus': 11,\n",
-       " 'mazama gouazoubira': 823,\n",
-       " 'daptrius ater': 53,\n",
-       " 'crypturellus bartletti': 4,\n",
-       " 'blastocerus dichotomus': 2,\n",
-       " 'philander opossum': 194,\n",
-       " 'lutreolina crassicaudata': 2,\n",
-       " 'tupinambis teguixin': 129,\n",
-       " 'capra aegagrus': 4414,\n",
-       " 'ovis aries': 1338,\n",
-       " 'canis lupus': 5341,\n",
-       " 'lepus saxatilis': 1474,\n",
-       " 'nesotragus moschatus': 1136,\n",
-       " 'turtur chalcospilos': 25,\n",
-       " 'papio anubis': 3865,\n",
-       " 'genetta genetta': 104,\n",
-       " 'sylvicapra grimmia': 1816,\n",
-       " 'tragelaphus scriptus': 1809,\n",
-       " 'turdus tephronotus': 1,\n",
-       " 'cercopithecus erythrogaster': 215,\n",
-       " 'thryonomys gregorianus': 4,\n",
-       " 'paraxerus ochraceus': 15,\n",
-       " 'herpestes sanguineus': 60,\n",
-       " 'loxodonta africana': 66941,\n",
-       " 'cricetomys gambianus': 3755,\n",
-       " 'raphicerus campestris': 1137,\n",
-       " 'hyaena hyaena': 1070,\n",
-       " 'bubulcus ibis': 117,\n",
-       " 'aepyceros melampus': 67099,\n",
-       " 'crocuta crocuta': 24731,\n",
-       " 'caracal caracal': 337,\n",
-       " 'panthera leo': 21258,\n",
-       " 'tragelaphus oryx': 19982,\n",
-       " 'kobus ellipsiprymnus': 2127,\n",
-       " 'phacochoerus africanus': 43716,\n",
-       " 'panthera pardus': 1161,\n",
-       " 'lamprotornis superbus': 88,\n",
-       " 'ichneumia albicauda': 176,\n",
-       " 'lanius collaris': 5,\n",
-       " 'lupulella mesomelas': 3974,\n",
-       " 'euxerus erythropus': 93,\n",
-       " 'syncerus caffer': 66268,\n",
-       " 'equus quagga': 314207,\n",
-       " 'giraffa camelopardalis': 51727,\n",
-       " 'alcelaphus buselaphus': 57998,\n",
-       " 'chlorocebus pygerythrus': 3616,\n",
-       " 'madoqua guentheri': 15762,\n",
-       " 'potamochoerus larvatus': 1973,\n",
-       " 'numida meleagris': 232,\n",
-       " 'nanger granti': 46600,\n",
-       " 'eudorcas thomsonii': 313244,\n",
-       " 'struthio camelus': 6417,\n",
-       " 'orycteropus afer': 1284,\n",
-       " 'acinonyx jubatus': 6834,\n",
-       " 'eupodotis senegalensis': 283,\n",
-       " 'felis silvestris': 30,\n",
-       " 'pternistis leucoscepus': 180,\n",
-       " 'stigmochelys pardalis': 11,\n",
-       " 'oryx beisa': 1105,\n",
-       " 'litocranius walleri': 790,\n",
-       " 'eupodotis gindiana': 53,\n",
-       " 'ardeotis kori': 4088,\n",
-       " 'pipile cumanensis': 36,\n",
-       " 'helogale parvula': 42,\n",
-       " 'lissotis melanogaster': 151,\n",
-       " 'ortygornis sephaena': 15,\n",
-       " 'trichys fasciculata': 4,\n",
-       " 'macaca nemestrina': 46304,\n",
-       " 'hydrornis guajanus': 8,\n",
-       " 'argusianus argus': 2333,\n",
-       " 'paradoxurus hermaphroditus': 14774,\n",
-       " 'prionailurus bengalensis': 512,\n",
-       " 'hemigalus derbyanus': 90,\n",
-       " 'muntiacus muntjak': 9855,\n",
-       " 'tragulus javanicus': 167,\n",
-       " 'helarctos malayanus': 941,\n",
-       " 'butorides striata': 11,\n",
-       " 'elephas maximus': 325,\n",
-       " 'rusa unicolor': 75076,\n",
-       " 'tapirus indicus': 285,\n",
-       " 'hystrix brachyura': 9485,\n",
-       " 'catopuma temminckii': 353,\n",
-       " 'panthera tigris': 321,\n",
-       " 'sus barbatus': 60,\n",
-       " 'lariscus insignis': 43,\n",
-       " 'tupaia glis': 13,\n",
-       " 'enicurus leschenaulti': 147,\n",
-       " 'chalcophaps indica': 295,\n",
-       " 'tragulus napu': 218,\n",
-       " 'genetta tigrina': 98,\n",
-       " 'hystrix cristata': 959,\n",
-       " 'lycaon pictus': 151,\n",
-       " 'oreotragus oreotragus': 97,\n",
-       " 'tragelaphus imberbis': 26,\n",
-       " 'procavia capensis': 65,\n",
-       " 'streptopelia senegalensis': 36,\n",
-       " 'heterohyrax brucei': 41,\n",
-       " 'mellivora capensis': 216,\n",
-       " 'ictonyx striatus': 102,\n",
-       " 'pternistis hildebrandti': 10,\n",
-       " 'tockus flavirostris': 1,\n",
-       " 'leopardus tigrinus': 37,\n",
-       " 'potos flavus': 1,\n",
-       " 'alectoris rufa': 2105,\n",
-       " 'leptotila rufaxilla': 419,\n",
-       " 'ardea cocoi': 24,\n",
-       " 'penelope superciliaris': 114,\n",
-       " 'pteroglossus beauharnaisii': 10,\n",
-       " 'sapajus apella': 419,\n",
-       " 'chelonoidis carbonarius': 11,\n",
-       " 'ramphastos tucanus': 256,\n",
-       " 'ateles chamek': 59,\n",
-       " 'momotus momota': 428,\n",
-       " 'rupornis magnirostris': 21,\n",
-       " 'cathartes burrovianus': 60,\n",
-       " 'amazona oratrix': 20,\n",
-       " 'galictis vittata': 50,\n",
-       " 'saimiri boliviensis': 32,\n",
-       " 'sciurus ignitus': 14,\n",
-       " 'pteronura brasiliensis': 19,\n",
-       " 'leptotila verreauxi': 245,\n",
-       " 'crypturellus soui': 174,\n",
-       " 'egretta thula': 10,\n",
-       " 'monasa morphoeus': 10,\n",
-       " 'tinamus tao': 10,\n",
-       " 'cebus albifrons': 32,\n",
-       " 'cathartes melambrotus': 20,\n",
-       " 'formicarius colma': 5,\n",
-       " 'spizaetus ornatus': 10,\n",
-       " 'ciconia maguari': 2,\n",
-       " 'ortalis guttata': 50,\n",
-       " 'morphnus guianensis': 154,\n",
-       " 'psophia crepitans': 2034,\n",
-       " 'nothocrax urumutum': 23,\n",
-       " 'dasyprocta fuliginosa': 14237,\n",
-       " 'sciurus igniventris': 283,\n",
-       " 'mitu salvini': 1531,\n",
-       " 'myoprocta pratti': 669,\n",
-       " 'coendou bicolor': 11,\n",
-       " 'microsciurus flaviventer': 1,\n",
-       " 'buteogallus solitarius': 1,\n",
-       " 'tamandua mexicana': 101,\n",
-       " 'microsciurus mimulus': 1,\n",
-       " 'penelope purpurascens': 758,\n",
-       " 'sciurus granatensis': 4,\n",
-       " 'odontophorus erythrops': 2,\n",
-       " 'geotrygon saphirina': 1,\n",
-       " 'cabassous centralis': 1,\n",
-       " 'cabassous unicinctus': 6,\n",
-       " 'streptopelia capicola': 55,\n",
-       " 'camelus dromedarius': 1214,\n",
-       " 'otocyon megalotis': 1276,\n",
-       " 'acryllium vulturinum': 1831,\n",
-       " 'equus grevyi': 3042,\n",
-       " 'diceros bicornis': 281,\n",
-       " 'ceratotherium simum': 84,\n",
-       " 'proteles cristatus': 937,\n",
-       " 'balearica regulorum': 3,\n",
-       " 'sagittarius serpentarius': 5194,\n",
-       " 'leptailurus serval': 2582,\n",
-       " 'melaenornis pammelaina': 4,\n",
-       " 'tragelaphus strepsiceros': 5145,\n",
-       " 'hippopotamus amphibius': 6048,\n",
-       " 'corythaixoides leucogaster': 6,\n",
-       " 'melierax poliopterus': 12,\n",
-       " 'burhinus capensis': 7,\n",
-       " 'lissotis hartlaubii': 3,\n",
-       " 'erythrocebus patas': 12,\n",
-       " 'ardea melanocephala': 16,\n",
-       " 'vanellus coronatus': 47,\n",
-       " 'tockus deckeni': 15,\n",
-       " 'atelerix albiventris': 3,\n",
-       " 'galago senegalensis': 2,\n",
-       " 'eudorcas rufifrons': 3,\n",
-       " 'xerus rutilus': 37,\n",
-       " 'laniarius funebris': 1,\n",
-       " 'lamprotornis chalybaeus': 15,\n",
-       " 'connochaetes taurinus': 480765,\n",
-       " 'bostrychia hagedash': 8,\n",
-       " 'dicerorhinus sumatrensis': 54,\n",
-       " 'paguma larvata': 13386,\n",
-       " 'pardofelis marmorata': 271,\n",
-       " 'neofelis nebulosa': 204,\n",
-       " 'cuon alpinus': 185,\n",
-       " 'varanus salvator': 17,\n",
-       " 'arctogalidia trivirgata': 9,\n",
-       " 'martes flavigula': 17174,\n",
-       " 'prionodon linsang': 110,\n",
-       " 'rollulus rouloul': 55,\n",
-       " 'lophura inornata': 56,\n",
-       " 'mydaus javanensis': 21,\n",
-       " 'lophura ignita': 4,\n",
-       " 'nesolagus netscheri': 2,\n",
-       " 'polyplectron chalcurum': 13,\n",
-       " 'manis javanica': 17,\n",
-       " 'hydrornis schneideri': 6,\n",
-       " 'capricornis sumatraensis': 33651,\n",
-       " 'macaca fascicularis': 727,\n",
-       " 'presbytis femoralis': 1,\n",
-       " 'baryphthengus martii': 1,\n",
-       " 'pternistis nobilis': 2020,\n",
-       " 'allochrocebus lhoesti': 3077,\n",
-       " 'cephalophus nigrifrons': 13490,\n",
-       " 'atherurus africanus': 1579,\n",
-       " 'pan troglodytes': 444,\n",
-       " 'cercopithecus mitis': 387,\n",
-       " 'funisciurus carruthersi': 1397,\n",
-       " 'motacilla flava': 15,\n",
-       " 'eurillas latirostris': 6,\n",
-       " 'eurillas virens': 6,\n",
-       " 'thamnomys venustus': 9,\n",
-       " 'protoxerus stangeri': 93,\n",
-       " 'paraxerus boehmi': 79,\n",
-       " 'cephalophus silvicultor': 859,\n",
-       " 'melaenornis ardesiacus': 3,\n",
-       " 'oenomys hypoxanthus': 138,\n",
-       " 'cyanomitra cyanolaema': 3,\n",
-       " 'delacourella capistrata': 9,\n",
-       " 'melocichla mentalis': 3,\n",
-       " 'hybomys univittatus': 78,\n",
-       " 'colomys goslingi': 15,\n",
-       " 'hylomyscus stella': 12,\n",
-       " 'genetta servalina': 167,\n",
-       " 'lupulella adusta': 59,\n",
-       " 'melaenornis fischeri': 9,\n",
-       " 'mus minutoides': 63,\n",
-       " 'stelgidillas gracilirostris': 3,\n",
-       " 'musophaga rossae': 9,\n",
-       " 'acrocephalus baeticatus': 6,\n",
-       " 'turtur tympanistria': 167,\n",
-       " 'praomys tullbergi': 9,\n",
-       " 'malacomys longipes': 15,\n",
-       " 'eurocephalus ruppelli': 3,\n",
-       " 'alopochen aegyptiaca': 11,\n",
-       " 'deomys ferrugineus': 9,\n",
-       " 'scleroptila afra': 32,\n",
-       " 'nandinia binotata': 48,\n",
-       " 'chloropicus griseocephalus': 3,\n",
-       " 'turdus olivaceus': 12,\n",
-       " 'streptopelia lugens': 33,\n",
-       " 'cossypha archeri': 18,\n",
-       " 'atilax paludinosus': 15,\n",
-       " 'colobus angolensis': 114,\n",
-       " 'neocossyphus rufus': 6,\n",
-       " 'heliosciurus rufobrachium': 3,\n",
-       " 'heliosciurus ruwenzorii': 3,\n",
-       " 'urosphena neumanni': 6,\n",
-       " 'dicrurus adsimilis': 1,\n",
-       " 'lamprotornis hildebrandti': 7,\n",
-       " 'aquila rapax': 21,\n",
-       " 'meleagris ocellata': 26493,\n",
-       " 'crax rubra': 18606,\n",
-       " 'tapirus bairdii': 4394,\n",
-       " 'leptotila plumbeiceps': 1541,\n",
-       " 'mazama temama': 5205,\n",
-       " 'conepatus semistriatus': 168,\n",
-       " 'odocoileus pandora': 1164,\n",
-       " 'ortalis vetula': 169,\n",
-       " 'sciurus deppei': 17,\n",
-       " 'buteo ridgwayi': 8,\n",
-       " 'presbytis thomasi': 99,\n",
-       " 'neofelis diardi': 76,\n",
-       " 'arctonyx hoevenii': 36,\n",
-       " 'alophoixus bres': 1,\n",
-       " 'dicaeum trigonostigma': 1,\n",
-       " 'otus spilocephalus': 15,\n",
-       " 'cyanoptila cyanomelana': 3,\n",
-       " 'ficedula mugimaki': 1,\n",
-       " 'dendrocitta occipitalis': 31,\n",
-       " 'rattus tiomanicus': 7,\n",
-       " 'caloperdix oculeus': 1,\n",
-       " 'tragulus kanchil': 59,\n",
-       " 'arctictis binturong': 82,\n",
-       " 'niltava sumatrana': 2,\n",
-       " 'mustela lutreolina': 2,\n",
-       " 'leiothrix argentauris': 6,\n",
-       " 'myophonus melanurus': 25,\n",
-       " 'arborophila rubrirostris': 11,\n",
-       " 'sundasciurus hippurus': 2,\n",
-       " 'larvivora cyane': 3,\n",
-       " 'myophonus glaucinus': 31,\n",
-       " 'lophura erythrophthalma': 3,\n",
-       " 'spilornis cheela': 194,\n",
-       " 'myophonus caeruleus': 2960,\n",
-       " 'urva semitorquata': 3,\n",
-       " 'collocalia linchi': 3,\n",
-       " 'callosciurus notatus': 1,\n",
-       " 'cavia aperea': 2,\n",
-       " 'cerdocyon thous': 349,\n",
-       " 'caracara plancus': 1,\n",
-       " 'columbina talpacoti': 2,\n",
-       " 'tolypeutes matacus': 1,\n",
-       " 'tyto alba': 1,\n",
-       " 'euphractus sexcinctus': 1,\n",
-       " 'rhea americana': 2,\n",
-       " 'cercocebus sanjei': 84,\n",
-       " 'guttera pucherani': 61,\n",
-       " 'cephalophus harveyi': 786,\n",
-       " 'bdeogale crassicauda': 440,\n",
-       " 'bdeogale jacksoni': 33,\n",
-       " 'mungos mungo': 7,\n",
-       " 'hystrix africaeaustralis': 184,\n",
-       " 'papio cynocephalus': 21,\n",
-       " 'cephalophus spadix': 25,\n",
-       " 'paraxerus vexillarius': 141,\n",
-       " 'paraxerus cepapi': 1,\n",
-       " 'genetta maculata': 15,\n",
-       " 'petrodromus tetradactylus': 118,\n",
-       " 'rhynchocyon cirnei': 91,\n",
-       " 'civettictis civetta': 15,\n",
-       " 'dendrohyrax arboreus': 17,\n",
-       " 'rhynchocyon udzungwensis': 134,\n",
-       " 'philantomba monticola': 77,\n",
-       " 'pogonocichla stellata': 1,\n",
-       " 'xenoperdix udzungwensis': 11,\n",
-       " 'sheppardia lowei': 1,\n",
-       " 'columba larvata': 184,\n",
-       " 'geokichla gurneyi': 35,\n",
-       " 'piliocolobus gordonorum': 2,\n",
-       " 'accipiter melanoleucus': 1,\n",
-       " 'tauraco livingstonii': 1,\n",
-       " 'stephanoaetus coronatus': 1,\n",
-       " 'rhynchocyon petersi': 27,\n",
-       " 'otolemur garnettii': 5,\n",
-       " 'bycanistes brevis': 1,\n",
-       " 'columba arquatrix': 1,\n",
-       " 'paraxerus lucifer': 6,\n",
-       " 'mazama chunyi': 760,\n",
-       " 'cuniculus taczanowskii': 1504,\n",
-       " 'didelphis pernigra': 10383,\n",
-       " 'tremarctos ornatus': 462,\n",
-       " 'macaca arctoides': 74636,\n",
-       " 'arctonyx collaris': 761,\n",
-       " 'ursus thibetanus': 1221,\n",
-       " 'lophura nycthemera': 16626,\n",
-       " 'muntiacus rooseveltorum': 60143,\n",
-       " 'zoothera dauma': 742,\n",
-       " 'atherurus macrourus': 13331,\n",
-       " 'polyplectron bicalcaratum': 7936,\n",
-       " 'viverra zibetha': 1570,\n",
-       " 'chrotogale owstoni': 216,\n",
-       " 'prionodon pardicolor': 3587,\n",
-       " 'dremomys rufigenis': 40465,\n",
-       " 'aonyx cinereus': 1,\n",
-       " 'macaca assamensis': 21,\n",
-       " 'arborophila brunneopectus': 4,\n",
-       " 'urva urva': 17535,\n",
-       " 'viverricula indica': 88,\n",
-       " 'bambusicola fytchii': 3,\n",
-       " 'bos gaurus': 3,\n",
-       " 'rhizomys sumatrensis': 1,\n",
-       " 'ardeola grayii': 1,\n",
-       " 'scolopax rusticola': 29,\n",
-       " 'arborophila rufogularis': 17,\n",
-       " 'hydrornis oatesi': 8,\n",
-       " 'hylopetes alboniger': 1,\n",
-       " 'circus cyaneus': 1,\n",
-       " 'jynx torquilla': 8,\n",
-       " 'felis chaus': 3,\n",
-       " 'hydrornis cyaneus': 113,\n",
-       " 'macaca mulatta': 3,\n",
-       " 'lophura diardi': 42,\n",
-       " 'mustela strigidorsa': 15,\n",
-       " 'nisaetus nipalensis': 6,\n",
-       " 'cissa chinensis': 2,\n",
-       " 'melogale personata': 1,\n",
-       " 'nisaetus nanus': 2,\n",
-       " 'callosciurus erythraeus': 5837,\n",
-       " 'trochalopteron milnei': 18,\n",
-       " 'arundinax aedon': 1,\n",
-       " 'ficedula tricolor': 1,\n",
-       " 'butastur indicus': 2,\n",
-       " 'aquila heliaca': 2,\n",
-       " 'ratufa bicolor': 233,\n",
-       " 'cyornis banyumas': 3,\n",
-       " 'erythrogenys mcclellandi': 1,\n",
-       " 'pernis ptilorhynchus': 2,\n",
-       " 'copsychus malabaricus': 3,\n",
-       " 'copsychus saularis': 1,\n",
-       " 'geokichla citrina': 313,\n",
-       " 'mustela kathiah': 16,\n",
-       " 'enicurus schistaceus': 3,\n",
-       " 'lagidium viscacia': 336,\n",
-       " 'grallaria andicolus': 116,\n",
-       " 'hippocamelus antisensis': 115,\n",
-       " 'lycalopex culpaeus': 1391,\n",
-       " 'conepatus chinga': 413,\n",
-       " 'vicugna pacos': 1232,\n",
-       " 'cinclodes fuscus': 53,\n",
-       " 'zonotrichia capensis': 3,\n",
-       " 'cinclodes atacamensis': 21,\n",
-       " 'odontophorus balliviani': 30,\n",
-       " 'carpococcyx renauldi': 1,\n",
-       " 'tupaia belangeri': 4780,\n",
-       " 'melogale everetti': 22,\n",
-       " 'muntiacus vuquangensis': 119784,\n",
-       " 'tigrisoma mexicanum': 20,\n",
-       " 'claravis pretiosa': 160,\n",
-       " 'aramus guarauna': 40,\n",
-       " 'fossa fossana': 1101,\n",
-       " 'setifer setosus': 247,\n",
-       " 'galidictis fasciata': 127,\n",
-       " 'eupleres goudotii': 246,\n",
-       " 'mentocrex kioloides': 600,\n",
-       " 'rattus rattus': 3315,\n",
-       " 'tenrec ecaudatus': 97,\n",
-       " 'copsychus albospecularis': 740,\n",
-       " 'galidia elegans': 354,\n",
-       " 'mystacornis crossleyi': 4,\n",
-       " 'lophotibis cristata': 1410,\n",
-       " 'cryptoprocta ferox': 729,\n",
-       " 'coua serriana': 4501,\n",
-       " 'propithecus candidus': 6,\n",
-       " 'coua ruficeps': 78,\n",
-       " 'streptopelia picturata': 792,\n",
-       " 'brachypteracias squamiger': 1736,\n",
-       " 'salanoia concolor': 82,\n",
-       " 'asio madagascariensis': 3,\n",
-       " 'suncus murinus': 50,\n",
-       " 'atelornis pittoides': 8,\n",
-       " 'hemicentetes semispinosus': 23,\n",
-       " 'eutriorchis astur': 6,\n",
-       " 'haliaeetus vociferoides': 2,\n",
-       " 'oxylabes madagascariensis': 14,\n",
-       " 'daubentonia madagascariensis': 5,\n",
-       " 'coturnix delegorguei': 3,\n",
-       " 'eliurus petteri': 98,\n",
-       " 'xanthomixis apperti': 3,\n",
-       " 'terpsiphone mutata': 3,\n",
-       " 'hypogeomys antimena': 554,\n",
-       " 'eliurus penicillatus': 161,\n",
-       " 'coua caerulea': 45,\n",
-       " 'nesomys audeberti': 3,\n",
-       " 'eliurus webbi': 1,\n",
-       " 'saxicola tectes': 9,\n",
-       " 'accipiter madagascariensis': 9,\n",
-       " 'cisticola cherina': 122,\n",
-       " 'eulemur albifrons': 3,\n",
-       " 'euryceros prevostii': 15,\n",
-       " 'motacilla flaviventris': 3,\n",
-       " 'microcebus murinus': 5,\n",
-       " 'crypturellus boucardi': 13,\n",
-       " 'arremonops chloronotus': 1,\n",
-       " 'crypturellus cinnamomeus': 1,\n",
-       " 'spilogale putorius': 2,\n",
-       " 'caprimulgus europaeus': 1,\n",
-       " 'nyctanassa violacea': 114,\n",
-       " 'hylocichla mustelina': 9,\n",
-       " 'dasyprocta leporina': 5245,\n",
-       " 'crax alector': 1427,\n",
-       " 'didelphis imperfecta': 102,\n",
-       " 'metachirus nudicaudatus': 23,\n",
-       " 'catharus ustulatus': 10,\n",
-       " 'mitu tomentosum': 189,\n",
-       " 'crypturellus variegatus': 62,\n",
-       " 'neomorphus rufipennis': 5,\n",
-       " 'pipile pipile': 2,\n",
-       " 'funisciurus pyrropus': 119,\n",
-       " 'chamaetylas poliophrys': 6,\n",
-       " 'ruwenzorornis johnstoni': 6,\n",
-       " 'thryonomys swinderianus': 6,\n",
-       " 'ploceus alienus': 3,\n",
-       " 'ploceus baglafecht': 3,\n",
-       " 'poecilogale albinucha': 2,\n",
-       " 'anomalurus derbianus': 6,\n",
-       " 'erinaceus europaeus': 10706,\n",
-       " 'capra hircus': 3151,\n",
-       " 'rattus norvegicus': 861,\n",
-       " 'equus asinus': 697,\n",
-       " 'corvus corax': 883,\n",
-       " 'zenaida asiatica': 211,\n",
-       " 'athene cunicularia': 6,\n",
-       " 'strix varia': 1,\n",
-       " 'butorides virescens': 6,\n",
-       " 'falco sparverius': 9,\n",
-       " 'ardea herodias': 9,\n",
-       " 'falco tinnunculus': 7,\n",
-       " 'hydrobates pelagicus': 12,\n",
-       " 'asio flammeus': 278,\n",
-       " 'anous stolidus': 106,\n",
-       " 'caloenas nicobarica': 10,\n",
-       " 'calcinus tubularis': 39,\n",
-       " 'urocyon littoralis': 45115,\n",
-       " 'antilocapra americana': 621,\n",
-       " 'ovis canadensis': 90,\n",
-       " 'pica hudsonia': 6,\n",
-       " 'ixoreus naevius': 6,\n",
-       " 'sitta canadensis': 6,\n",
-       " 'piranga ludoviciana': 3,\n",
-       " 'pekania pennanti': 45,\n",
-       " 'nucifraga columbiana': 5,\n",
-       " 'damaliscus lunatus': 11325,\n",
-       " 'felis lybica': 204,\n",
-       " 'parahyaena brunnea': 47,\n",
-       " 'equus zebra': 3091,\n",
-       " 'oryx gazella': 3972,\n",
-       " 'antidorcas marsupialis': 2740,\n",
-       " 'pelea capreolus': 88,\n",
-       " 'redunca fulvorufula': 207,\n",
-       " 'vulpes chama': 21,\n",
-       " 'bunolagus monticularis': 8,\n",
-       " 'neotis ludwigii': 36,\n",
-       " 'herpestes pulverulentus': 3,\n",
-       " 'suricata suricatta': 45,\n",
-       " 'cynictis penicillata': 23,\n",
-       " 'lepus capensis': 1,\n",
-       " 'lepus victoriae': 12,\n",
-       " 'ourebia ourebi': 2,\n",
-       " 'connochaetes gnou': 667,\n",
-       " 'damaliscus pygargus': 1123,\n",
-       " 'anthropoides paradiseus': 16,\n",
-       " 'pedetes capensis': 17,\n",
-       " 'dama dama': 29,\n",
-       " 'herpestes ichneumon': 15,\n",
-       " 'pronolagus randensis': 8,\n",
-       " 'tragelaphus angasii': 30,\n",
-       " 'cephalophus natalensis': 2,\n",
-       " 'hippotragus equinus': 1,\n",
-       " 'crocodylus niloticus': 1,\n",
-       " 'urocissa whiteheadi': 24,\n",
-       " 'turdus cardis': 85,\n",
-       " 'garrulax maesi': 12,\n",
-       " 'picus rabieri': 18,\n",
-       " 'erythrogenys hypoleucos': 25,\n",
-       " 'garrulax castanotis': 479,\n",
-       " 'pygathrix nemaeus': 1229,\n",
-       " 'nesolagus timminsi': 4943,\n",
-       " 'cinclidium frontale': 21,\n",
-       " 'harpactes oreskios': 6,\n",
-       " 'moschiola meminna': 3918,\n",
-       " 'hydrornis elliotii': 44,\n",
-       " 'cissa hypoleuca': 3,\n",
-       " 'trachypithecus francoisi': 2,\n",
-       " 'rheinardia ocellata': 446,\n",
-       " 'varanus bengalensis': 11,\n",
-       " 'trachypithecus hatinhensis': 26,\n",
-       " 'trachypithecus phayrei': 105,\n",
-       " 'aceros nipalensis': 6,\n",
-       " 'strix leptogrammica': 415,\n",
-       " 'pterorhinus pectoralis': 442,\n",
-       " 'ducula badia': 34,\n",
-       " 'ianthocincla konkakinhensis': 4,\n",
-       " 'picus chlorolophus': 9,\n",
-       " 'geokichla sibirica': 283,\n",
-       " 'irena puella': 2,\n",
-       " 'pterorhinus chinensis': 15,\n",
-       " 'cyanoderma chrysaeum': 14,\n",
-       " 'macropygia unchall': 10,\n",
-       " 'myiomela leucura': 9,\n",
-       " 'turdus obscurus': 106,\n",
-       " 'culicicapa ceylonensis': 14,\n",
-       " 'plecturocebus ornatus': 49,\n",
-       " 'iguana iguana': 22,\n",
-       " 'dasyprocta guamara': 3,\n",
-       " 'cathartes aura': 4,\n",
-       " 'penelope jacucaca': 4,\n",
-       " 'tigrisoma fasciatum': 6}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_species_dict = {}\n",
-    "for species in dedupe_species.species.unique():\n",
-    "    num_species = len(dedupe_species.loc[dedupe_species.species == species])\n",
-    "    num_species_dict[species] = num_species\n",
-    "num_species_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/2756880910.py:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  dedupe_species.loc[dedupe_species.species == species, 'num_species'] = num\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Add this info to the dataset\n",
-    "for species, num in num_species_dict.items():\n",
-    "    dedupe_species.loc[dedupe_species.species == species, 'num_species'] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'felis catus': 40604,\n",
-       " 'didelphis virginiana': 19810,\n",
-       " 'canis familiaris': 9804,\n",
-       " 'procyon lotor': 49930,\n",
-       " 'canis latrans': 40265,\n",
-       " 'urocyon cinereoargenteus': 19794,\n",
-       " 'lynx rufus': 34954,\n",
-       " 'taxidea taxus': 48,\n",
-       " 'puma concolor': 23495,\n",
-       " 'sus scrofa': 379915,\n",
-       " 'bos taurus': 2062964,\n",
-       " 'sciurus carolinensis': 27465,\n",
-       " 'tamias striatus': 311,\n",
-       " 'marmota monax': 206,\n",
-       " 'meleagris gallopavo': 4791,\n",
-       " 'odocoileus virginianus': 4850,\n",
-       " 'sylvilagus floridanus': 255,\n",
-       " 'homo sapiens': 254612,\n",
-       " 'mephitis mephitis': 11782,\n",
-       " 'vulpes vulpes': 2632,\n",
-       " 'sciurus niger': 340,\n",
-       " 'equus caballus': 1320,\n",
-       " 'corvus brachyrhynchos': 895,\n",
-       " 'gallus gallus': 7993,\n",
-       " 'ursus americanus': 32777,\n",
-       " 'pecari tajacu': 36536,\n",
-       " 'mazama americana': 7974,\n",
-       " 'nasua narica': 3254,\n",
-       " 'rattus praetor': 696,\n",
-       " 'leopardus pardalis': 15847,\n",
-       " 'tamiasciurus hudsonicus': 4647,\n",
-       " 'didelphis marsupialis': 2915,\n",
-       " 'tinamus major': 2794,\n",
-       " 'ovis ammon': 2365,\n",
-       " 'cervus elaphus': 186592,\n",
-       " 'lepus europaeus': 693,\n",
-       " 'apodemus sylvaticus': 1329,\n",
-       " 'dasyprocta coibae': 1354,\n",
-       " 'martes americana': 1433,\n",
-       " 'odocoileus hemionus': 85395,\n",
-       " 'lepus americanus': 13868,\n",
-       " 'erethizon dorsatum': 556,\n",
-       " 'marmota flaviventris': 309,\n",
-       " 'alces alces': 13779,\n",
-       " 'equus ferus': 2732,\n",
-       " 'perisoreus canadensis': 77,\n",
-       " 'cervus canadensis': 156314,\n",
-       " 'otospermophilus beecheyi': 33866,\n",
-       " 'callipepla californica': 2275,\n",
-       " 'lepus californicus': 1150,\n",
-       " 'neogale frenata': 98,\n",
-       " 'meles meles': 157,\n",
-       " 'lontra canadensis': 557,\n",
-       " 'dasypus novemcinctus': 12280,\n",
-       " 'equus africanus': 2880,\n",
-       " 'psophia leucoptera': 8214,\n",
-       " 'tayassu pecari': 99601,\n",
-       " 'dasyprocta punctata': 15862,\n",
-       " 'cuniculus paca': 15529,\n",
-       " 'sciurus spadiceus': 1681,\n",
-       " 'tapirus terrestris': 9761,\n",
-       " 'mitu tuberosum': 12367,\n",
-       " 'geotrygon montana': 649,\n",
-       " 'nasua nasua': 4449,\n",
-       " 'eira barbara': 2879,\n",
-       " 'penelope jacquacu': 4176,\n",
-       " 'atelocynus microtis': 1900,\n",
-       " 'procyon cancrivorus': 700,\n",
-       " 'aramides cajaneus': 1020,\n",
-       " 'panthera onca': 9438,\n",
-       " 'myrmecophaga tridactyla': 2661,\n",
-       " 'hydrochoerus hydrochaeris': 587,\n",
-       " 'odontophorus gujanensis': 100,\n",
-       " 'crypturellus cinereus': 111,\n",
-       " 'sylvilagus brasiliensis': 4693,\n",
-       " 'mesembrinibis cayennensis': 93,\n",
-       " 'dasypus kappleri': 587,\n",
-       " 'priodontes maximus': 851,\n",
-       " 'tamandua tetradactyla': 1657,\n",
-       " 'herpailurus yagouaroundi': 249,\n",
-       " 'leopardus wiedii': 1001,\n",
-       " 'buteogallus urubitinga': 876,\n",
-       " 'tinamus guttatus': 51,\n",
-       " 'formicarius analis': 187,\n",
-       " 'oressochen jubatus': 110,\n",
-       " 'coragyps atratus': 835,\n",
-       " 'mazama gouazoubira': 823,\n",
-       " 'daptrius ater': 53,\n",
-       " 'philander opossum': 194,\n",
-       " 'tupinambis teguixin': 129,\n",
-       " 'capra aegagrus': 4414,\n",
-       " 'ovis aries': 1338,\n",
-       " 'canis lupus': 5341,\n",
-       " 'lepus saxatilis': 1474,\n",
-       " 'nesotragus moschatus': 1136,\n",
-       " 'papio anubis': 3865,\n",
-       " 'genetta genetta': 104,\n",
-       " 'sylvicapra grimmia': 1816,\n",
-       " 'tragelaphus scriptus': 1809,\n",
-       " 'cercopithecus erythrogaster': 215,\n",
-       " 'herpestes sanguineus': 60,\n",
-       " 'loxodonta africana': 66941,\n",
-       " 'cricetomys gambianus': 3755,\n",
-       " 'raphicerus campestris': 1137,\n",
-       " 'hyaena hyaena': 1070,\n",
-       " 'bubulcus ibis': 117,\n",
-       " 'aepyceros melampus': 67099,\n",
-       " 'crocuta crocuta': 24731,\n",
-       " 'caracal caracal': 337,\n",
-       " 'panthera leo': 21258,\n",
-       " 'tragelaphus oryx': 19982,\n",
-       " 'kobus ellipsiprymnus': 2127,\n",
-       " 'phacochoerus africanus': 43716,\n",
-       " 'panthera pardus': 1161,\n",
-       " 'lamprotornis superbus': 88,\n",
-       " 'ichneumia albicauda': 176,\n",
-       " 'lupulella mesomelas': 3974,\n",
-       " 'euxerus erythropus': 93,\n",
-       " 'syncerus caffer': 66268,\n",
-       " 'equus quagga': 314207,\n",
-       " 'giraffa camelopardalis': 51727,\n",
-       " 'alcelaphus buselaphus': 57998,\n",
-       " 'chlorocebus pygerythrus': 3616,\n",
-       " 'madoqua guentheri': 15762,\n",
-       " 'potamochoerus larvatus': 1973,\n",
-       " 'numida meleagris': 232,\n",
-       " 'nanger granti': 46600,\n",
-       " 'eudorcas thomsonii': 313244,\n",
-       " 'struthio camelus': 6417,\n",
-       " 'orycteropus afer': 1284,\n",
-       " 'acinonyx jubatus': 6834,\n",
-       " 'eupodotis senegalensis': 283,\n",
-       " 'pternistis leucoscepus': 180,\n",
-       " 'oryx beisa': 1105,\n",
-       " 'litocranius walleri': 790,\n",
-       " 'eupodotis gindiana': 53,\n",
-       " 'ardeotis kori': 4088,\n",
-       " 'helogale parvula': 42,\n",
-       " 'lissotis melanogaster': 151,\n",
-       " 'macaca nemestrina': 46304,\n",
-       " 'argusianus argus': 2333,\n",
-       " 'paradoxurus hermaphroditus': 14774,\n",
-       " 'prionailurus bengalensis': 512,\n",
-       " 'hemigalus derbyanus': 90,\n",
-       " 'muntiacus muntjak': 9855,\n",
-       " 'tragulus javanicus': 167,\n",
-       " 'helarctos malayanus': 941,\n",
-       " 'elephas maximus': 325,\n",
-       " 'rusa unicolor': 75076,\n",
-       " 'tapirus indicus': 285,\n",
-       " 'hystrix brachyura': 9485,\n",
-       " 'catopuma temminckii': 353,\n",
-       " 'panthera tigris': 321,\n",
-       " 'sus barbatus': 60,\n",
-       " 'lariscus insignis': 43,\n",
-       " 'enicurus leschenaulti': 147,\n",
-       " 'chalcophaps indica': 295,\n",
-       " 'tragulus napu': 218,\n",
-       " 'genetta tigrina': 98,\n",
-       " 'hystrix cristata': 959,\n",
-       " 'lycaon pictus': 151,\n",
-       " 'oreotragus oreotragus': 97,\n",
-       " 'procavia capensis': 65,\n",
-       " 'heterohyrax brucei': 41,\n",
-       " 'mellivora capensis': 216,\n",
-       " 'ictonyx striatus': 102,\n",
-       " 'alectoris rufa': 2105,\n",
-       " 'leptotila rufaxilla': 419,\n",
-       " 'penelope superciliaris': 114,\n",
-       " 'sapajus apella': 419,\n",
-       " 'ramphastos tucanus': 256,\n",
-       " 'ateles chamek': 59,\n",
-       " 'momotus momota': 428,\n",
-       " 'cathartes burrovianus': 60,\n",
-       " 'galictis vittata': 50,\n",
-       " 'leptotila verreauxi': 245,\n",
-       " 'crypturellus soui': 174,\n",
-       " 'ortalis guttata': 50,\n",
-       " 'morphnus guianensis': 154,\n",
-       " 'psophia crepitans': 2034,\n",
-       " 'dasyprocta fuliginosa': 14237,\n",
-       " 'sciurus igniventris': 283,\n",
-       " 'mitu salvini': 1531,\n",
-       " 'myoprocta pratti': 669,\n",
-       " 'tamandua mexicana': 101,\n",
-       " 'penelope purpurascens': 758,\n",
-       " 'streptopelia capicola': 55,\n",
-       " 'camelus dromedarius': 1214,\n",
-       " 'otocyon megalotis': 1276,\n",
-       " 'acryllium vulturinum': 1831,\n",
-       " 'equus grevyi': 3042,\n",
-       " 'diceros bicornis': 281,\n",
-       " 'ceratotherium simum': 84,\n",
-       " 'proteles cristatus': 937,\n",
-       " 'sagittarius serpentarius': 5194,\n",
-       " 'leptailurus serval': 2582,\n",
-       " 'tragelaphus strepsiceros': 5145,\n",
-       " 'hippopotamus amphibius': 6048,\n",
-       " 'vanellus coronatus': 47,\n",
-       " 'connochaetes taurinus': 480765,\n",
-       " 'dicerorhinus sumatrensis': 54,\n",
-       " 'paguma larvata': 13386,\n",
-       " 'pardofelis marmorata': 271,\n",
-       " 'neofelis nebulosa': 204,\n",
-       " 'cuon alpinus': 185,\n",
-       " 'martes flavigula': 17174,\n",
-       " 'prionodon linsang': 110,\n",
-       " 'rollulus rouloul': 55,\n",
-       " 'lophura inornata': 56,\n",
-       " 'capricornis sumatraensis': 33651,\n",
-       " 'macaca fascicularis': 727,\n",
-       " 'pternistis nobilis': 2020,\n",
-       " 'allochrocebus lhoesti': 3077,\n",
-       " 'cephalophus nigrifrons': 13490,\n",
-       " 'atherurus africanus': 1579,\n",
-       " 'pan troglodytes': 444,\n",
-       " 'cercopithecus mitis': 387,\n",
-       " 'funisciurus carruthersi': 1397,\n",
-       " 'protoxerus stangeri': 93,\n",
-       " 'paraxerus boehmi': 79,\n",
-       " 'cephalophus silvicultor': 859,\n",
-       " 'oenomys hypoxanthus': 138,\n",
-       " 'hybomys univittatus': 78,\n",
-       " 'genetta servalina': 167,\n",
-       " 'lupulella adusta': 59,\n",
-       " 'mus minutoides': 63,\n",
-       " 'turtur tympanistria': 167,\n",
-       " 'nandinia binotata': 48,\n",
-       " 'colobus angolensis': 114,\n",
-       " 'meleagris ocellata': 26493,\n",
-       " 'crax rubra': 18606,\n",
-       " 'tapirus bairdii': 4394,\n",
-       " 'leptotila plumbeiceps': 1541,\n",
-       " 'mazama temama': 5205,\n",
-       " 'conepatus semistriatus': 168,\n",
-       " 'odocoileus pandora': 1164,\n",
-       " 'ortalis vetula': 169,\n",
-       " 'presbytis thomasi': 99,\n",
-       " 'neofelis diardi': 76,\n",
-       " 'tragulus kanchil': 59,\n",
-       " 'arctictis binturong': 82,\n",
-       " 'spilornis cheela': 194,\n",
-       " 'myophonus caeruleus': 2960,\n",
-       " 'cerdocyon thous': 349,\n",
-       " 'cercocebus sanjei': 84,\n",
-       " 'guttera pucherani': 61,\n",
-       " 'cephalophus harveyi': 786,\n",
-       " 'bdeogale crassicauda': 440,\n",
-       " 'hystrix africaeaustralis': 184,\n",
-       " 'paraxerus vexillarius': 141,\n",
-       " 'petrodromus tetradactylus': 118,\n",
-       " 'rhynchocyon cirnei': 91,\n",
-       " 'rhynchocyon udzungwensis': 134,\n",
-       " 'philantomba monticola': 77,\n",
-       " 'columba larvata': 184,\n",
-       " 'mazama chunyi': 760,\n",
-       " 'cuniculus taczanowskii': 1504,\n",
-       " 'didelphis pernigra': 10383,\n",
-       " 'tremarctos ornatus': 462,\n",
-       " 'macaca arctoides': 74636,\n",
-       " 'arctonyx collaris': 761,\n",
-       " 'ursus thibetanus': 1221,\n",
-       " 'lophura nycthemera': 16626,\n",
-       " 'muntiacus rooseveltorum': 60143,\n",
-       " 'zoothera dauma': 742,\n",
-       " 'atherurus macrourus': 13331,\n",
-       " 'polyplectron bicalcaratum': 7936,\n",
-       " 'viverra zibetha': 1570,\n",
-       " 'chrotogale owstoni': 216,\n",
-       " 'prionodon pardicolor': 3587,\n",
-       " 'dremomys rufigenis': 40465,\n",
-       " 'urva urva': 17535,\n",
-       " 'viverricula indica': 88,\n",
-       " 'hydrornis cyaneus': 113,\n",
-       " 'lophura diardi': 42,\n",
-       " 'callosciurus erythraeus': 5837,\n",
-       " 'ratufa bicolor': 233,\n",
-       " 'geokichla citrina': 313,\n",
-       " 'lagidium viscacia': 336,\n",
-       " 'grallaria andicolus': 116,\n",
-       " 'hippocamelus antisensis': 115,\n",
-       " 'lycalopex culpaeus': 1391,\n",
-       " 'conepatus chinga': 413,\n",
-       " 'vicugna pacos': 1232,\n",
-       " 'cinclodes fuscus': 53,\n",
-       " 'tupaia belangeri': 4780,\n",
-       " 'muntiacus vuquangensis': 119784,\n",
-       " 'claravis pretiosa': 160,\n",
-       " 'aramus guarauna': 40,\n",
-       " 'fossa fossana': 1101,\n",
-       " 'setifer setosus': 247,\n",
-       " 'galidictis fasciata': 127,\n",
-       " 'eupleres goudotii': 246,\n",
-       " 'mentocrex kioloides': 600,\n",
-       " 'rattus rattus': 3315,\n",
-       " 'tenrec ecaudatus': 97,\n",
-       " 'copsychus albospecularis': 740,\n",
-       " 'galidia elegans': 354,\n",
-       " 'lophotibis cristata': 1410,\n",
-       " 'cryptoprocta ferox': 729,\n",
-       " 'coua serriana': 4501,\n",
-       " 'coua ruficeps': 78,\n",
-       " 'streptopelia picturata': 792,\n",
-       " 'brachypteracias squamiger': 1736,\n",
-       " 'salanoia concolor': 82,\n",
-       " 'suncus murinus': 50,\n",
-       " 'eliurus petteri': 98,\n",
-       " 'hypogeomys antimena': 554,\n",
-       " 'eliurus penicillatus': 161,\n",
-       " 'coua caerulea': 45,\n",
-       " 'cisticola cherina': 122,\n",
-       " 'nyctanassa violacea': 114,\n",
-       " 'dasyprocta leporina': 5245,\n",
-       " 'crax alector': 1427,\n",
-       " 'didelphis imperfecta': 102,\n",
-       " 'mitu tomentosum': 189,\n",
-       " 'crypturellus variegatus': 62,\n",
-       " 'funisciurus pyrropus': 119,\n",
-       " 'erinaceus europaeus': 10706,\n",
-       " 'capra hircus': 3151,\n",
-       " 'rattus norvegicus': 861,\n",
-       " 'equus asinus': 697,\n",
-       " 'corvus corax': 883,\n",
-       " 'zenaida asiatica': 211,\n",
-       " 'asio flammeus': 278,\n",
-       " 'anous stolidus': 106,\n",
-       " 'urocyon littoralis': 45115,\n",
-       " 'antilocapra americana': 621,\n",
-       " 'ovis canadensis': 90,\n",
-       " 'pekania pennanti': 45,\n",
-       " 'damaliscus lunatus': 11325,\n",
-       " 'felis lybica': 204,\n",
-       " 'parahyaena brunnea': 47,\n",
-       " 'equus zebra': 3091,\n",
-       " 'oryx gazella': 3972,\n",
-       " 'antidorcas marsupialis': 2740,\n",
-       " 'pelea capreolus': 88,\n",
-       " 'redunca fulvorufula': 207,\n",
-       " 'suricata suricatta': 45,\n",
-       " 'connochaetes gnou': 667,\n",
-       " 'damaliscus pygargus': 1123,\n",
-       " 'turdus cardis': 85,\n",
-       " 'garrulax castanotis': 479,\n",
-       " 'pygathrix nemaeus': 1229,\n",
-       " 'nesolagus timminsi': 4943,\n",
-       " 'moschiola meminna': 3918,\n",
-       " 'hydrornis elliotii': 44,\n",
-       " 'rheinardia ocellata': 446,\n",
-       " 'trachypithecus phayrei': 105,\n",
-       " 'strix leptogrammica': 415,\n",
-       " 'pterorhinus pectoralis': 442,\n",
-       " 'geokichla sibirica': 283,\n",
-       " 'turdus obscurus': 106,\n",
-       " 'plecturocebus ornatus': 49}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "more_samples = {}\n",
-    "for species, num in num_species_dict.items():\n",
-    "    if num >= 40:\n",
-    "        more_samples[species] = num\n",
-    "\n",
-    "more_samples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have  354  species for which there are at least 40 images.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"We have \", len(more_samples), \" species for which there are at least 40 images.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have  407  species for which there are at least 20 images.\n"
-     ]
-    }
-   ],
-   "source": [
-    "mid_samples = {}\n",
-    "for species, num in num_species_dict.items():\n",
-    "    if num >= 20:\n",
-    "        mid_samples[species] = num\n",
-    "\n",
-    "print(\"We have \", len(mid_samples), \" species for which there are at least 20 images.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## One Image per Species per Sequence\n",
-    "\n",
-    "Now let's generate the final dataset we'll use for testing. When there are sequences of images, there's the clear potential to capture multiple images of the same animal, so we'll check for duplicates of `sequence_id` and `species` pairings within datasets and keep the first instance of each species within a sequence. We expect to have between 948,156 and 1,000,000 labeled images as a result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/885438569.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  dedupe_species['multi-image'] = dedupe_species.duplicated(subset = ['dataset_name', 'sequence_id', 'species', 'location_id'], keep = 'first')\n"
-     ]
-    }
-   ],
-   "source": [
-    "dedupe_species['multi-image'] = dedupe_species.duplicated(subset = ['dataset_name', 'sequence_id', 'species', 'location_id'], keep = 'first')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
-       "      <th>image_id</th>\n",
-       "      <th>sequence_id</th>\n",
-       "      <th>location_id</th>\n",
-       "      <th>frame_num</th>\n",
-       "      <th>original_label</th>\n",
-       "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>...</th>\n",
-       "      <th>superfamily</th>\n",
-       "      <th>family</th>\n",
-       "      <th>subfamily</th>\n",
-       "      <th>tribe</th>\n",
-       "      <th>genus</th>\n",
-       "      <th>species</th>\n",
-       "      <th>subspecies</th>\n",
-       "      <th>variety</th>\n",
-       "      <th>url_dupe</th>\n",
-       "      <th>multi-image</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>05-09-2012 07:33:45</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felidae</td>\n",
-       "      <td>felinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felis</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>opossum</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>virginia opossum</td>\n",
-       "      <td>03-29-2012 02:34:13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>didelphidae</td>\n",
-       "      <td>didelphinae</td>\n",
-       "      <td>didelphini</td>\n",
-       "      <td>didelphis</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a2176e7-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f011019-5567-11e8-a650...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>2</td>\n",
-       "      <td>dog</td>\n",
-       "      <td>canis familiaris</td>\n",
-       "      <td>domestic dog</td>\n",
-       "      <td>11-29-2011 17:28:26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis familiaris</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 598de824-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f8257b0-5567-11e8-b82e...</td>\n",
-       "      <td>Caltech Camera Traps : 57</td>\n",
-       "      <td>1</td>\n",
-       "      <td>raccoon</td>\n",
-       "      <td>procyon lotor</td>\n",
-       "      <td>raccoon</td>\n",
-       "      <td>12-25-2013 21:48:54</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>procyonidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>procyon</td>\n",
-       "      <td>procyon lotor</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5a1cb0d8-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f18c3eb-5567-11e8-8b81...</td>\n",
-       "      <td>Caltech Camera Traps : 46</td>\n",
-       "      <td>2</td>\n",
-       "      <td>opossum</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>virginia opossum</td>\n",
-       "      <td>04-19-2012 22:13:23</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>didelphidae</td>\n",
-       "      <td>didelphinae</td>\n",
-       "      <td>didelphini</td>\n",
-       "      <td>didelphis</td>\n",
-       "      <td>didelphis virginiana</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
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-       "</table>\n",
-       "<p>5 rows × 32 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            dataset_name                                                url   \n",
-       "2   Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "3   Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "8   Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "10  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "14  Caltech Camera Traps  https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "\n",
-       "                                             image_id   \n",
-       "2   Caltech Camera Traps : 59b93afb-23d2-11e8-a6a3...  \\\n",
-       "3   Caltech Camera Traps : 59641f56-23d2-11e8-a6a3...   \n",
-       "8   Caltech Camera Traps : 5a2176e7-23d2-11e8-a6a3...   \n",
-       "10  Caltech Camera Traps : 598de824-23d2-11e8-a6a3...   \n",
-       "14  Caltech Camera Traps : 5a1cb0d8-23d2-11e8-a6a3...   \n",
-       "\n",
-       "                                          sequence_id   \n",
-       "2   Caltech Camera Traps : 6f04895c-5567-11e8-a3d6...  \\\n",
-       "3   Caltech Camera Traps : 6f0385b5-5567-11e8-a80b...   \n",
-       "8   Caltech Camera Traps : 6f011019-5567-11e8-a650...   \n",
-       "10  Caltech Camera Traps : 6f8257b0-5567-11e8-b82e...   \n",
-       "14  Caltech Camera Traps : 6f18c3eb-5567-11e8-8b81...   \n",
-       "\n",
-       "                  location_id  frame_num original_label       scientific_name   \n",
-       "2   Caltech Camera Traps : 38          2            cat           felis catus  \\\n",
-       "3   Caltech Camera Traps : 38          2        opossum  didelphis virginiana   \n",
-       "8   Caltech Camera Traps : 38          2            dog      canis familiaris   \n",
-       "10  Caltech Camera Traps : 57          1        raccoon         procyon lotor   \n",
-       "14  Caltech Camera Traps : 46          2        opossum  didelphis virginiana   \n",
-       "\n",
-       "         common_name             datetime  ... superfamily       family   \n",
-       "2                cat  05-09-2012 07:33:45  ...         NaN      felidae  \\\n",
-       "3   virginia opossum  03-29-2012 02:34:13  ...         NaN  didelphidae   \n",
-       "8       domestic dog  11-29-2011 17:28:26  ...         NaN      canidae   \n",
-       "10           raccoon  12-25-2013 21:48:54  ...         NaN  procyonidae   \n",
-       "14  virginia opossum  04-19-2012 22:13:23  ...         NaN  didelphidae   \n",
-       "\n",
-       "      subfamily       tribe      genus               species subspecies   \n",
-       "2       felinae         NaN      felis           felis catus        NaN  \\\n",
-       "3   didelphinae  didelphini  didelphis  didelphis virginiana        NaN   \n",
-       "8           NaN         NaN      canis      canis familiaris        NaN   \n",
-       "10          NaN         NaN    procyon         procyon lotor        NaN   \n",
-       "14  didelphinae  didelphini  didelphis  didelphis virginiana        NaN   \n",
-       "\n",
-       "   variety url_dupe multi-image  \n",
-       "2      NaN    False       False  \n",
-       "3      NaN    False       False  \n",
-       "8      NaN    False       False  \n",
-       "10     NaN    False       False  \n",
-       "14     NaN    False       False  \n",
-       "\n",
-       "[5 rows x 32 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_species.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "multi-image\n",
-       "True     5410675\n",
-       "False     955310\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dedupe_species['multi-image'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
-       "      <th>image_id</th>\n",
-       "      <th>sequence_id</th>\n",
-       "      <th>location_id</th>\n",
-       "      <th>frame_num</th>\n",
-       "      <th>original_label</th>\n",
-       "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>...</th>\n",
-       "      <th>superfamily</th>\n",
-       "      <th>family</th>\n",
-       "      <th>subfamily</th>\n",
-       "      <th>tribe</th>\n",
-       "      <th>genus</th>\n",
-       "      <th>species</th>\n",
-       "      <th>subspecies</th>\n",
-       "      <th>variety</th>\n",
-       "      <th>url_dupe</th>\n",
-       "      <th>multi-image</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>9217270</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S5/C01/C01_R2/S5_C01_R2_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S5#C01#2#60</td>\n",
-       "      <td>Snapshot Serengeti : C01</td>\n",
-       "      <td>1</td>\n",
-       "      <td>gazellethomsons</td>\n",
-       "      <td>eudorcas thomsonii</td>\n",
-       "      <td>thomson's gazelle</td>\n",
-       "      <td>07-22-2012 17:18:52</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>antilopinae</td>\n",
-       "      <td>antilopini</td>\n",
-       "      <td>eudorcas</td>\n",
-       "      <td>eudorcas thomsonii</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7191</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 59b12eec-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f031702-5567-11e8-a352...</td>\n",
-       "      <td>Caltech Camera Traps : 38</td>\n",
-       "      <td>3</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>cat</td>\n",
-       "      <td>03-11-2012 02:11:19</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felidae</td>\n",
-       "      <td>felinae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>felis</td>\n",
-       "      <td>felis catus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3717318</th>\n",
-       "      <td>WCS Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/wcs-...</td>\n",
-       "      <td>WCS Camera Traps : d16366f0-92d4-11e9-9412-000...</td>\n",
-       "      <td>WCS Camera Traps : ken-004-d0051-1</td>\n",
-       "      <td>WCS Camera Traps : 338</td>\n",
-       "      <td>1</td>\n",
-       "      <td>litocranius walleri</td>\n",
-       "      <td>litocranius walleri</td>\n",
-       "      <td>gerenuk</td>\n",
-       "      <td>11-17-2010 01:51:16</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>antilopinae</td>\n",
-       "      <td>antilopini</td>\n",
-       "      <td>litocranius</td>\n",
-       "      <td>litocranius walleri</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15724471</th>\n",
-       "      <td>SWG Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/swg-...</td>\n",
-       "      <td>SWG Camera Traps : d26dc2c7-8c29-11eb-a99a-000...</td>\n",
-       "      <td>SWG Camera Traps : 3550f0a8-8c2a-11eb-b40a-000...</td>\n",
-       "      <td>SWG Camera Traps : loc_0577</td>\n",
-       "      <td>0</td>\n",
-       "      <td>spotted_linsang</td>\n",
-       "      <td>prionodon pardicolor</td>\n",
-       "      <td>spotted linsang</td>\n",
-       "      <td>07-24-2019 04:51:19</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>prionodontidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>prionodon</td>\n",
-       "      <td>prionodon pardicolor</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4167844</th>\n",
-       "      <td>WCS Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/wcs-...</td>\n",
-       "      <td>WCS Camera Traps : 23c2cb9f-92d5-11e9-8dda-000...</td>\n",
-       "      <td>WCS Camera Traps : ken-010-d0054-2</td>\n",
-       "      <td>WCS Camera Traps : 1370</td>\n",
-       "      <td>1</td>\n",
-       "      <td>struthio camelus</td>\n",
-       "      <td>struthio camelus</td>\n",
-       "      <td>common ostrich</td>\n",
-       "      <td>06-22-2011 17:28:43</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>struthionidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>struthio</td>\n",
-       "      <td>struthio camelus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8980365</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S4/L08/L08_R1/S4_L08_R1_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S4#L08#1#544</td>\n",
-       "      <td>Snapshot Serengeti : L08</td>\n",
-       "      <td>1</td>\n",
-       "      <td>wildebeest</td>\n",
-       "      <td>connochaetes taurinus</td>\n",
-       "      <td>blue wildebeest</td>\n",
-       "      <td>02-27-2012 14:25:40</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>bovidae</td>\n",
-       "      <td>antilopinae</td>\n",
-       "      <td>alcelaphini</td>\n",
-       "      <td>connochaetes</td>\n",
-       "      <td>connochaetes taurinus</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12541802</th>\n",
-       "      <td>Snapshot Serengeti</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/snap...</td>\n",
-       "      <td>Snapshot Serengeti : S9/E07/E07_R3/S9_E07_R3_I...</td>\n",
-       "      <td>Snapshot Serengeti : SER_S9#E07#3#988</td>\n",
-       "      <td>Snapshot Serengeti : E07</td>\n",
-       "      <td>1</td>\n",
-       "      <td>zebra</td>\n",
-       "      <td>equus quagga</td>\n",
-       "      <td>plains zebra</td>\n",
-       "      <td>11-15-2014 13:49:32</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>equidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>equus</td>\n",
-       "      <td>equus quagga</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>7 rows × 32 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  dataset_name   \n",
-       "9217270     Snapshot Serengeti  \\\n",
-       "7191      Caltech Camera Traps   \n",
-       "3717318       WCS Camera Traps   \n",
-       "15724471      SWG Camera Traps   \n",
-       "4167844       WCS Camera Traps   \n",
-       "8980365     Snapshot Serengeti   \n",
-       "12541802    Snapshot Serengeti   \n",
-       "\n",
-       "                                                        url   \n",
-       "9217270   https://lilablobssc.blob.core.windows.net/snap...  \\\n",
-       "7191      https://lilablobssc.blob.core.windows.net/calt...   \n",
-       "3717318   https://lilablobssc.blob.core.windows.net/wcs-...   \n",
-       "15724471  https://lilablobssc.blob.core.windows.net/swg-...   \n",
-       "4167844   https://lilablobssc.blob.core.windows.net/wcs-...   \n",
-       "8980365   https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "12541802  https://lilablobssc.blob.core.windows.net/snap...   \n",
-       "\n",
-       "                                                   image_id   \n",
-       "9217270   Snapshot Serengeti : S5/C01/C01_R2/S5_C01_R2_I...  \\\n",
-       "7191      Caltech Camera Traps : 59b12eec-23d2-11e8-a6a3...   \n",
-       "3717318   WCS Camera Traps : d16366f0-92d4-11e9-9412-000...   \n",
-       "15724471  SWG Camera Traps : d26dc2c7-8c29-11eb-a99a-000...   \n",
-       "4167844   WCS Camera Traps : 23c2cb9f-92d5-11e9-8dda-000...   \n",
-       "8980365   Snapshot Serengeti : S4/L08/L08_R1/S4_L08_R1_I...   \n",
-       "12541802  Snapshot Serengeti : S9/E07/E07_R3/S9_E07_R3_I...   \n",
-       "\n",
-       "                                                sequence_id   \n",
-       "9217270                Snapshot Serengeti : SER_S5#C01#2#60  \\\n",
-       "7191      Caltech Camera Traps : 6f031702-5567-11e8-a352...   \n",
-       "3717318                  WCS Camera Traps : ken-004-d0051-1   \n",
-       "15724471  SWG Camera Traps : 3550f0a8-8c2a-11eb-b40a-000...   \n",
-       "4167844                  WCS Camera Traps : ken-010-d0054-2   \n",
-       "8980365               Snapshot Serengeti : SER_S4#L08#1#544   \n",
-       "12541802              Snapshot Serengeti : SER_S9#E07#3#988   \n",
-       "\n",
-       "                          location_id  frame_num       original_label   \n",
-       "9217270      Snapshot Serengeti : C01          1      gazellethomsons  \\\n",
-       "7191        Caltech Camera Traps : 38          3                  cat   \n",
-       "3717318        WCS Camera Traps : 338          1  litocranius walleri   \n",
-       "15724471  SWG Camera Traps : loc_0577          0      spotted_linsang   \n",
-       "4167844       WCS Camera Traps : 1370          1     struthio camelus   \n",
-       "8980365      Snapshot Serengeti : L08          1           wildebeest   \n",
-       "12541802     Snapshot Serengeti : E07          1                zebra   \n",
-       "\n",
-       "                scientific_name        common_name             datetime  ...   \n",
-       "9217270      eudorcas thomsonii  thomson's gazelle  07-22-2012 17:18:52  ...  \\\n",
-       "7191                felis catus                cat  03-11-2012 02:11:19  ...   \n",
-       "3717318     litocranius walleri            gerenuk  11-17-2010 01:51:16  ...   \n",
-       "15724471   prionodon pardicolor    spotted linsang  07-24-2019 04:51:19  ...   \n",
-       "4167844        struthio camelus     common ostrich  06-22-2011 17:28:43  ...   \n",
-       "8980365   connochaetes taurinus    blue wildebeest  02-27-2012 14:25:40  ...   \n",
-       "12541802           equus quagga       plains zebra  11-15-2014 13:49:32  ...   \n",
-       "\n",
-       "         superfamily          family    subfamily        tribe         genus   \n",
-       "9217270          NaN         bovidae  antilopinae   antilopini      eudorcas  \\\n",
-       "7191             NaN         felidae      felinae          NaN         felis   \n",
-       "3717318          NaN         bovidae  antilopinae   antilopini   litocranius   \n",
-       "15724471         NaN  prionodontidae          NaN          NaN     prionodon   \n",
-       "4167844          NaN   struthionidae          NaN          NaN      struthio   \n",
-       "8980365          NaN         bovidae  antilopinae  alcelaphini  connochaetes   \n",
-       "12541802         NaN         equidae          NaN          NaN         equus   \n",
-       "\n",
-       "                        species subspecies variety url_dupe multi-image  \n",
-       "9217270      eudorcas thomsonii        NaN     NaN    False       False  \n",
-       "7191                felis catus        NaN     NaN    False       False  \n",
-       "3717318     litocranius walleri        NaN     NaN    False       False  \n",
-       "15724471   prionodon pardicolor        NaN     NaN    False       False  \n",
-       "4167844        struthio camelus        NaN     NaN    False       False  \n",
-       "8980365   connochaetes taurinus        NaN     NaN    False       False  \n",
-       "12541802           equus quagga        NaN     NaN    False       False  \n",
-       "\n",
-       "[7 rows x 32 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "species_single_seq = dedupe_species.loc[~dedupe_species['multi-image']]\n",
-    "species_single_seq.sample(7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "species_single_seq.to_csv(\"../data/lila_image_urls_and_labels_SingleSpecies.csv\", index = False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dataset_name            18\n",
-       "url                 955310\n",
-       "image_id            955310\n",
-       "sequence_id         948156\n",
-       "location_id           6000\n",
-       "frame_num              112\n",
-       "original_label         872\n",
-       "scientific_name        673\n",
-       "common_name            729\n",
-       "datetime            927905\n",
-       "annotation_level         3\n",
-       "genus                  452\n",
-       "species                667\n",
-       "dtype: int64"
+       "num_species\n",
+       "1.0    10764331\n",
+       "2.0      195134\n",
+       "3.0        6069\n",
+       "4.0         368\n",
+       "Name: count, dtype: int64"
       ]
      },
      "execution_count": 29,
@@ -4697,1015 +1980,239 @@
     }
    ],
    "source": [
-    "species_single_seq[cols].nunique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Same number of unique common and scientific names."
+    "df_cleaned.loc[df_cleaned[\"num_species\"].isna(), \"num_species\"] = 1.0\n",
+    "\n",
+    "df_cleaned.num_species.value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Check Number of Images per Species"
+    "### Taxonomic String Exploration"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'felis catus': 11942,\n",
-       " 'didelphis virginiana': 5804,\n",
-       " 'canis familiaris': 2661,\n",
-       " 'procyon lotor': 4706,\n",
-       " 'canis latrans': 11585,\n",
-       " 'urocyon cinereoargenteus': 1162,\n",
-       " 'lynx rufus': 4020,\n",
-       " 'taxidea taxus': 23,\n",
-       " 'puma concolor': 766,\n",
-       " 'sus scrofa': 26287,\n",
-       " 'bos taurus': 7645,\n",
-       " 'sciurus carolinensis': 3,\n",
-       " 'tamias striatus': 1,\n",
-       " 'marmota monax': 1,\n",
-       " 'meleagris gallopavo': 138,\n",
-       " 'odocoileus virginianus': 197,\n",
-       " 'sylvilagus floridanus': 1,\n",
-       " 'homo sapiens': 40360,\n",
-       " 'mephitis mephitis': 4,\n",
-       " 'vulpes vulpes': 69,\n",
-       " 'sciurus niger': 1,\n",
-       " 'equus caballus': 133,\n",
-       " 'corvus brachyrhynchos': 3,\n",
-       " 'gallus gallus': 1711,\n",
-       " 'ursus americanus': 561,\n",
-       " 'pecari tajacu': 4996,\n",
-       " 'mazama americana': 1588,\n",
-       " 'nasua narica': 207,\n",
-       " 'rattus praetor': 100,\n",
-       " 'leopardus pardalis': 1820,\n",
-       " 'tamiasciurus hudsonicus': 99,\n",
-       " 'didelphis marsupialis': 575,\n",
-       " 'tinamus major': 239,\n",
-       " 'ovis ammon': 100,\n",
-       " 'cervus elaphus': 101,\n",
-       " 'lepus europaeus': 87,\n",
-       " 'apodemus sylvaticus': 100,\n",
-       " 'dasyprocta coibae': 100,\n",
-       " 'martes americana': 1,\n",
-       " 'odocoileus hemionus': 1,\n",
-       " 'lepus americanus': 1,\n",
-       " 'erethizon dorsatum': 3,\n",
-       " 'marmota flaviventris': 1,\n",
-       " 'alces alces': 410,\n",
-       " 'equus ferus': 93,\n",
-       " 'mustela erminea': 2,\n",
-       " 'perisoreus canadensis': 1,\n",
-       " 'troglodytes aedon': 1,\n",
-       " 'cyanocitta stelleri': 1,\n",
-       " 'dendragapus obscurus': 1,\n",
-       " 'junco hyemalis': 1,\n",
-       " 'cervus canadensis': 12477,\n",
-       " 'otospermophilus beecheyi': 2,\n",
-       " 'callipepla californica': 2,\n",
-       " 'lepus californicus': 1,\n",
-       " 'neogale frenata': 14,\n",
-       " 'meles meles': 1,\n",
-       " 'lontra canadensis': 1,\n",
-       " 'dasypus novemcinctus': 490,\n",
-       " 'equus africanus': 93,\n",
-       " 'psophia leucoptera': 1354,\n",
-       " 'tayassu pecari': 11530,\n",
-       " 'dasyprocta punctata': 2277,\n",
-       " 'cuniculus paca': 2646,\n",
-       " 'sciurus spadiceus': 327,\n",
-       " 'tapirus terrestris': 1721,\n",
-       " 'mitu tuberosum': 1794,\n",
-       " 'geotrygon montana': 54,\n",
-       " 'nasua nasua': 747,\n",
-       " 'eira barbara': 631,\n",
-       " 'penelope jacquacu': 765,\n",
-       " 'alouatta sara': 3,\n",
-       " 'atelocynus microtis': 355,\n",
-       " 'procyon cancrivorus': 248,\n",
-       " 'aramides cajaneus': 146,\n",
-       " 'panthera onca': 832,\n",
-       " 'myrmecophaga tridactyla': 398,\n",
-       " 'pilherodius pileatus': 7,\n",
-       " 'hydrochoerus hydrochaeris': 172,\n",
-       " 'odontophorus gujanensis': 17,\n",
-       " 'crypturellus cinereus': 13,\n",
-       " 'sylvilagus brasiliensis': 1121,\n",
-       " 'mesembrinibis cayennensis': 6,\n",
-       " 'turdus ignobilis': 1,\n",
-       " 'dasypus kappleri': 78,\n",
-       " 'eurypyga helias': 8,\n",
-       " 'priodontes maximus': 184,\n",
-       " 'tamandua tetradactyla': 359,\n",
-       " 'agamia agami': 3,\n",
-       " 'tigrisoma lineatum': 7,\n",
-       " 'cochlearius cochlearius': 3,\n",
-       " 'crypturellus atrocapillus': 1,\n",
-       " 'crypturellus undulatus': 1,\n",
-       " 'herpailurus yagouaroundi': 57,\n",
-       " 'speothos venaticus': 13,\n",
-       " 'lontra longicaudis': 3,\n",
-       " 'neomorphus geoffroyi': 3,\n",
-       " 'nyctidromus albicollis': 1,\n",
-       " 'harpia harpyja': 1,\n",
-       " 'leopardus wiedii': 136,\n",
-       " 'buteogallus urubitinga': 112,\n",
-       " 'cairina moschata': 6,\n",
-       " 'tinamus guttatus': 10,\n",
-       " 'formicarius analis': 27,\n",
-       " 'ardea alba': 3,\n",
-       " 'oressochen jubatus': 51,\n",
-       " 'molothrus oryzivorus': 2,\n",
-       " 'coragyps atratus': 100,\n",
-       " 'crypturellus erythropus': 2,\n",
-       " 'anhima cornuta': 24,\n",
-       " 'vanellus cayanus': 2,\n",
-       " 'mazama gouazoubira': 182,\n",
-       " 'daptrius ater': 8,\n",
-       " 'crypturellus bartletti': 4,\n",
-       " 'blastocerus dichotomus': 2,\n",
-       " 'philander opossum': 28,\n",
-       " 'lutreolina crassicaudata': 2,\n",
-       " 'tupinambis teguixin': 20,\n",
-       " 'capra aegagrus': 1642,\n",
-       " 'ovis aries': 602,\n",
-       " 'canis lupus': 925,\n",
-       " 'lepus saxatilis': 647,\n",
-       " 'nesotragus moschatus': 521,\n",
-       " 'turtur chalcospilos': 10,\n",
-       " 'papio anubis': 783,\n",
-       " 'genetta genetta': 43,\n",
-       " 'sylvicapra grimmia': 736,\n",
-       " 'tragelaphus scriptus': 837,\n",
-       " 'turdus tephronotus': 1,\n",
-       " 'cercopithecus erythrogaster': 41,\n",
-       " 'thryonomys gregorianus': 2,\n",
-       " 'paraxerus ochraceus': 5,\n",
-       " 'herpestes sanguineus': 14,\n",
-       " 'loxodonta africana': 23779,\n",
-       " 'cricetomys gambianus': 941,\n",
-       " 'raphicerus campestris': 494,\n",
-       " 'hyaena hyaena': 565,\n",
-       " 'bubulcus ibis': 19,\n",
-       " 'aepyceros melampus': 20995,\n",
-       " 'crocuta crocuta': 12746,\n",
-       " 'caracal caracal': 173,\n",
-       " 'panthera leo': 8088,\n",
-       " 'tragelaphus oryx': 6990,\n",
-       " 'kobus ellipsiprymnus': 864,\n",
-       " 'phacochoerus africanus': 14871,\n",
-       " 'panthera pardus': 661,\n",
-       " 'lamprotornis superbus': 46,\n",
-       " 'ichneumia albicauda': 102,\n",
-       " 'lanius collaris': 3,\n",
-       " 'lupulella mesomelas': 2172,\n",
-       " 'euxerus erythropus': 29,\n",
-       " 'syncerus caffer': 25140,\n",
-       " 'equus quagga': 123208,\n",
-       " 'giraffa camelopardalis': 18314,\n",
-       " 'alcelaphus buselaphus': 20674,\n",
-       " 'chlorocebus pygerythrus': 1350,\n",
-       " 'madoqua guentheri': 4593,\n",
-       " 'potamochoerus larvatus': 219,\n",
-       " 'numida meleagris': 78,\n",
-       " 'nanger granti': 16790,\n",
-       " 'eudorcas thomsonii': 113587,\n",
-       " 'struthio camelus': 2216,\n",
-       " 'orycteropus afer': 967,\n",
-       " 'acinonyx jubatus': 2649,\n",
-       " 'eupodotis senegalensis': 102,\n",
-       " 'felis silvestris': 8,\n",
-       " 'pternistis leucoscepus': 72,\n",
-       " 'stigmochelys pardalis': 5,\n",
-       " 'oryx beisa': 241,\n",
-       " 'litocranius walleri': 261,\n",
-       " 'eupodotis gindiana': 19,\n",
-       " 'ardeotis kori': 1421,\n",
-       " 'pipile cumanensis': 7,\n",
-       " 'helogale parvula': 22,\n",
-       " 'lissotis melanogaster': 60,\n",
-       " 'ortygornis sephaena': 6,\n",
-       " 'trichys fasciculata': 4,\n",
-       " 'macaca nemestrina': 8669,\n",
-       " 'hydrornis guajanus': 8,\n",
-       " 'argusianus argus': 1361,\n",
-       " 'paradoxurus hermaphroditus': 3972,\n",
-       " 'prionailurus bengalensis': 274,\n",
-       " 'hemigalus derbyanus': 84,\n",
-       " 'muntiacus muntjak': 3514,\n",
-       " 'tragulus javanicus': 156,\n",
-       " 'helarctos malayanus': 434,\n",
-       " 'butorides striata': 10,\n",
-       " 'elephas maximus': 175,\n",
-       " 'rusa unicolor': 8142,\n",
-       " 'tapirus indicus': 244,\n",
-       " 'hystrix brachyura': 2839,\n",
-       " 'catopuma temminckii': 253,\n",
-       " 'panthera tigris': 181,\n",
-       " 'sus barbatus': 20,\n",
-       " 'lariscus insignis': 33,\n",
-       " 'tupaia glis': 13,\n",
-       " 'enicurus leschenaulti': 46,\n",
-       " 'chalcophaps indica': 113,\n",
-       " 'tragulus napu': 214,\n",
-       " 'genetta tigrina': 23,\n",
-       " 'hystrix cristata': 702,\n",
-       " 'lycaon pictus': 39,\n",
-       " 'oreotragus oreotragus': 49,\n",
-       " 'tragelaphus imberbis': 7,\n",
-       " 'procavia capensis': 20,\n",
-       " 'streptopelia senegalensis': 7,\n",
-       " 'heterohyrax brucei': 15,\n",
-       " 'mellivora capensis': 134,\n",
-       " 'ictonyx striatus': 63,\n",
-       " 'pternistis hildebrandti': 4,\n",
-       " 'tockus flavirostris': 1,\n",
-       " 'leopardus tigrinus': 6,\n",
-       " 'potos flavus': 1,\n",
-       " 'alectoris rufa': 263,\n",
-       " 'leptotila rufaxilla': 27,\n",
-       " 'ardea cocoi': 5,\n",
-       " 'penelope superciliaris': 17,\n",
-       " 'pteroglossus beauharnaisii': 1,\n",
-       " 'sapajus apella': 74,\n",
-       " 'chelonoidis carbonarius': 2,\n",
-       " 'ramphastos tucanus': 28,\n",
-       " 'ateles chamek': 16,\n",
-       " 'momotus momota': 109,\n",
-       " 'rupornis magnirostris': 3,\n",
-       " 'cathartes burrovianus': 6,\n",
-       " 'amazona oratrix': 2,\n",
-       " 'galictis vittata': 7,\n",
-       " 'saimiri boliviensis': 5,\n",
-       " 'sciurus ignitus': 6,\n",
-       " 'pteronura brasiliensis': 3,\n",
-       " 'leptotila verreauxi': 60,\n",
-       " 'crypturellus soui': 20,\n",
-       " 'egretta thula': 1,\n",
-       " 'monasa morphoeus': 1,\n",
-       " 'tinamus tao': 1,\n",
-       " 'cebus albifrons': 4,\n",
-       " 'cathartes melambrotus': 2,\n",
-       " 'formicarius colma': 1,\n",
-       " 'spizaetus ornatus': 1,\n",
-       " 'ciconia maguari': 1,\n",
-       " 'ortalis guttata': 5,\n",
-       " 'morphnus guianensis': 17,\n",
-       " 'psophia crepitans': 151,\n",
-       " 'nothocrax urumutum': 23,\n",
-       " 'dasyprocta fuliginosa': 3258,\n",
-       " 'sciurus igniventris': 133,\n",
-       " 'mitu salvini': 271,\n",
-       " 'myoprocta pratti': 150,\n",
-       " 'coendou bicolor': 10,\n",
-       " 'microsciurus flaviventer': 1,\n",
-       " 'buteogallus solitarius': 1,\n",
-       " 'tamandua mexicana': 14,\n",
-       " 'microsciurus mimulus': 1,\n",
-       " 'penelope purpurascens': 29,\n",
-       " 'sciurus granatensis': 4,\n",
-       " 'odontophorus erythrops': 2,\n",
-       " 'geotrygon saphirina': 1,\n",
-       " 'cabassous centralis': 1,\n",
-       " 'cabassous unicinctus': 2,\n",
-       " 'streptopelia capicola': 25,\n",
-       " 'camelus dromedarius': 315,\n",
-       " 'otocyon megalotis': 764,\n",
-       " 'acryllium vulturinum': 316,\n",
-       " 'equus grevyi': 578,\n",
-       " 'diceros bicornis': 94,\n",
-       " 'ceratotherium simum': 36,\n",
-       " 'proteles cristatus': 643,\n",
-       " 'balearica regulorum': 1,\n",
-       " 'sagittarius serpentarius': 1743,\n",
-       " 'leptailurus serval': 1413,\n",
-       " 'melaenornis pammelaina': 2,\n",
-       " 'tragelaphus strepsiceros': 2762,\n",
-       " 'hippopotamus amphibius': 5332,\n",
-       " 'corythaixoides leucogaster': 2,\n",
-       " 'melierax poliopterus': 4,\n",
-       " 'burhinus capensis': 3,\n",
-       " 'lissotis hartlaubii': 2,\n",
-       " 'erythrocebus patas': 3,\n",
-       " 'ardea melanocephala': 2,\n",
-       " 'vanellus coronatus': 26,\n",
-       " 'tockus deckeni': 5,\n",
-       " 'atelerix albiventris': 1,\n",
-       " 'galago senegalensis': 2,\n",
-       " 'eudorcas rufifrons': 2,\n",
-       " 'xerus rutilus': 20,\n",
-       " 'laniarius funebris': 1,\n",
-       " 'lamprotornis chalybaeus': 8,\n",
-       " 'connochaetes taurinus': 178695,\n",
-       " 'bostrychia hagedash': 3,\n",
-       " 'dicerorhinus sumatrensis': 34,\n",
-       " 'paguma larvata': 3491,\n",
-       " 'pardofelis marmorata': 145,\n",
-       " 'neofelis nebulosa': 133,\n",
-       " 'cuon alpinus': 90,\n",
-       " 'varanus salvator': 12,\n",
-       " 'arctogalidia trivirgata': 8,\n",
-       " 'martes flavigula': 4273,\n",
-       " 'prionodon linsang': 95,\n",
-       " 'rollulus rouloul': 35,\n",
-       " 'lophura inornata': 33,\n",
-       " 'mydaus javanensis': 21,\n",
-       " 'lophura ignita': 2,\n",
-       " 'nesolagus netscheri': 2,\n",
-       " 'polyplectron chalcurum': 9,\n",
-       " 'manis javanica': 17,\n",
-       " 'hydrornis schneideri': 6,\n",
-       " 'capricornis sumatraensis': 3950,\n",
-       " 'macaca fascicularis': 74,\n",
-       " 'presbytis femoralis': 1,\n",
-       " 'baryphthengus martii': 1,\n",
-       " 'pternistis nobilis': 223,\n",
-       " 'allochrocebus lhoesti': 272,\n",
-       " 'cephalophus nigrifrons': 602,\n",
-       " 'atherurus africanus': 215,\n",
-       " 'pan troglodytes': 31,\n",
-       " 'cercopithecus mitis': 98,\n",
-       " 'funisciurus carruthersi': 308,\n",
-       " 'motacilla flava': 5,\n",
-       " 'eurillas latirostris': 1,\n",
-       " 'eurillas virens': 2,\n",
-       " 'thamnomys venustus': 3,\n",
-       " 'protoxerus stangeri': 13,\n",
-       " 'paraxerus boehmi': 18,\n",
-       " 'cephalophus silvicultor': 22,\n",
-       " 'melaenornis ardesiacus': 1,\n",
-       " 'oenomys hypoxanthus': 41,\n",
-       " 'cyanomitra cyanolaema': 1,\n",
-       " 'delacourella capistrata': 2,\n",
-       " 'melocichla mentalis': 1,\n",
-       " 'hybomys univittatus': 21,\n",
-       " 'colomys goslingi': 1,\n",
-       " 'hylomyscus stella': 2,\n",
-       " 'genetta servalina': 103,\n",
-       " 'lupulella adusta': 13,\n",
-       " 'melaenornis fischeri': 2,\n",
-       " 'mus minutoides': 19,\n",
-       " 'stelgidillas gracilirostris': 1,\n",
-       " 'musophaga rossae': 2,\n",
-       " 'acrocephalus baeticatus': 2,\n",
-       " 'turtur tympanistria': 24,\n",
-       " 'praomys tullbergi': 2,\n",
-       " 'malacomys longipes': 4,\n",
-       " 'eurocephalus ruppelli': 3,\n",
-       " 'alopochen aegyptiaca': 1,\n",
-       " 'deomys ferrugineus': 3,\n",
-       " 'scleroptila afra': 3,\n",
-       " 'nandinia binotata': 26,\n",
-       " 'chloropicus griseocephalus': 1,\n",
-       " 'turdus olivaceus': 2,\n",
-       " 'streptopelia lugens': 1,\n",
-       " 'cossypha archeri': 5,\n",
-       " 'atilax paludinosus': 13,\n",
-       " 'colobus angolensis': 2,\n",
-       " 'neocossyphus rufus': 2,\n",
-       " 'heliosciurus rufobrachium': 1,\n",
-       " 'heliosciurus ruwenzorii': 1,\n",
-       " 'urosphena neumanni': 2,\n",
-       " 'dicrurus adsimilis': 1,\n",
-       " 'lamprotornis hildebrandti': 3,\n",
-       " 'aquila rapax': 5,\n",
-       " 'meleagris ocellata': 651,\n",
-       " 'crax rubra': 319,\n",
-       " 'tapirus bairdii': 84,\n",
-       " 'leptotila plumbeiceps': 30,\n",
-       " 'mazama temama': 55,\n",
-       " 'conepatus semistriatus': 23,\n",
-       " 'odocoileus pandora': 18,\n",
-       " 'ortalis vetula': 17,\n",
-       " 'sciurus deppei': 8,\n",
-       " 'buteo ridgwayi': 2,\n",
-       " 'presbytis thomasi': 40,\n",
-       " 'neofelis diardi': 44,\n",
-       " 'arctonyx hoevenii': 20,\n",
-       " 'alophoixus bres': 1,\n",
-       " 'dicaeum trigonostigma': 1,\n",
-       " 'otus spilocephalus': 14,\n",
-       " 'cyanoptila cyanomelana': 1,\n",
-       " 'ficedula mugimaki': 1,\n",
-       " 'dendrocitta occipitalis': 9,\n",
-       " 'rattus tiomanicus': 7,\n",
-       " 'caloperdix oculeus': 1,\n",
-       " 'tragulus kanchil': 45,\n",
-       " 'arctictis binturong': 38,\n",
-       " 'niltava sumatrana': 1,\n",
-       " 'mustela lutreolina': 1,\n",
-       " 'leiothrix argentauris': 2,\n",
-       " 'myophonus melanurus': 18,\n",
-       " 'arborophila rubrirostris': 4,\n",
-       " 'sundasciurus hippurus': 2,\n",
-       " 'larvivora cyane': 2,\n",
-       " 'myophonus glaucinus': 18,\n",
-       " 'lophura erythrophthalma': 1,\n",
-       " 'spilornis cheela': 29,\n",
-       " 'myophonus caeruleus': 899,\n",
-       " 'urva semitorquata': 2,\n",
-       " 'collocalia linchi': 3,\n",
-       " 'callosciurus notatus': 1,\n",
-       " 'cavia aperea': 2,\n",
-       " 'cerdocyon thous': 235,\n",
-       " 'caracara plancus': 1,\n",
-       " 'columbina talpacoti': 2,\n",
-       " 'tolypeutes matacus': 1,\n",
-       " 'tyto alba': 1,\n",
-       " 'euphractus sexcinctus': 1,\n",
-       " 'rhea americana': 2,\n",
-       " 'cercocebus sanjei': 79,\n",
-       " 'guttera pucherani': 55,\n",
-       " 'cephalophus harveyi': 775,\n",
-       " 'bdeogale crassicauda': 433,\n",
-       " 'bdeogale jacksoni': 31,\n",
-       " 'mungos mungo': 7,\n",
-       " 'hystrix africaeaustralis': 182,\n",
-       " 'papio cynocephalus': 18,\n",
-       " 'cephalophus spadix': 25,\n",
-       " 'paraxerus vexillarius': 139,\n",
-       " 'paraxerus cepapi': 1,\n",
-       " 'genetta maculata': 14,\n",
-       " 'petrodromus tetradactylus': 117,\n",
-       " 'rhynchocyon cirnei': 89,\n",
-       " 'civettictis civetta': 15,\n",
-       " 'dendrohyrax arboreus': 17,\n",
-       " 'rhynchocyon udzungwensis': 132,\n",
-       " 'philantomba monticola': 74,\n",
-       " 'pogonocichla stellata': 1,\n",
-       " 'xenoperdix udzungwensis': 11,\n",
-       " 'sheppardia lowei': 1,\n",
-       " 'columba larvata': 183,\n",
-       " 'geokichla gurneyi': 35,\n",
-       " 'piliocolobus gordonorum': 2,\n",
-       " 'accipiter melanoleucus': 1,\n",
-       " 'tauraco livingstonii': 1,\n",
-       " 'stephanoaetus coronatus': 1,\n",
-       " 'rhynchocyon petersi': 25,\n",
-       " 'otolemur garnettii': 5,\n",
-       " 'bycanistes brevis': 1,\n",
-       " 'columba arquatrix': 1,\n",
-       " 'paraxerus lucifer': 5,\n",
-       " 'mazama chunyi': 84,\n",
-       " 'cuniculus taczanowskii': 156,\n",
-       " 'didelphis pernigra': 1103,\n",
-       " 'tremarctos ornatus': 40,\n",
-       " 'macaca arctoides': 12909,\n",
-       " 'arctonyx collaris': 355,\n",
-       " 'ursus thibetanus': 86,\n",
-       " 'lophura nycthemera': 3731,\n",
-       " 'muntiacus rooseveltorum': 8228,\n",
-       " 'zoothera dauma': 179,\n",
-       " 'atherurus macrourus': 3900,\n",
-       " 'polyplectron bicalcaratum': 2192,\n",
-       " 'viverra zibetha': 747,\n",
-       " 'chrotogale owstoni': 63,\n",
-       " 'prionodon pardicolor': 1123,\n",
-       " 'dremomys rufigenis': 12341,\n",
-       " 'aonyx cinereus': 1,\n",
-       " 'macaca assamensis': 8,\n",
-       " 'arborophila brunneopectus': 4,\n",
-       " 'urva urva': 5229,\n",
-       " 'viverricula indica': 68,\n",
-       " 'bambusicola fytchii': 3,\n",
-       " 'bos gaurus': 3,\n",
-       " 'rhizomys sumatrensis': 1,\n",
-       " 'ardeola grayii': 1,\n",
-       " 'scolopax rusticola': 8,\n",
-       " 'arborophila rufogularis': 14,\n",
-       " 'hydrornis oatesi': 7,\n",
-       " 'hylopetes alboniger': 1,\n",
-       " 'circus cyaneus': 1,\n",
-       " 'jynx torquilla': 3,\n",
-       " 'felis chaus': 1,\n",
-       " 'hydrornis cyaneus': 42,\n",
-       " 'macaca mulatta': 2,\n",
-       " 'lophura diardi': 20,\n",
-       " 'mustela strigidorsa': 5,\n",
-       " 'nisaetus nipalensis': 4,\n",
-       " 'cissa chinensis': 2,\n",
-       " 'melogale personata': 1,\n",
-       " 'nisaetus nanus': 1,\n",
-       " 'callosciurus erythraeus': 2035,\n",
-       " 'trochalopteron milnei': 6,\n",
-       " 'arundinax aedon': 1,\n",
-       " 'ficedula tricolor': 1,\n",
-       " 'butastur indicus': 1,\n",
-       " 'aquila heliaca': 1,\n",
-       " 'ratufa bicolor': 55,\n",
-       " 'cyornis banyumas': 3,\n",
-       " 'erythrogenys mcclellandi': 1,\n",
-       " 'pernis ptilorhynchus': 1,\n",
-       " 'copsychus malabaricus': 1,\n",
-       " 'copsychus saularis': 1,\n",
-       " 'geokichla citrina': 110,\n",
-       " 'mustela kathiah': 6,\n",
-       " 'enicurus schistaceus': 1,\n",
-       " 'lagidium viscacia': 38,\n",
-       " 'grallaria andicolus': 20,\n",
-       " 'hippocamelus antisensis': 13,\n",
-       " 'lycalopex culpaeus': 311,\n",
-       " 'conepatus chinga': 58,\n",
-       " 'vicugna pacos': 116,\n",
-       " 'cinclodes fuscus': 7,\n",
-       " 'zonotrichia capensis': 1,\n",
-       " 'cinclodes atacamensis': 3,\n",
-       " 'odontophorus balliviani': 3,\n",
-       " 'carpococcyx renauldi': 1,\n",
-       " 'tupaia belangeri': 1706,\n",
-       " 'melogale everetti': 22,\n",
-       " 'muntiacus vuquangensis': 15951,\n",
-       " 'tigrisoma mexicanum': 2,\n",
-       " 'claravis pretiosa': 10,\n",
-       " 'aramus guarauna': 1,\n",
-       " 'fossa fossana': 84,\n",
-       " 'setifer setosus': 42,\n",
-       " 'galidictis fasciata': 34,\n",
-       " 'eupleres goudotii': 33,\n",
-       " 'mentocrex kioloides': 46,\n",
-       " 'rattus rattus': 1074,\n",
-       " 'tenrec ecaudatus': 23,\n",
-       " 'copsychus albospecularis': 45,\n",
-       " 'galidia elegans': 36,\n",
-       " 'mystacornis crossleyi': 2,\n",
-       " 'lophotibis cristata': 27,\n",
-       " 'cryptoprocta ferox': 74,\n",
-       " 'coua serriana': 81,\n",
-       " 'propithecus candidus': 1,\n",
-       " 'coua ruficeps': 19,\n",
-       " 'streptopelia picturata': 58,\n",
-       " 'brachypteracias squamiger': 35,\n",
-       " 'salanoia concolor': 15,\n",
-       " 'asio madagascariensis': 1,\n",
-       " 'suncus murinus': 19,\n",
-       " 'atelornis pittoides': 2,\n",
-       " 'hemicentetes semispinosus': 8,\n",
-       " 'eutriorchis astur': 1,\n",
-       " 'haliaeetus vociferoides': 1,\n",
-       " 'oxylabes madagascariensis': 5,\n",
-       " 'daubentonia madagascariensis': 2,\n",
-       " 'coturnix delegorguei': 1,\n",
-       " 'eliurus petteri': 9,\n",
-       " 'xanthomixis apperti': 1,\n",
-       " 'terpsiphone mutata': 1,\n",
-       " 'hypogeomys antimena': 20,\n",
-       " 'eliurus penicillatus': 19,\n",
-       " 'coua caerulea': 5,\n",
-       " 'nesomys audeberti': 1,\n",
-       " 'eliurus webbi': 1,\n",
-       " 'saxicola tectes': 3,\n",
-       " 'accipiter madagascariensis': 1,\n",
-       " 'cisticola cherina': 5,\n",
-       " 'eulemur albifrons': 1,\n",
-       " 'euryceros prevostii': 3,\n",
-       " 'motacilla flaviventris': 1,\n",
-       " 'microcebus murinus': 1,\n",
-       " 'crypturellus boucardi': 7,\n",
-       " 'arremonops chloronotus': 1,\n",
-       " 'crypturellus cinnamomeus': 1,\n",
-       " 'spilogale putorius': 2,\n",
-       " 'caprimulgus europaeus': 1,\n",
-       " 'nyctanassa violacea': 8,\n",
-       " 'hylocichla mustelina': 1,\n",
-       " 'dasyprocta leporina': 82,\n",
-       " 'crax alector': 30,\n",
-       " 'didelphis imperfecta': 9,\n",
-       " 'metachirus nudicaudatus': 4,\n",
-       " 'catharus ustulatus': 1,\n",
-       " 'mitu tomentosum': 14,\n",
-       " 'crypturellus variegatus': 7,\n",
-       " 'neomorphus rufipennis': 1,\n",
-       " 'pipile pipile': 1,\n",
-       " 'funisciurus pyrropus': 4,\n",
-       " 'chamaetylas poliophrys': 1,\n",
-       " 'ruwenzorornis johnstoni': 1,\n",
-       " 'thryonomys swinderianus': 1,\n",
-       " 'ploceus alienus': 1,\n",
-       " 'ploceus baglafecht': 1,\n",
-       " 'poecilogale albinucha': 1,\n",
-       " 'anomalurus derbianus': 1,\n",
-       " 'erinaceus europaeus': 3592,\n",
-       " 'capra hircus': 227,\n",
-       " 'rattus norvegicus': 287,\n",
-       " 'equus asinus': 21,\n",
-       " 'corvus corax': 48,\n",
-       " 'zenaida asiatica': 23,\n",
-       " 'athene cunicularia': 3,\n",
-       " 'strix varia': 1,\n",
-       " 'butorides virescens': 1,\n",
-       " 'falco sparverius': 4,\n",
-       " 'ardea herodias': 1,\n",
-       " 'falco tinnunculus': 2,\n",
-       " 'hydrobates pelagicus': 1,\n",
-       " 'asio flammeus': 8,\n",
-       " 'anous stolidus': 1,\n",
-       " 'caloenas nicobarica': 1,\n",
-       " 'calcinus tubularis': 3,\n",
-       " 'urocyon littoralis': 8109,\n",
-       " 'antilocapra americana': 154,\n",
-       " 'ovis canadensis': 19,\n",
-       " 'pica hudsonia': 1,\n",
-       " 'ixoreus naevius': 1,\n",
-       " 'sitta canadensis': 1,\n",
-       " 'piranga ludoviciana': 1,\n",
-       " 'pekania pennanti': 11,\n",
-       " 'nucifraga columbiana': 1,\n",
-       " 'damaliscus lunatus': 4233,\n",
-       " 'felis lybica': 114,\n",
-       " 'parahyaena brunnea': 42,\n",
-       " 'equus zebra': 2297,\n",
-       " 'oryx gazella': 1552,\n",
-       " 'antidorcas marsupialis': 1318,\n",
-       " 'pelea capreolus': 54,\n",
-       " 'redunca fulvorufula': 101,\n",
-       " 'vulpes chama': 21,\n",
-       " 'bunolagus monticularis': 8,\n",
-       " 'neotis ludwigii': 18,\n",
-       " 'herpestes pulverulentus': 1,\n",
-       " 'suricata suricatta': 15,\n",
-       " 'cynictis penicillata': 9,\n",
-       " 'lepus capensis': 1,\n",
-       " 'lepus victoriae': 12,\n",
-       " 'ourebia ourebi': 2,\n",
-       " 'connochaetes gnou': 493,\n",
-       " 'damaliscus pygargus': 447,\n",
-       " 'anthropoides paradiseus': 10,\n",
-       " 'pedetes capensis': 17,\n",
-       " 'dama dama': 25,\n",
-       " 'herpestes ichneumon': 5,\n",
-       " 'pronolagus randensis': 8,\n",
-       " 'tragelaphus angasii': 10,\n",
-       " 'cephalophus natalensis': 2,\n",
-       " 'hippotragus equinus': 1,\n",
-       " 'crocodylus niloticus': 1,\n",
-       " 'urocissa whiteheadi': 7,\n",
-       " 'turdus cardis': 66,\n",
-       " 'garrulax maesi': 3,\n",
-       " 'picus rabieri': 6,\n",
-       " 'erythrogenys hypoleucos': 6,\n",
-       " 'garrulax castanotis': 117,\n",
-       " 'pygathrix nemaeus': 258,\n",
-       " 'nesolagus timminsi': 1657,\n",
-       " 'cinclidium frontale': 6,\n",
-       " 'harpactes oreskios': 2,\n",
-       " 'moschiola meminna': 527,\n",
-       " 'hydrornis elliotii': 39,\n",
-       " 'cissa hypoleuca': 3,\n",
-       " 'trachypithecus francoisi': 2,\n",
-       " 'rheinardia ocellata': 172,\n",
-       " 'varanus bengalensis': 3,\n",
-       " 'trachypithecus hatinhensis': 5,\n",
-       " 'trachypithecus phayrei': 29,\n",
-       " 'aceros nipalensis': 2,\n",
-       " 'strix leptogrammica': 101,\n",
-       " 'pterorhinus pectoralis': 112,\n",
-       " 'ducula badia': 10,\n",
-       " 'ianthocincla konkakinhensis': 2,\n",
-       " 'picus chlorolophus': 3,\n",
-       " 'geokichla sibirica': 61,\n",
-       " 'irena puella': 2,\n",
-       " 'pterorhinus chinensis': 2,\n",
-       " 'cyanoderma chrysaeum': 6,\n",
-       " 'macropygia unchall': 2,\n",
-       " 'myiomela leucura': 2,\n",
-       " 'turdus obscurus': 25,\n",
-       " 'culicicapa ceylonensis': 3,\n",
-       " 'plecturocebus ornatus': 16,\n",
-       " 'iguana iguana': 7,\n",
-       " 'dasyprocta guamara': 1,\n",
-       " 'cathartes aura': 1,\n",
-       " 'penelope jacucaca': 1,\n",
-       " 'tigrisoma fasciatum': 2}"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "num_singleSpecies_dict = {}\n",
-    "for species in species_single_seq.species.unique():\n",
-    "    num_species = len(species_single_seq.loc[species_single_seq.species == species])\n",
-    "    num_singleSpecies_dict[species] = num_species\n",
-    "num_singleSpecies_dict"
+    "lin_taxa = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n",
+    "all_taxa = ['kingdom',\n",
+    "            'phylum',\n",
+    "            'subphylum',\n",
+    "            'superclass',\n",
+    "            'class',\n",
+    "            'subclass',\n",
+    "            'infraclass',\n",
+    "            'superorder',\n",
+    "            'order',\n",
+    "            'suborder',\n",
+    "            'infraorder',\n",
+    "            'superfamily',\n",
+    "            'family',\n",
+    "            'subfamily',\n",
+    "            'tribe',\n",
+    "            'genus',\n",
+    "            'species',\n",
+    "            'subspecies',\n",
+    "            'variety']"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 31,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We have  244  species for which there are at least 40 images.\n",
-      "\n",
-      "We have  302  species for which there are at least 20 images.\n"
-     ]
-    }
-   ],
    "source": [
-    "more_singleSamples = {}\n",
-    "for species, num in num_singleSpecies_dict.items():\n",
-    "    if num >= 40:\n",
-    "        more_singleSamples[species] = num\n",
-    "\n",
-    "print(\"We have \", len(more_singleSamples), \" species for which there are at least 40 images.\")\n",
-    "print()\n",
-    "\n",
-    "mid_singleSamples = {}\n",
-    "for species, num in num_singleSpecies_dict.items():\n",
-    "    if num >= 20:\n",
-    "        mid_singleSamples[species] = num\n",
-    "\n",
-    "print(\"We have \", len(mid_singleSamples), \" species for which there are at least 20 images.\")"
+    "#### How many have all 7 Linnean ranks?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/2855569829.py:3: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  species_single_seq.loc[species_single_seq.species == species, 'num_singleSpecies'] = num\n"
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 7521712 entries, 2 to 19351155\n",
+      "Data columns (total 19 columns):\n",
+      " #   Column       Non-Null Count    Dtype \n",
+      "---  ------       --------------    ----- \n",
+      " 0   kingdom      7521712 non-null  object\n",
+      " 1   phylum       7521712 non-null  object\n",
+      " 2   subphylum    7521548 non-null  object\n",
+      " 3   superclass   59 non-null       object\n",
+      " 4   class        7521712 non-null  object\n",
+      " 5   subclass     6917435 non-null  object\n",
+      " 6   infraclass   6917376 non-null  object\n",
+      " 7   superorder   6717750 non-null  object\n",
+      " 8   order        7521712 non-null  object\n",
+      " 9   suborder     5374157 non-null  object\n",
+      " 10  infraorder   481338 non-null   object\n",
+      " 11  superfamily  324711 non-null   object\n",
+      " 12  family       7521712 non-null  object\n",
+      " 13  subfamily    5857614 non-null  object\n",
+      " 14  tribe        4854254 non-null  object\n",
+      " 15  genus        7521712 non-null  object\n",
+      " 16  species      7521712 non-null  object\n",
+      " 17  subspecies   74052 non-null    object\n",
+      " 18  variety      2050 non-null     object\n",
+      "dtypes: object(19)\n",
+      "memory usage: 1.1+ GB\n"
      ]
     }
    ],
    "source": [
-    "#Add the counts to the dataset for filtering\n",
-    "for species, num in num_singleSpecies_dict.items():\n",
-    "    species_single_seq.loc[species_single_seq.species == species, 'num_singleSpecies'] = num"
+    "df_all_taxa = df_cleaned.dropna(subset = lin_taxa)\n",
+    "df_all_taxa[all_taxa].info(show_counts = True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 33,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "total_more_images = 0\n",
-    "for species, num in more_singleSamples.items():\n",
-    "    total_more_images += num\n",
+    "That's pretty good coverage: 7,521,712 out of 10,965,902. It looks like many of them also have the other taxonomic ranks too. Now how many different 7-tuples are there?\n",
     "\n",
-    "total_mid_images = 0\n",
-    "for species, num in mid_singleSamples.items():\n",
-    "    total_mid_images += num"
+    "#### How many unique 7-tuples?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With a 20 image per species cutoff, we have  953700 total images.\n",
-      "With a 40 image per species cutoff, we have  952134 total images.\n"
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 891 entries, 1 to 19350355\n",
+      "Data columns (total 35 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   dataset_name      891 non-null    object \n",
+      " 1   url_gcp           891 non-null    object \n",
+      " 2   url_aws           891 non-null    object \n",
+      " 3   url_azure         891 non-null    object \n",
+      " 4   image_id          891 non-null    object \n",
+      " 5   sequence_id       891 non-null    object \n",
+      " 6   location_id       891 non-null    object \n",
+      " 7   frame_num         891 non-null    int64  \n",
+      " 8   original_label    891 non-null    object \n",
+      " 9   scientific_name   890 non-null    object \n",
+      " 10  common_name       890 non-null    object \n",
+      " 11  datetime          812 non-null    object \n",
+      " 12  annotation_level  891 non-null    object \n",
+      " 13  kingdom           890 non-null    object \n",
+      " 14  phylum            889 non-null    object \n",
+      " 15  subphylum         886 non-null    object \n",
+      " 16  superclass        2 non-null      object \n",
+      " 17  class             888 non-null    object \n",
+      " 18  subclass          445 non-null    object \n",
+      " 19  infraclass        441 non-null    object \n",
+      " 20  superorder        432 non-null    object \n",
+      " 21  order             883 non-null    object \n",
+      " 22  suborder          249 non-null    object \n",
+      " 23  infraorder        73 non-null     object \n",
+      " 24  superfamily       56 non-null     object \n",
+      " 25  family            870 non-null    object \n",
+      " 26  subfamily         338 non-null    object \n",
+      " 27  tribe             168 non-null    object \n",
+      " 28  genus             829 non-null    object \n",
+      " 29  species           739 non-null    object \n",
+      " 30  subspecies        5 non-null      object \n",
+      " 31  variety           1 non-null      object \n",
+      " 32  multi_species     891 non-null    bool   \n",
+      " 33  num_species       891 non-null    float64\n",
+      " 34  lin_duplicate     891 non-null    bool   \n",
+      "dtypes: bool(2), float64(1), int64(1), object(31)\n",
+      "memory usage: 238.4+ KB\n"
      ]
     }
    ],
    "source": [
-    "print(\"With a 20 image per species cutoff, we have \", total_mid_images, \"total images.\")\n",
-    "print(\"With a 40 image per species cutoff, we have \", total_more_images, \"total images.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the family distribution for these."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "sns.set_style(\"whitegrid\")\n",
-    "sns.set(rc = {'figure.figsize': (10,10)})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Count', ylabel='family'>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.histplot(species_single_seq.loc[species_single_seq.num_singleSpecies >= 40], y = 'family')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Count', ylabel='family'>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.histplot(species_single_seq.loc[species_single_seq.num_singleSpecies >= 20], y = 'family')"
+    "#number of unique 7-tuples in full dataset\n",
+    "df_cleaned['lin_duplicate'] = df_cleaned.duplicated(subset = lin_taxa, keep = 'first')\n",
+    "df_unique_lin_taxa = df_cleaned.loc[~df_cleaned['lin_duplicate']].copy()\n",
+    "df_unique_lin_taxa.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Check for images marked as having animal that are actually empty \n",
-    "This can occur when images are labeled as a sequence, and it is suggested that it is not an unusual occurance. We will use the [MegaDetector results](https://lila.science/megadetector-results-for-camera-trap-datasets/) provided by LILA BC to run a check for these. Ultimately, we won't use repeated instances of the same animal in the same sequence so the process of removing extra instances may also alleviate this issue. Additionally, it's worth noting that MegaDector is trained to err on the side of detection (i.e., it's more likely to see an animal that's not there), so not likely to change our assessment.\n",
-    "\n",
-    "#### Caltech Camera Traps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Look up a couple sample file values in the model results JSON (with and without detection)."
+    "Interesting, we have 891 unique 7-tuple taxonomic strings, but 1 scientific and common name seem to be missing.\n",
+    "What's the uniqueness count here?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
-       "      <th>image_id</th>\n",
-       "      <th>sequence_id</th>\n",
-       "      <th>location_id</th>\n",
-       "      <th>frame_num</th>\n",
-       "      <th>original_label</th>\n",
-       "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>...</th>\n",
-       "      <th>suborder</th>\n",
-       "      <th>infraorder</th>\n",
-       "      <th>superfamily</th>\n",
-       "      <th>family</th>\n",
-       "      <th>subfamily</th>\n",
-       "      <th>tribe</th>\n",
-       "      <th>genus</th>\n",
-       "      <th>species</th>\n",
-       "      <th>subspecies</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>158597</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5862934b-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6fa348e3-5567-11e8-be87...</td>\n",
-       "      <td>Caltech Camera Traps : 96</td>\n",
-       "      <td>1</td>\n",
-       "      <td>empty</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>05-08-2014 13:23:40</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 30 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                dataset_name   \n",
-       "158597  Caltech Camera Traps  \\\n",
-       "\n",
-       "                                                      url   \n",
-       "158597  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "\n",
-       "                                                 image_id   \n",
-       "158597  Caltech Camera Traps : 5862934b-23d2-11e8-a6a3...  \\\n",
-       "\n",
-       "                                              sequence_id   \n",
-       "158597  Caltech Camera Traps : 6fa348e3-5567-11e8-be87...  \\\n",
-       "\n",
-       "                      location_id  frame_num original_label scientific_name   \n",
-       "158597  Caltech Camera Traps : 96          1          empty             NaN  \\\n",
-       "\n",
-       "       common_name             datetime  ... suborder infraorder superfamily   \n",
-       "158597         NaN  05-08-2014 13:23:40  ...      NaN        NaN         NaN  \\\n",
-       "\n",
-       "       family subfamily tribe genus species subspecies variety  \n",
-       "158597    NaN       NaN   NaN   NaN     NaN        NaN     NaN  \n",
-       "\n",
-       "[1 rows x 30 columns]"
+       "dataset_name         20\n",
+       "url_gcp             891\n",
+       "url_aws             891\n",
+       "url_azure           891\n",
+       "image_id            891\n",
+       "sequence_id         661\n",
+       "location_id         620\n",
+       "frame_num            12\n",
+       "original_label      885\n",
+       "scientific_name     890\n",
+       "common_name         886\n",
+       "datetime            806\n",
+       "annotation_level      3\n",
+       "kingdom               1\n",
+       "phylum                2\n",
+       "subphylum             5\n",
+       "superclass            1\n",
+       "class                 8\n",
+       "subclass              3\n",
+       "infraclass            2\n",
+       "superorder            5\n",
+       "order                58\n",
+       "suborder             16\n",
+       "infraorder            9\n",
+       "superfamily          11\n",
+       "family              187\n",
+       "subfamily            71\n",
+       "tribe                46\n",
+       "genus               538\n",
+       "species             739\n",
+       "subspecies            5\n",
+       "variety               1\n",
+       "multi_species         2\n",
+       "num_species           3\n",
+       "lin_duplicate         1\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.loc[df['url'].str.contains(\"cct_images/5862934b-23d2-11e8-a6a3-ec086b02610b\")] "
+    "df_unique_lin_taxa.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They're across all datasets. We have 890 unique scientific names and 886 unique common names (from 885 original labels)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -5730,19 +2237,16 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
        "      <th>image_id</th>\n",
        "      <th>sequence_id</th>\n",
        "      <th>location_id</th>\n",
        "      <th>frame_num</th>\n",
        "      <th>original_label</th>\n",
        "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
        "      <th>...</th>\n",
-       "      <th>suborder</th>\n",
-       "      <th>infraorder</th>\n",
-       "      <th>superfamily</th>\n",
        "      <th>family</th>\n",
        "      <th>subfamily</th>\n",
        "      <th>tribe</th>\n",
@@ -5750,21 +2254,24 @@
        "      <th>species</th>\n",
        "      <th>subspecies</th>\n",
        "      <th>variety</th>\n",
+       "      <th>multi_species</th>\n",
+       "      <th>num_species</th>\n",
+       "      <th>lin_duplicate</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>183932</th>\n",
+       "      <th>5</th>\n",
        "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5862934c-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6f6b872e-5567-11e8-854c...</td>\n",
-       "      <td>Caltech Camera Traps : 106</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 5a096955-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 70096335-5567-11e8-a99a...</td>\n",
+       "      <td>Caltech Camera Traps : 36</td>\n",
        "      <td>1</td>\n",
-       "      <td>empty</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>car</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>12-21-2013 10:00:00</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -5773,5805 +2280,243 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1 rows × 30 columns</p>\n",
+       "<p>1 rows × 35 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                dataset_name   \n",
-       "183932  Caltech Camera Traps  \\\n",
+       "           dataset_name                                            url_gcp  \\\n",
+       "5  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                             url_aws  \\\n",
+       "5  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
        "\n",
-       "                                                      url   \n",
-       "183932  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
+       "                                           url_azure  \\\n",
+       "5  https://lilawildlife.blob.core.windows.net/lil...   \n",
        "\n",
-       "                                                 image_id   \n",
-       "183932  Caltech Camera Traps : 5862934c-23d2-11e8-a6a3...  \\\n",
+       "                                            image_id  \\\n",
+       "5  Caltech Camera Traps : 5a096955-23d2-11e8-a6a3...   \n",
        "\n",
-       "                                              sequence_id   \n",
-       "183932  Caltech Camera Traps : 6f6b872e-5567-11e8-854c...  \\\n",
+       "                                         sequence_id  \\\n",
+       "5  Caltech Camera Traps : 70096335-5567-11e8-a99a...   \n",
        "\n",
-       "                       location_id  frame_num original_label scientific_name   \n",
-       "183932  Caltech Camera Traps : 106          1          empty             NaN  \\\n",
+       "                 location_id  frame_num original_label scientific_name  ...  \\\n",
+       "5  Caltech Camera Traps : 36          1            car             NaN  ...   \n",
        "\n",
-       "       common_name             datetime  ... suborder infraorder superfamily   \n",
-       "183932         NaN  12-21-2013 10:00:00  ...      NaN        NaN         NaN  \\\n",
+       "  family subfamily tribe genus species subspecies variety multi_species  \\\n",
+       "5    NaN       NaN   NaN   NaN     NaN        NaN     NaN         False   \n",
        "\n",
-       "       family subfamily tribe genus species subspecies variety  \n",
-       "183932    NaN       NaN   NaN   NaN     NaN        NaN     NaN  \n",
+       "  num_species lin_duplicate  \n",
+       "5         1.0         False  \n",
        "\n",
-       "[1 rows x 30 columns]"
+       "[1 rows x 35 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.loc[df['url'].str.contains(\"cct_images/5862934c-23d2-11e8-a6a3-ec086b02610b.jpg\")] "
+    "df_unique_lin_taxa.loc[(df_unique_lin_taxa[\"scientific_name\"].isna()) | (df_unique_lin_taxa[\"common_name\"].isna())]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's a car...We need to remove cars..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dataset_name</th>\n",
-       "      <th>url</th>\n",
-       "      <th>image_id</th>\n",
-       "      <th>sequence_id</th>\n",
-       "      <th>location_id</th>\n",
-       "      <th>frame_num</th>\n",
-       "      <th>original_label</th>\n",
-       "      <th>scientific_name</th>\n",
-       "      <th>common_name</th>\n",
-       "      <th>datetime</th>\n",
-       "      <th>...</th>\n",
-       "      <th>suborder</th>\n",
-       "      <th>infraorder</th>\n",
-       "      <th>superfamily</th>\n",
-       "      <th>family</th>\n",
-       "      <th>subfamily</th>\n",
-       "      <th>tribe</th>\n",
-       "      <th>genus</th>\n",
-       "      <th>species</th>\n",
-       "      <th>subspecies</th>\n",
-       "      <th>variety</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>125919</th>\n",
-       "      <td>Caltech Camera Traps</td>\n",
-       "      <td>https://lilablobssc.blob.core.windows.net/calt...</td>\n",
-       "      <td>Caltech Camera Traps : 5874d5d3-23d2-11e8-a6a3...</td>\n",
-       "      <td>Caltech Camera Traps : 6ff2f0a6-5567-11e8-9187...</td>\n",
-       "      <td>Caltech Camera Traps : 114</td>\n",
-       "      <td>1</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>coyote</td>\n",
-       "      <td>09-28-2013 04:54:29</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canidae</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>canis</td>\n",
-       "      <td>canis latrans</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 30 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                dataset_name   \n",
-       "125919  Caltech Camera Traps  \\\n",
-       "\n",
-       "                                                      url   \n",
-       "125919  https://lilablobssc.blob.core.windows.net/calt...  \\\n",
-       "\n",
-       "                                                 image_id   \n",
-       "125919  Caltech Camera Traps : 5874d5d3-23d2-11e8-a6a3...  \\\n",
-       "\n",
-       "                                              sequence_id   \n",
-       "125919  Caltech Camera Traps : 6ff2f0a6-5567-11e8-9187...  \\\n",
-       "\n",
-       "                       location_id  frame_num original_label scientific_name   \n",
-       "125919  Caltech Camera Traps : 114          1         coyote   canis latrans  \\\n",
-       "\n",
-       "       common_name             datetime  ... suborder infraorder superfamily   \n",
-       "125919      coyote  09-28-2013 04:54:29  ...      NaN        NaN         NaN  \\\n",
-       "\n",
-       "         family subfamily tribe  genus        species subspecies variety  \n",
-       "125919  canidae       NaN   NaN  canis  canis latrans        NaN     NaN  \n",
-       "\n",
-       "[1 rows x 30 columns]"
+       "(4717, 36)"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.loc[df['url'].str.contains(\"cct_images/5874d5d3-23d2-11e8-a6a3-ec086b02610b.jpg\")] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
+    "df_cleaned.loc[df_cleaned[\"original_label\"] == \"car\"].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "dataset_name\n",
-       "NACTI                               2715543\n",
-       "Snapshot Serengeti                  1576449\n",
-       "SWG Camera Traps                     836448\n",
-       "WCS Camera Traps                     723069\n",
-       "Idaho Camera Traps                   189095\n",
-       "Orinoquia Camera Traps                72373\n",
-       "Caltech Camera Traps                  67690\n",
-       "Channel Islands Camera Traps          50183\n",
-       "Wellington Camera Traps               46871\n",
-       "Missouri Camera Traps                 20216\n",
-       "Island Conservation Camera Traps      17622\n",
-       "Snapshot Camdeboo                     16241\n",
-       "ENA24                                  8904\n",
-       "Snapshot Enonkishu                     8002\n",
-       "Snapshot Mountain Zebra                5735\n",
-       "Snapshot Karoo                         5706\n",
-       "Snapshot Kruger                        3752\n",
-       "Snapshot Kgalagadi                     2086\n",
+       "Caltech Camera Traps    4717\n",
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dedupe_species.dataset_name.value_counts()"
+    "df_cleaned.loc[df_cleaned[\"original_label\"] == \"car\", \"dataset_name\"].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Most of our images are comingi from NACTI and Snapshot Serengeti, so those may be the more important to focus on, but good to check through. Those two will also take longer. Let's start with the smaller ones to get some preliminary checks. As noted above, it's not likely to change our results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mdv5b_files = {\"Caltech Camera Traps\": \"caltech-camera-traps_mdv5a.0.0_results.json\",\n",
-    "               \"Channel Islands Camera Traps\": \"channel-islands-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"ENA24\": \"ena24_mdv5b.0.0_results.json\",\n",
-    "               \"Idaho Camera Traps\": \"idaho-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"Island Conservation Camera Traps\": \"island-conservation-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"Missouri Camera Traps\": \"missouri-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"Orinoquia Camera Traps\": \"orinoquia-camera-traps_public_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Camdeboo\": \"snapshot-safari_CDB_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Enonkishu\": \"snapshot-safari_ENO_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Karoo\": \"snapshot-safari_KAR_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Kgalagadi\": \"snapshot-safari_KGA_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Kruger\": \"snapshot-safari_KRU_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Mountain Zebra\": \"snapshot-safari_MTZ_mdv5b.0.0_results.json\",\n",
-    "               \"SWG Camera Traps\": \"swg-camera-traps_public_mdv5b.0.0_results.json\",\n",
-    "               \"WCS Camera Traps\": \"wcs-camera-traps_animals_mdv5b.0.0_results.json\",\n",
-    "               \"Wellington Camera Traps\": \"wellington-camera-traps_images_mdv5b.0.0_results.json\"}"
+    "#### How many unique full taxa (sub ranks included)?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 909 entries, 1 to 19350355\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   dataset_name      909 non-null    object \n",
+      " 1   url_gcp           909 non-null    object \n",
+      " 2   url_aws           909 non-null    object \n",
+      " 3   url_azure         909 non-null    object \n",
+      " 4   image_id          909 non-null    object \n",
+      " 5   sequence_id       909 non-null    object \n",
+      " 6   location_id       909 non-null    object \n",
+      " 7   frame_num         909 non-null    int64  \n",
+      " 8   original_label    909 non-null    object \n",
+      " 9   scientific_name   908 non-null    object \n",
+      " 10  common_name       908 non-null    object \n",
+      " 11  datetime          827 non-null    object \n",
+      " 12  annotation_level  909 non-null    object \n",
+      " 13  kingdom           908 non-null    object \n",
+      " 14  phylum            907 non-null    object \n",
+      " 15  subphylum         904 non-null    object \n",
+      " 16  superclass        2 non-null      object \n",
+      " 17  class             906 non-null    object \n",
+      " 18  subclass          458 non-null    object \n",
+      " 19  infraclass        453 non-null    object \n",
+      " 20  superorder        444 non-null    object \n",
+      " 21  order             899 non-null    object \n",
+      " 22  suborder          256 non-null    object \n",
+      " 23  infraorder        74 non-null     object \n",
+      " 24  superfamily       58 non-null     object \n",
+      " 25  family            882 non-null    object \n",
+      " 26  subfamily         346 non-null    object \n",
+      " 27  tribe             172 non-null    object \n",
+      " 28  genus             837 non-null    object \n",
+      " 29  species           747 non-null    object \n",
+      " 30  subspecies        12 non-null     object \n",
+      " 31  variety           1 non-null      object \n",
+      " 32  multi_species     909 non-null    bool   \n",
+      " 33  num_species       909 non-null    float64\n",
+      " 34  lin_duplicate     909 non-null    bool   \n",
+      " 35  full_duplicate    909 non-null    bool   \n",
+      "dtypes: bool(3), float64(1), int64(1), object(31)\n",
+      "memory usage: 244.1+ KB\n"
+     ]
+    }
+   ],
    "source": [
-    "def filter_md_results(dataset_name, filename):\n",
-    "    with open(\"../MegaDetector_results/\" + filename) as file:\n",
-    "        data = json.load(file)\n",
-    "    df_mdv5b = pd.json_normalize(data[\"images\"], max_level = 1)\n",
-    "    print(df_mdv5b.head())\n",
-    "    dedupe_url = list(dedupe_species.loc[dedupe_species[\"dataset_name\"] == dataset_name, 'url'])\n",
-    "    dedupe_url_empties = []\n",
-    "    for file in list(df_mdv5b.loc[(df_mdv5b['max_detection_conf'] <= 80 & df_mdv5b['detections'].astype(str) != '[]'), 'file']):\n",
-    "        if file in dedupe_url:\n",
-    "            dedupe_url_empties.append(file)\n",
-    "    print(dataset_name, \": \", dedupe_url_empties)\n",
-    "    return dedupe_url_empties"
+    "#number of unique 7-tuples in full dataset\n",
+    "df_cleaned['full_duplicate'] = df_cleaned.duplicated(subset = all_taxa, keep = 'first')\n",
+    "df_unique_all_taxa = df_cleaned.loc[~df_cleaned['full_duplicate']].copy()\n",
+    "df_unique_all_taxa.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Earlier check on `[]`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"../MegaDetector_results/caltech-camera-traps_mdv5a.0.0_results.json\") as file:\n",
-    "    data = json.load(file)"
+    "When we consider the sub-ranks as well we wind up with 909 unique taxa (still with one scientific and common name missing--the car!)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'images': [{'file': 'cct_images/586ae111-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.893,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.893,\n",
-       "     'bbox': [0.6577, 0.759, 0.2148, 0.2322]}]},\n",
-       "  {'file': 'cct_images/586ae112-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.56, 0.4839, 0.1425, 0.3253]}]},\n",
-       "  {'file': 'cct_images/586ae113-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.724,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.724,\n",
-       "     'bbox': [0.8569, 0.5033, 0.1406, 0.1137]}]},\n",
-       "  {'file': 'cct_images/586ae114-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.918,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.918,\n",
-       "     'bbox': [0.5332, 0.4424, 0.4648, 0.5562]}]},\n",
-       "  {'file': 'cct_images/586ae115-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.815,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.815,\n",
-       "     'bbox': [0.2504, 0.5247, 0.08447, 0.1204]}]},\n",
-       "  {'file': 'cct_images/586ae117-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/586ae118-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.691,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.691,\n",
-       "     'bbox': [0.5546, 0.4303, 0.08251, 0.06358]}]},\n",
-       "  {'file': 'cct_images/586ae119-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.798,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.122,\n",
-       "     'bbox': [0.0009765, 0.0174, 0.9902, 0.9825]},\n",
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-       "     'conf': 0.798,\n",
-       "     'bbox': [0.8002, 0.356, 0.09179, 0.1465]}]},\n",
-       "  {'file': 'cct_images/586ae11a-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.2563, 0.1271, 0.5996, 0.6151]}]},\n",
-       "  {'file': 'cct_images/586ae11c-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/586ae11d-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.0009765, 0.4752, 0.2377, 0.3212]}]},\n",
-       "  {'file': 'cct_images/586ae11e-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/585a65e3-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0, 0.01874, 0.2631, 0.9779]},\n",
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-       "    {'category': '1', 'conf': 0.976, 'bbox': [0, 0.02008, 0.8706, 0.9799]}]},\n",
-       "  {'file': 'cct_images/585a65e6-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.4672, 0.4377, 0.3613, 0.2182]}]},\n",
-       "  {'file': 'cct_images/585a65e8-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0217,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0217,\n",
-       "     'bbox': [0.5278, 0.4163, 0.0581, 0.05555]}]},\n",
-       "  {'file': 'cct_images/585a65e9-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.872,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.872,\n",
-       "     'bbox': [0.4907, 0.7409, 0.1503, 0.1231]}]},\n",
-       "  {'file': 'cct_images/585a65ea-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.905,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.905,\n",
-       "     'bbox': [0.1552, 0.8038, 0.4003, 0.1954]}]},\n",
-       "  {'file': 'cct_images/585a65ec-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.881,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.881,\n",
-       "     'bbox': [0.1855, 0.6164, 0.1767, 0.2389]}]},\n",
-       "  {'file': 'cct_images/585a65ed-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0226,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0159,\n",
-       "     'bbox': [0.1503, 0.6398, 0.04345, 0.05087]},\n",
-       "    {'category': '2',\n",
-       "     'conf': 0.0226,\n",
-       "     'bbox': [0.1499, 0.6385, 0.04394, 0.05421]}]},\n",
-       "  {'file': 'cct_images/585a65ee-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0274,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0274,\n",
-       "     'bbox': [0.04394, 0.6385, 0.06933, 0.07965]}]},\n",
-       "  {'file': 'cct_images/585a65ef-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.02,\n",
-       "   'detections': [{'category': '1',\n",
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-       "  {'file': 'cct_images/585a65f0-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0289,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0289,\n",
-       "     'bbox': [0, 0.6405, 0.09765, 0.188]}]},\n",
-       "  {'file': 'cct_images/585a65f1-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.887,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.887,\n",
-       "     'bbox': [0.5507, 0.4999, 0.1728, 0.1124]}]},\n",
-       "  {'file': 'cct_images/585a65f2-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': -1,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/585a65f3-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.706,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.706,\n",
-       "     'bbox': [0.003417, 0.3681, 0.7211, 0.6311]}]},\n",
-       "  {'file': 'cct_images/585a65f4-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.904,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.904,\n",
-       "     'bbox': [0.3637, 0.6519, 0.1489, 0.2637]}]},\n",
-       "  {'file': 'cct_images/585a65f5-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.923,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0102,\n",
-       "     'bbox': [0, 0.6111, 0.04394, 0.1378]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0132,\n",
-       "     'bbox': [0.4775, 0.4049, 0.06689, 0.07965]},\n",
-       "    {'category': '1', 'conf': 0.0354, 'bbox': [0, 0.8152, 0.01953, 0.1834]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.923,\n",
-       "     'bbox': [0.3666, 0.3199, 0.1015, 0.1519]}]},\n",
-       "  {'file': 'cct_images/585a65f6-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/585a65f7-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.616,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.616,\n",
-       "     'bbox': [0.395, 0.51, 0.1069, 0.182]}]},\n",
-       "  {'file': 'cct_images/585a65f8-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.89,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.89,\n",
-       "     'bbox': [0.0004882, 0.7342, 0.3432, 0.2248]}]},\n",
-       "  {'file': 'cct_images/585a65f9-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.848,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.848,\n",
-       "     'bbox': [0.3212, 0.7336, 0.1083, 0.17]}]},\n",
-       "  {'file': 'cct_images/585a65fa-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0123,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0123,\n",
-       "     'bbox': [0.9672, 0.4892, 0.03271, 0.1331]}]},\n",
-       "  {'file': 'cct_images/585a65fb-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.886,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0179,\n",
-       "     'bbox': [0.07519, 0.7095, 0.02929, 0.03882]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0216,\n",
-       "     'bbox': [0.06054, 0.6981, 0.1157, 0.1278]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0261,\n",
-       "     'bbox': [0.08398, 0.7208, 0.01855, 0.02409]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.189,\n",
-       "     'bbox': [0.0004882, 0.6854, 0.1059, 0.1278]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.886,\n",
-       "     'bbox': [0.5966, 0.7784, 0.1909, 0.2202]}]},\n",
-       "  {'file': 'cct_images/58732be6-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/58732be7-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': -1,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/58732be9-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/58732bea-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '3',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0, 0.000651, 1.0, 0.7089]}]},\n",
-       "  {'file': 'cct_images/58732beb-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0663,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0663,\n",
-       "     'bbox': [0.4619, 0.925, 0.08251, 0.07429]}]},\n",
-       "  {'file': 'cct_images/58732bec-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.04687, 0.767, 0.2382, 0.2322]}]},\n",
-       "  {'file': 'cct_images/58732bed-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.854,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.854,\n",
-       "     'bbox': [0.5151, 0.4631, 0.0996, 0.2269]}]},\n",
-       "  {'file': 'cct_images/58732bee-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.278,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0626,\n",
-       "     'bbox': [0.6396, 0.3406, 0.03857, 0.0676]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.278,\n",
-       "     'bbox': [0.6552, 0.34, 0.02343, 0.0522]}]},\n",
-       "  {'file': 'cct_images/58732bef-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0146,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0146,\n",
-       "     'bbox': [0.7915, 0.5742, 0.02294, 0.03212]}]},\n",
-       "  {'file': 'cct_images/58732bf2-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0119,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0119,\n",
-       "     'bbox': [0.04492, 0.6432, 0.06005, 0.06492]}]},\n",
-       "  {'file': 'cct_images/58732bf3-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '3',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0, 0.000651, 1.0, 0.9667]}]},\n",
-       "  {'file': 'cct_images/5897b214-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '2',\n",
-       "     'conf': 0.0172,\n",
-       "     'bbox': [0.9423, 0.6941, 0.05761, 0.1412]},\n",
-       "    {'category': '2',\n",
-       "     'conf': 0.0254,\n",
-       "     'bbox': [0.9663, 0.01941, 0.03369, 0.2831]},\n",
-       "    {'category': '2',\n",
-       "     'conf': 0.17,\n",
-       "     'bbox': [0.9414, 0.02208, 0.05859, 0.8105]},\n",
-       "    {'category': '2',\n",
-       "     'conf': 0.238,\n",
-       "     'bbox': [0.8154, 0.02074, 0.1845, 0.2844]},\n",
-       "    {'category': '2', 'conf': 0.831, 'bbox': [0.8159, 0.02208, 0.184, 0.8099]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.09765, 0.3929, 0.1235, 0.2336]}]},\n",
-       "  {'file': 'cct_images/5897b215-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.728,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.728,\n",
-       "     'bbox': [0.3793, 0.516, 0.2548, 0.1579]}]},\n",
-       "  {'file': 'cct_images/5897b216-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/5897b217-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2402, 0.5682, 0.7578, 0.4303]}]},\n",
-       "  {'file': 'cct_images/5897b219-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0954,\n",
-       "     'bbox': [0.83, 0.6124, 0.03076, 0.03748]}]},\n",
-       "  {'file': 'cct_images/5897b21b-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.816,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.816,\n",
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-       "  {'file': 'cct_images/585c0539-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
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-       "   'detections': []},\n",
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-       "   'detections': []},\n",
-       "  {'file': 'cct_images/585c0375-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
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-       "     'bbox': [0.5185, 0.4538, 0.1489, 0.08634]}]},\n",
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-       "   'detections': []},\n",
-       "  {'file': 'cct_images/585c0382-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': -1,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/585c0383-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': -1,\n",
-       "   'detections': []},\n",
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-       "  {'file': 'cct_images/5864352e-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
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-       "    {'category': '1',\n",
-       "     'conf': 0.0194,\n",
-       "     'bbox': [0.8403, 0.9672, 0.1591, 0.03212]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0209,\n",
-       "     'bbox': [0.8393, 0.9665, 0.02636, 0.03346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0434,\n",
-       "     'bbox': [0.8759, 0.7074, 0.04492, 0.04953]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0537,\n",
-       "     'bbox': [0.7597, 0.6291, 0.06835, 0.05287]}]},\n",
-       "  {'file': 'cct_images/5879d4ef-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.664,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0138,\n",
-       "     'bbox': [0.9775, 0.4912, 0.0205, 0.1405]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.664,\n",
-       "     'bbox': [0.7084, 0.4939, 0.1113, 0.2838]}]},\n",
-       "  {'file': 'cct_images/5879d4f0-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.045,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0125,\n",
-       "     'bbox': [0.5415, 0.7771, 0.4584, 0.2228]},\n",
-       "    {'category': '1', 'conf': 0.0315, 'bbox': [0.54, 0.9337, 0.1538, 0.06559]},\n",
-       "    {'category': '1', 'conf': 0.045, 'bbox': [0.539, 0.9149, 0.4609, 0.085]}]},\n",
-       "  {'file': 'cct_images/5879d4f1-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.188,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.188,\n",
-       "     'bbox': [0.413, 0.9176, 0.07128, 0.08165]}]},\n",
-       "  {'file': 'cct_images/5879d4f2-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0423,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0423,\n",
-       "     'bbox': [0.9023, 0.8045, 0.09765, 0.08768]}]},\n",
-       "  {'file': 'cct_images/5879d4f3-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0144,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0144,\n",
-       "     'bbox': [0, 0.8139, 0.01464, 0.1827]}]},\n",
-       "  {'file': 'cct_images/5879d4f4-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.884,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.884,\n",
-       "     'bbox': [0.3906, 0.4953, 0.187, 0.3085]}]},\n",
-       "  {'file': 'cct_images/5879d4f5-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.858,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.858,\n",
-       "     'bbox': [0.4418, 0.5816, 0.185, 0.2222]}]},\n",
-       "  {'file': 'cct_images/5879d4f6-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.8,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.8,\n",
-       "     'bbox': [0.3759, 0.6586, 0.08105, 0.07898]}]},\n",
-       "  {'file': 'cct_images/587b7c6d-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/587b7c6e-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.789,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.789,\n",
-       "     'bbox': [0.8872, 0.5307, 0.1098, 0.08768]}]},\n",
-       "  {'file': 'cct_images/587b7c6f-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': -1,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/587b7c70-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0288,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0288,\n",
-       "     'bbox': [0.09423, 0.6398, 0.1396, 0.1465]}]},\n",
-       "  {'file': 'cct_images/587b7c72-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.7543, 0.5261, 0.2456, 0.2222]}]},\n",
-       "  {'file': 'cct_images/587b7c73-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.925,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0589,\n",
-       "     'bbox': [0.9458, 0.4886, 0.05419, 0.1285]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.925,\n",
-       "     'bbox': [0.009277, 0.4123, 0.3764, 0.3734]}]},\n",
-       "  {'file': 'cct_images/587b7c76-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.026,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.026,\n",
-       "     'bbox': [0.2314, 0.9598, 0.083, 0.03949]}]},\n",
-       "  {'file': 'cct_images/587b7c77-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.01,\n",
-       "     'bbox': [0.9628, 0.6191, 0.03466, 0.0435]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.107,\n",
-       "     'bbox': [0.06347, 0.4571, 0.02148, 0.03145]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4189, 0.668, 0.3193, 0.3313]}]},\n",
-       "  {'file': 'cct_images/587b7c78-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0697,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0697,\n",
-       "     'bbox': [0.7744, 0.2242, 0.06835, 0.0917]}]},\n",
-       "  {'file': 'cct_images/587b7c79-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0146,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0142,\n",
-       "     'bbox': [0.04296, 0.6378, 0.06787, 0.07095]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0146,\n",
-       "     'bbox': [0.1347, 0.6405, 0.04833, 0.04953]}]},\n",
-       "  {'file': 'cct_images/587b7c7b-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.919,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.919,\n",
-       "     'bbox': [0.04394, 0.6653, 0.7089, 0.3333]}]},\n",
-       "  {'file': 'cct_images/587b7c7c-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.794,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.794,\n",
-       "     'bbox': [0.2421, 0.4912, 0.08349, 0.1465]}]},\n",
-       "  {'file': 'cct_images/587b7c7d-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.86,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.86,\n",
-       "     'bbox': [0.3583, 0.2315, 0.09521, 0.2001]}]},\n",
-       "  {'file': 'cct_images/5860ef9f-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0258,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0258,\n",
-       "     'bbox': [0.6035, 0.5709, 0.05859, 0.0522]}]},\n",
-       "  {'file': 'cct_images/5860efa3-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.883,\n",
-       "   'detections': [{'category': '3',\n",
-       "     'conf': 0.883,\n",
-       "     'bbox': [0, 0, 1.0, 0.9661]}]},\n",
-       "  {'file': 'cct_images/5860efa4-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.756,\n",
-       "     'bbox': [0.5395, 0.17, 0.06494, 0.1331]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.5605, 0.4196, 0.3032, 0.3132]}]},\n",
-       "  {'file': 'cct_images/5860efa5-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0133,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0133,\n",
-       "     'bbox': [0.9472, 0.6994, 0.05273, 0.1238]}]},\n",
-       "  {'file': 'cct_images/5860efa6-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': 0.0,\n",
-       "   'detections': []},\n",
-       "  {'file': 'cct_images/5860efa7-23d2-11e8-a6a3-ec086b02610b.jpg',\n",
-       "   'max_detection_conf': -1,\n",
-       "   'detections': []},\n",
-       "  ...],\n",
-       " 'detection_categories': {'1': 'animal', '2': 'person', '3': 'vehicle'},\n",
-       " 'info': {'detection_completion_time': '2022-08-01 21:13:54',\n",
-       "  'format_version': '1.2',\n",
-       "  'detector': 'md_v5a.0.0.pt',\n",
-       "  'detector_metadata': {'megadetector_version': 'v5a.0.0',\n",
-       "   'typical_detection_threshold': 0.2,\n",
-       "   'conservative_detection_threshold': 0.05}}}"
+       "dataset_name         20\n",
+       "url_gcp             909\n",
+       "url_aws             909\n",
+       "url_azure           909\n",
+       "image_id            909\n",
+       "sequence_id         673\n",
+       "location_id         629\n",
+       "frame_num            12\n",
+       "original_label      901\n",
+       "scientific_name     908\n",
+       "common_name         901\n",
+       "datetime            821\n",
+       "annotation_level      3\n",
+       "kingdom               1\n",
+       "phylum                2\n",
+       "subphylum             5\n",
+       "superclass            1\n",
+       "class                 8\n",
+       "subclass              3\n",
+       "infraclass            2\n",
+       "superorder            5\n",
+       "order                58\n",
+       "suborder             17\n",
+       "infraorder            9\n",
+       "superfamily          12\n",
+       "family              187\n",
+       "subfamily            71\n",
+       "tribe                46\n",
+       "genus               538\n",
+       "species             739\n",
+       "subspecies           12\n",
+       "variety               1\n",
+       "multi_species         2\n",
+       "num_species           3\n",
+       "lin_duplicate         2\n",
+       "full_duplicate        1\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data"
+    "df_unique_all_taxa.nunique()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 97,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "cct_mdv5a = pd.json_normalize(data[\"images\"], max_level = 1)"
+    "We have now captured all 908 unique scientific names, but only 901 of the 999 unique common names."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -11595,8875 +2540,594 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>max_detection_conf</th>\n",
-       "      <th>detections</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>cct_images/586ae111-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.893</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.893, 'bbox': [0.6...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>cct_images/586ae112-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.958</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.958, 'bbox': [0.5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>cct_images/586ae113-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.724</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.724, 'bbox': [0.8...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>cct_images/586ae114-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.918</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.918, 'bbox': [0.5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>cct_images/586ae115-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.815</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.815, 'bbox': [0.2...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>cct_images/586ae117-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>cct_images/586ae118-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.691</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.691, 'bbox': [0.5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>cct_images/586ae119-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.798</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.122, 'bbox': [0.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>cct_images/586ae11a-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.954</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.954, 'bbox': [0.2...</td>\n",
+       "      <th>dataset_name</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
+       "      <th>image_id</th>\n",
+       "      <th>sequence_id</th>\n",
+       "      <th>location_id</th>\n",
+       "      <th>frame_num</th>\n",
+       "      <th>original_label</th>\n",
+       "      <th>scientific_name</th>\n",
+       "      <th>...</th>\n",
+       "      <th>subfamily</th>\n",
+       "      <th>tribe</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>variety</th>\n",
+       "      <th>multi_species</th>\n",
+       "      <th>num_species</th>\n",
+       "      <th>lin_duplicate</th>\n",
+       "      <th>full_duplicate</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>cct_images/586ae11c-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>[]</td>\n",
+       "      <th>5</th>\n",
+       "      <td>Caltech Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Caltech Camera Traps : 5a096955-23d2-11e8-a6a3...</td>\n",
+       "      <td>Caltech Camera Traps : 70096335-5567-11e8-a99a...</td>\n",
+       "      <td>Caltech Camera Traps : 36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>car</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>1 rows × 36 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                file  max_detection_conf   \n",
-       "0  cct_images/586ae111-23d2-11e8-a6a3-ec086b02610...               0.893  \\\n",
-       "1  cct_images/586ae112-23d2-11e8-a6a3-ec086b02610...               0.958   \n",
-       "2  cct_images/586ae113-23d2-11e8-a6a3-ec086b02610...               0.724   \n",
-       "3  cct_images/586ae114-23d2-11e8-a6a3-ec086b02610...               0.918   \n",
-       "4  cct_images/586ae115-23d2-11e8-a6a3-ec086b02610...               0.815   \n",
-       "5  cct_images/586ae117-23d2-11e8-a6a3-ec086b02610...               0.000   \n",
-       "6  cct_images/586ae118-23d2-11e8-a6a3-ec086b02610...               0.691   \n",
-       "7  cct_images/586ae119-23d2-11e8-a6a3-ec086b02610...               0.798   \n",
-       "8  cct_images/586ae11a-23d2-11e8-a6a3-ec086b02610...               0.954   \n",
-       "9  cct_images/586ae11c-23d2-11e8-a6a3-ec086b02610...               0.000   \n",
+       "           dataset_name                                            url_gcp  \\\n",
+       "5  Caltech Camera Traps  https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                             url_aws  \\\n",
+       "5  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "\n",
+       "                                           url_azure  \\\n",
+       "5  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "\n",
+       "                                            image_id  \\\n",
+       "5  Caltech Camera Traps : 5a096955-23d2-11e8-a6a3...   \n",
+       "\n",
+       "                                         sequence_id  \\\n",
+       "5  Caltech Camera Traps : 70096335-5567-11e8-a99a...   \n",
+       "\n",
+       "                 location_id  frame_num original_label scientific_name  ...  \\\n",
+       "5  Caltech Camera Traps : 36          1            car             NaN  ...   \n",
+       "\n",
+       "  subfamily tribe genus species subspecies variety multi_species num_species  \\\n",
+       "5       NaN   NaN   NaN     NaN        NaN     NaN         False         1.0   \n",
+       "\n",
+       "  lin_duplicate full_duplicate  \n",
+       "5         False          False  \n",
        "\n",
-       "                                          detections  \n",
-       "0  [{'category': '1', 'conf': 0.893, 'bbox': [0.6...  \n",
-       "1  [{'category': '1', 'conf': 0.958, 'bbox': [0.5...  \n",
-       "2  [{'category': '1', 'conf': 0.724, 'bbox': [0.8...  \n",
-       "3  [{'category': '1', 'conf': 0.918, 'bbox': [0.5...  \n",
-       "4  [{'category': '1', 'conf': 0.815, 'bbox': [0.2...  \n",
-       "5                                                 []  \n",
-       "6  [{'category': '1', 'conf': 0.691, 'bbox': [0.5...  \n",
-       "7  [{'category': '1', 'conf': 0.122, 'bbox': [0.0...  \n",
-       "8  [{'category': '1', 'conf': 0.954, 'bbox': [0.2...  \n",
-       "9                                                 []  "
+       "[1 rows x 36 columns]"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "cct_mdv5a.head(10)"
+    "df_unique_all_taxa.loc[(df_unique_all_taxa[\"scientific_name\"].isna()) | (df_unique_all_taxa[\"common_name\"].isna())]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`file` matches the end of the `url` value in our DataFame. We want to filter out any empty detections that are still in `dedupe_species`. We could also consider lower confidences, but maybe start there."
+    "#### Let's remove those cars"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "244584"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 10961185 entries, 1 to 19351155\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column           Non-Null Count     Dtype \n",
+      "---  ------           --------------     ----- \n",
+      " 0   original_label   10961185 non-null  object\n",
+      " 1   scientific_name  10192703 non-null  object\n",
+      " 2   common_name      10192703 non-null  object\n",
+      " 3   kingdom          10192703 non-null  object\n",
+      "dtypes: object(4)\n",
+      "memory usage: 418.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_cleaned = df_cleaned[df_cleaned[\"original_label\"] != \"car\"].copy()\n",
+    "df_cleaned[[\"original_label\", \"scientific_name\", \"common_name\", \"kingdom\"]].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have 10,961,185 instead of 10,965,902 images; they all have `original_label`, but only 10,192,703 of them have `scientific_name`, `common_name`, and `kingdom`. What are the `original_label`s for those ~800K images?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset_name      12\n",
+      "original_label    24\n",
+      "dtype: int64\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 768482 entries, 245042 to 16833833\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column          Non-Null Count   Dtype \n",
+      "---  ------          --------------   ----- \n",
+      " 0   dataset_name    768482 non-null  object\n",
+      " 1   original_label  768482 non-null  object\n",
+      "dtypes: object(2)\n",
+      "memory usage: 17.6+ MB\n"
+     ]
     }
    ],
    "source": [
-    "len(cct_mdv5a)"
+    "no_taxa = df_cleaned.loc[(df_cleaned[\"scientific_name\"].isna()) & (df_cleaned[\"common_name\"].isna()) & (df_cleaned[\"kingdom\"].isna())].copy()\n",
+    "\n",
+    "print(no_taxa[[\"dataset_name\", \"original_label\"]].nunique())\n",
+    "no_taxa[[\"dataset_name\", \"original_label\"]].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What are these 24 other labels and how are the 768,482 images with them distributed across these 12 datasets?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "list"
+       "original_label\n",
+       "problem                   288579\n",
+       "blurred                   184620\n",
+       "ignore                    177546\n",
+       "vehicle                    26445\n",
+       "unknown                    26170\n",
+       "snow on lens               17552\n",
+       "foggy lens                 15832\n",
+       "vegetation obstruction      6994\n",
+       "malfunction                 5640\n",
+       "unclassifiable              3484\n",
+       "motorcycle                  3423\n",
+       "misdirected                 2832\n",
+       "other                       2474\n",
+       "unidentifiable              1472\n",
+       "foggy weather               1380\n",
+       "lens obscured                866\n",
+       "sun                          835\n",
+       "end                          616\n",
+       "fire                         578\n",
+       "misfire                      400\n",
+       "eye_shine                    328\n",
+       "start                        321\n",
+       "tilted                        56\n",
+       "unidentified                  39\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(cct_mdv5a.detections[5])"
+    "no_taxa[\"original_label\"].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>max_detection_conf</th>\n",
-       "      <th>detections</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>cct_images/586ae117-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>cct_images/586ae11c-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>cct_images/586ae11e-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>cct_images/585a65f2-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>cct_images/585a65f6-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244568</th>\n",
-       "      <td>cct_images/5a14a56f-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244577</th>\n",
-       "      <td>cct_images/5a14a705-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244579</th>\n",
-       "      <td>cct_images/5a14a707-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244581</th>\n",
-       "      <td>cct_images/5a14a709-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244582</th>\n",
-       "      <td>cct_images/5a14a70a-23d2-11e8-a6a3-ec086b02610...</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>[]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>67945 rows × 3 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                                                     file  max_detection_conf   \n",
-       "5       cct_images/586ae117-23d2-11e8-a6a3-ec086b02610...                 0.0  \\\n",
-       "9       cct_images/586ae11c-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "11      cct_images/586ae11e-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "23      cct_images/585a65f2-23d2-11e8-a6a3-ec086b02610...                -1.0   \n",
-       "27      cct_images/585a65f6-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "...                                                   ...                 ...   \n",
-       "244568  cct_images/5a14a56f-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "244577  cct_images/5a14a705-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "244579  cct_images/5a14a707-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "244581  cct_images/5a14a709-23d2-11e8-a6a3-ec086b02610...                 0.0   \n",
-       "244582  cct_images/5a14a70a-23d2-11e8-a6a3-ec086b02610...                -1.0   \n",
-       "\n",
-       "       detections  \n",
-       "5              []  \n",
-       "9              []  \n",
-       "11             []  \n",
-       "23             []  \n",
-       "27             []  \n",
-       "...           ...  \n",
-       "244568         []  \n",
-       "244577         []  \n",
-       "244579         []  \n",
-       "244581         []  \n",
-       "244582         []  \n",
-       "\n",
-       "[67945 rows x 3 columns]"
+       "dataset_name\n",
+       "SWG Camera Traps                    650745\n",
+       "Idaho Camera Traps                   66339\n",
+       "NACTI                                26015\n",
+       "WCS Camera Traps                     18320\n",
+       "Wellington Camera Traps               3484\n",
+       "Orinoquia Camera Traps                1280\n",
+       "Island Conservation Camera Traps      1269\n",
+       "Snapshot Serengeti                     568\n",
+       "ENA24                                  293\n",
+       "Channel Islands Camera Traps           159\n",
+       "Snapshot Mountain Zebra                  7\n",
+       "Snapshot Camdeboo                        3\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "cct_mdv5a.loc[cct_mdv5a['detections'].astype(str) == '[]']"
+    "no_taxa[\"dataset_name\"].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[]"
+       "dataset_name                      original_label        \n",
+       "Channel Islands Camera Traps      other                        159\n",
+       "ENA24                             vehicle                      293\n",
+       "Idaho Camera Traps                snow on lens               17552\n",
+       "                                  foggy lens                 15832\n",
+       "                                  unknown                    11900\n",
+       "                                  vegetation obstruction      6994\n",
+       "                                  malfunction                 5640\n",
+       "                                  misdirected                 2832\n",
+       "                                  other                       2315\n",
+       "                                  foggy weather               1380\n",
+       "                                  lens obscured                866\n",
+       "                                  sun                          835\n",
+       "                                  vehicle                      137\n",
+       "                                  tilted                        56\n",
+       "Island Conservation Camera Traps  unknown                      941\n",
+       "                                  eye_shine                    328\n",
+       "NACTI                             vehicle                    26015\n",
+       "Orinoquia Camera Traps            unknown                     1280\n",
+       "SWG Camera Traps                  problem                   288579\n",
+       "                                  blurred                   184620\n",
+       "                                  ignore                    177546\n",
+       "Snapshot Camdeboo                 fire                           3\n",
+       "Snapshot Mountain Zebra           fire                           7\n",
+       "Snapshot Serengeti                fire                         568\n",
+       "WCS Camera Traps                  unknown                    12049\n",
+       "                                  motorcycle                  3423\n",
+       "                                  unidentifiable              1472\n",
+       "                                  end                          616\n",
+       "                                  misfire                      400\n",
+       "                                  start                        321\n",
+       "                                  unidentified                  39\n",
+       "Wellington Camera Traps           unclassifiable              3484\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dedupe_url_cct = list(dedupe_species.loc[dedupe_species['dataset_name'] == \"Caltech Camera Traps\", 'url'])\n",
-    "dedupe_cct_empties = []\n",
-    "for file in list(cct_mdv5a.loc[cct_mdv5a['detections'].astype(str) == '[]', 'file']):\n",
-    "    if file in dedupe_url_cct:\n",
-    "        dedupe_cct_empties.append(file)\n",
-    "\n",
-    "dedupe_cct_empties"
+    "no_taxa.groupby([\"dataset_name\"])[\"original_label\"].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "CCT Seem fine"
+    "Interesting. It seems like all of these should also be removed. Vegetation obstruction could of course be labeled in Plantae, but we're not going to be labeling 7K images for this project. \n",
+    "\n",
+    "Let's remove them, then we should have 10,192,703 images."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "#### WCS Camera Traps\n",
-    "Since these have a note that empties may be labeled as species within the sequence, let's check this as well."
+    "non_taxa_labels = list(no_taxa[\"original_label\"].unique())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 10192703 entries, 1 to 19351155\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column            Non-Null Count     Dtype  \n",
+      "---  ------            --------------     -----  \n",
+      " 0   dataset_name      10192703 non-null  object \n",
+      " 1   url_gcp           10192703 non-null  object \n",
+      " 2   url_aws           10192703 non-null  object \n",
+      " 3   url_azure         10192703 non-null  object \n",
+      " 4   image_id          10192703 non-null  object \n",
+      " 5   sequence_id       10192703 non-null  object \n",
+      " 6   location_id       10192703 non-null  object \n",
+      " 7   frame_num         10192703 non-null  int64  \n",
+      " 8   original_label    10192703 non-null  object \n",
+      " 9   scientific_name   10192703 non-null  object \n",
+      " 10  common_name       10192703 non-null  object \n",
+      " 11  datetime          6873684 non-null   object \n",
+      " 12  annotation_level  10192703 non-null  object \n",
+      " 13  kingdom           10192703 non-null  object \n",
+      " 14  phylum            10177119 non-null  object \n",
+      " 15  subphylum         10141160 non-null  object \n",
+      " 16  superclass        79 non-null        object \n",
+      " 17  class             10174780 non-null  object \n",
+      " 18  subclass          9022382 non-null   object \n",
+      " 19  infraclass        9021471 non-null   object \n",
+      " 20  superorder        8812501 non-null   object \n",
+      " 21  order             9796763 non-null   object \n",
+      " 22  suborder          7289300 non-null   object \n",
+      " 23  infraorder        510351 non-null    object \n",
+      " 24  superfamily       1805446 non-null   object \n",
+      " 25  family            9693066 non-null   object \n",
+      " 26  subfamily         7662399 non-null   object \n",
+      " 27  tribe             6614011 non-null   object \n",
+      " 28  genus             9445408 non-null   object \n",
+      " 29  species           7521712 non-null   object \n",
+      " 30  subspecies        74052 non-null     object \n",
+      " 31  variety           2050 non-null      object \n",
+      " 32  multi_species     10192703 non-null  bool   \n",
+      " 33  num_species       10192703 non-null  float64\n",
+      " 34  lin_duplicate     10192703 non-null  bool   \n",
+      " 35  full_duplicate    10192703 non-null  bool   \n",
+      "dtypes: bool(3), float64(1), int64(1), object(31)\n",
+      "memory usage: 2.6+ GB\n"
+     ]
+    }
+   ],
    "source": [
-    "with open(\"../MegaDetector_results/wcs-camera-traps_animals_mdv5a.0.0_results.json\") as file:\n",
-    "    data = json.load(file)"
+    "df_clean = df_cleaned.loc[~df_cleaned[\"original_label\"].isin(non_taxa_labels)].copy()\n",
+    "df_clean.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'images': [{'file': '0000/0239.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.4285, 0.5793, 0.221, 0.3911]}]},\n",
-       "  {'file': '0000/0240.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0102,\n",
-       "     'bbox': [0.652, 0.4741, 0.1196, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0299,\n",
-       "     'bbox': [0.6495, 0.4778, 0.07203, 0.05166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3968, 0.5221, 0.1697, 0.2878]}]},\n",
-       "  {'file': '0000/0241.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0229,\n",
-       "     'bbox': [0.7203, 0.4815, 0.04884, 0.0535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.116,\n",
-       "     'bbox': [0.6764, 0.4446, 0.04884, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.734,\n",
-       "     'bbox': [0.6471, 0.5202, 0.09157, 0.1033]},\n",
-       "    {'category': '1', 'conf': 0.883, 'bbox': [0.5335, 0.4852, 0.1147, 0.1476]},\n",
-       "    {'category': '1', 'conf': 0.93, 'bbox': [0.5335, 0.6162, 0.1477, 0.3542]},\n",
-       "    {'category': '1', 'conf': 0.968, 'bbox': [0.3186, 0.559, 0.238, 0.4114]}]},\n",
-       "  {'file': '0000/0242.jpg',\n",
-       "   'max_detection_conf': 0.847,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.017,\n",
-       "     'bbox': [0.5763, 0.4649, 0.03785, 0.04428]},\n",
-       "    {'category': '1', 'conf': 0.827, 'bbox': [0.6141, 0.511, 0.09157, 0.1678]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.847,\n",
-       "     'bbox': [0.5482, 0.5092, 0.08302, 0.2214]}]},\n",
-       "  {'file': '0000/0243.jpg',\n",
-       "   'max_detection_conf': 0.779,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0119,\n",
-       "     'bbox': [0.5347, 0.5922, 0.1208, 0.1476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0238,\n",
-       "     'bbox': [0.5409, 0.5442, 0.1282, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0309,\n",
-       "     'bbox': [0.735, 0.4907, 0.04517, 0.05719]},\n",
-       "    {'category': '1', 'conf': 0.722, 'bbox': [0.5384, 0.5405, 0.1978, 0.1992]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.779,\n",
-       "     'bbox': [0.4835, 0.5498, 0.1013, 0.1291]}]},\n",
-       "  {'file': '0000/0244.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0118,\n",
-       "     'bbox': [0.5714, 0.5313, 0.1318, 0.2029]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.679,\n",
-       "     'bbox': [0.6141, 0.5295, 0.08791, 0.1236]},\n",
-       "    {'category': '1', 'conf': 0.794, 'bbox': [0.2503, 0.5424, 0.1599, 0.1863]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.3223, 0.5369, 0.2087, 0.2084]}]},\n",
-       "  {'file': '0000/0245.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.3162, 0.5405, 0.1672, 0.4298]}]},\n",
-       "  {'file': '0000/0246.jpg',\n",
-       "   'max_detection_conf': 0.915,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.915,\n",
-       "     'bbox': [0.4957, 0.5221, 0.1709, 0.2029]}]},\n",
-       "  {'file': '0000/0247.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.827,\n",
-       "     'bbox': [0.2417, 0.5258, 0.1819, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.2478, 0.5129, 0.2771, 0.3431]}]},\n",
-       "  {'file': '0000/0248.jpg',\n",
-       "   'max_detection_conf': 0.928,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.928,\n",
-       "     'bbox': [0.4664, 0.5073, 0.2246, 0.2306]}]},\n",
-       "  {'file': '0000/0249.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0185,\n",
-       "     'bbox': [0.7277, 0.476, 0.04639, 0.05719]},\n",
-       "    {'category': '1', 'conf': 0.786, 'bbox': [0.5641, 0.5184, 0.1623, 0.1383]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.3345, 0.511, 0.1721, 0.2029]}]},\n",
-       "  {'file': '0000/0250.jpg',\n",
-       "   'max_detection_conf': 0.0845,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0845,\n",
-       "     'bbox': [0.7203, 0.4833, 0.04884, 0.05904]}]},\n",
-       "  {'file': '0000/0251.jpg',\n",
-       "   'max_detection_conf': 0.725,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.253,\n",
-       "     'bbox': [0.3052, 0.5424, 0.07081, 0.08302]},\n",
-       "    {'category': '1', 'conf': 0.725, 'bbox': [0.282, 0.594, 0.3199, 0.3763]}]},\n",
-       "  {'file': '0000/0252.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0153,\n",
-       "     'bbox': [0.1086, 0.5239, 0.03174, 0.04428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0957,\n",
-       "     'bbox': [0.09768, 0.5479, 0.1782, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.477,\n",
-       "     'bbox': [0.1343, 0.5479, 0.1428, 0.09225]},\n",
-       "    {'category': '1', 'conf': 0.779, 'bbox': [0.3247, 0.5645, 0.1147, 0.1199]},\n",
-       "    {'category': '1', 'conf': 0.79, 'bbox': [0.2429, 0.583, 0.1452, 0.1605]},\n",
-       "    {'category': '1', 'conf': 0.928, 'bbox': [0.3882, 0.7232, 0.3272, 0.2472]},\n",
-       "    {'category': '1', 'conf': 0.935, 'bbox': [0.4554, 0.559, 0.1575, 0.1771]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.1587, 0.6808, 0.3345, 0.2896]}]},\n",
-       "  {'file': '0000/0253.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0135,\n",
-       "     'bbox': [0.3406, 0.5369, 0.05738, 0.08487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0168,\n",
-       "     'bbox': [0.01221, 0.5442, 0.2246, 0.2527]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0256,\n",
-       "     'bbox': [0.1367, 0.5442, 0.1062, 0.1199]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0784,\n",
-       "     'bbox': [0.4297, 0.5036, 0.08302, 0.06826]},\n",
-       "    {'category': '1', 'conf': 0.238, 'bbox': [0.387, 0.5405, 0.0525, 0.06457]},\n",
-       "    {'category': '1', 'conf': 0.501, 'bbox': [0.2026, 0.5479, 0.0525, 0.0738]},\n",
-       "    {'category': '1', 'conf': 0.678, 'bbox': [0.1294, 0.5461, 0.1074, 0.2361]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.691,\n",
-       "     'bbox': [0.3394, 0.5369, 0.09768, 0.1217]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.836,\n",
-       "     'bbox': [0.2478, 0.5424, 0.09645, 0.1365]},\n",
-       "    {'category': '1', 'conf': 0.939, 'bbox': [0.6544, 0.7416, 0.3199, 0.2306]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.2271, 0.5571, 0.3577, 0.4132]}]},\n",
-       "  {'file': '0000/0254.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0116,\n",
-       "     'bbox': [0.1623, 0.5608, 0.2625, 0.2269]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.127,\n",
-       "     'bbox': [0.6996, 0.5018, 0.09035, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.784,\n",
-       "     'bbox': [0.01831, 0.6051, 0.2063, 0.2472]},\n",
-       "    {'category': '1', 'conf': 0.818, 'bbox': [0.293, 0.5571, 0.1636, 0.2398]},\n",
-       "    {'category': '1', 'conf': 0.906, 'bbox': [0.01953, 0.7472, 0.3919, 0.225]},\n",
-       "    {'category': '1', 'conf': 0.914, 'bbox': [0.05372, 0.5, 0.3052, 0.2693]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.3614, 0.5498, 0.2002, 0.2656]}]},\n",
-       "  {'file': '0000/0255.jpg',\n",
-       "   'max_detection_conf': 0.784,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0104,\n",
-       "     'bbox': [0.1575, 0.4926, 0.04639, 0.0535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0125,\n",
-       "     'bbox': [0.3223, 0.5147, 0.04273, 0.04428]},\n",
-       "    {'category': '1', 'conf': 0.0219, 'bbox': [0.3675, 0.5036, 0.1269, 0.118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0247,\n",
-       "     'bbox': [0.08424, 0.4833, 0.1147, 0.1236]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0306,\n",
-       "     'bbox': [0.002442, 0.5738, 0.1318, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0451,\n",
-       "     'bbox': [0.2759, 0.5664, 0.0586, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0841,\n",
-       "     'bbox': [0.1147, 0.4815, 0.08547, 0.06457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0962,\n",
-       "     'bbox': [0.3174, 0.5147, 0.04761, 0.1088]},\n",
-       "    {'category': '1', 'conf': 0.45, 'bbox': [0.3565, 0.5258, 0.06227, 0.1328]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.533,\n",
-       "     'bbox': [0.2735, 0.5166, 0.09157, 0.1457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.784,\n",
-       "     'bbox': [0.5262, 0.5424, 0.1782, 0.2066]}]},\n",
-       "  {'file': '0000/0256.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.01,\n",
-       "     'bbox': [0.144, 0.5239, 0.09279, 0.1199]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0112,\n",
-       "     'bbox': [0.2796, 0.5405, 0.04761, 0.04612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0124,\n",
-       "     'bbox': [0.2588, 0.5202, 0.06593, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0127,\n",
-       "     'bbox': [0.1733, 0.5276, 0.01953, 0.0166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0153,\n",
-       "     'bbox': [0.6617, 0.559, 0.06837, 0.1881]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0158,\n",
-       "     'bbox': [0.293, 0.5221, 0.05128, 0.0535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0175,\n",
-       "     'bbox': [0.3968, 0.5276, 0.04395, 0.04059]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0413,\n",
-       "     'bbox': [0.3076, 0.5682, 0.06837, 0.05166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0872,\n",
-       "     'bbox': [0.2588, 0.5479, 0.06715, 0.0738]},\n",
-       "    {'category': '1', 'conf': 0.54, 'bbox': [0.6642, 0.559, 0.05616, 0.08487]},\n",
-       "    {'category': '1', 'conf': 0.57, 'bbox': [0.2588, 0.5645, 0.1159, 0.07195]},\n",
-       "    {'category': '1', 'conf': 0.787, 'bbox': [0.5445, 0.5608, 0.1159, 0.1974]},\n",
-       "    {'category': '1', 'conf': 0.811, 'bbox': [0.1501, 0.5129, 0.166, 0.1439]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.3858, 0.5295, 0.1684, 0.2896]}]},\n",
-       "  {'file': '0000/0257.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0112,\n",
-       "     'bbox': [0.1184, 0.476, 0.08913, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0178,\n",
-       "     'bbox': [0.2112, 0.5276, 0.05494, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0192,\n",
-       "     'bbox': [0.3235, 0.5295, 0.04273, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0207,\n",
-       "     'bbox': [0.03907, 0.5904, 0.1306, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0208,\n",
-       "     'bbox': [0.1575, 0.5036, 0.0586, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0289,\n",
-       "     'bbox': [0.3626, 0.4981, 0.07081, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.043,\n",
-       "     'bbox': [0.1318, 0.4704, 0.0525, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0533,\n",
-       "     'bbox': [0.2417, 0.4575, 0.04761, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.259,\n",
-       "     'bbox': [0.4981, 0.5166, 0.04639, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.568,\n",
-       "     'bbox': [0.3235, 0.4981, 0.09157, 0.1236]},\n",
-       "    {'category': '1', 'conf': 0.639, 'bbox': [0.4981, 0.5166, 0.09768, 0.107]},\n",
-       "    {'category': '1', 'conf': 0.763, 'bbox': [0.1648, 0.5036, 0.1001, 0.107]},\n",
-       "    {'category': '1', 'conf': 0.778, 'bbox': [0.2905, 0.5387, 0.1013, 0.1678]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.793,\n",
-       "     'bbox': [0.6324, 0.5369, 0.09279, 0.1291]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.872,\n",
-       "     'bbox': [0.4285, 0.5369, 0.06959, 0.1402]},\n",
-       "    {'category': '1', 'conf': 0.925, 'bbox': [0.5921, 0.7472, 0.4078, 0.225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.03785, 0.5867, 0.398, 0.3856]}]},\n",
-       "  {'file': '0000/0258.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.442, 0.4923, 0.2857, 0.2868]}]},\n",
-       "  {'file': '0000/0259.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.3445, 0.4949, 0.21, 0.2994]}]},\n",
-       "  {'file': '0000/0260.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.4806, 0.3375, 0.4957, 0.5583]}]},\n",
-       "  {'file': '0000/0261.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.4352, 0.3401, 0.4184, 0.5279]}]},\n",
-       "  {'file': '0000/0262.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.4521, 0.3604, 0.2605, 0.5862]}]},\n",
-       "  {'file': '0000/0263.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4537, 0.5076, 0.2857, 0.3045]}]},\n",
-       "  {'file': '0000/0264.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.4756, 0.4847, 0.2504, 0.269]}]},\n",
-       "  {'file': '0000/0265.jpg',\n",
-       "   'max_detection_conf': 0.92,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0278,\n",
-       "     'bbox': [0.3579, 0.6776, 0.06386, 0.1065]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.92,\n",
-       "     'bbox': [0.4016, 0.4822, 0.1579, 0.1776]}]},\n",
-       "  {'file': '0000/0266.jpg',\n",
-       "   'max_detection_conf': 0.92,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.92,\n",
-       "     'bbox': [0.4588, 0.4847, 0.08739, 0.1928]}]},\n",
-       "  {'file': '0000/0267.jpg',\n",
-       "   'max_detection_conf': 0.904,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0168,\n",
-       "     'bbox': [0.6369, 0.3807, 0.05714, 0.08375]},\n",
-       "    {'category': '1', 'conf': 0.117, 'bbox': [0.3579, 0.5329, 0.1109, 0.2005]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.904,\n",
-       "     'bbox': [0.4621, 0.4568, 0.09747, 0.1903]}]},\n",
-       "  {'file': '0000/0268.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.4134, 0.4923, 0.1899, 0.2258]}]},\n",
-       "  {'file': '0000/0269.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.4621, 0.2994, 0.5075, 0.6675]}]},\n",
-       "  {'file': '0000/0270.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3915, 0.4898, 0.1747, 0.1928]}]},\n",
-       "  {'file': '0000/0271.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.3126, 0.5025, 0.2252, 0.3071]}]},\n",
-       "  {'file': '0000/0272.jpg',\n",
-       "   'max_detection_conf': 0.874,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0166,\n",
-       "     'bbox': [0.368, 0.5406, 0.09747, 0.1725]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0.5042, 0.5253, 0.09075, 0.1218]}]},\n",
-       "  {'file': '0000/0273.jpg',\n",
-       "   'max_detection_conf': 0.903,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.903,\n",
-       "     'bbox': [0.4218, 0.4873, 0.07899, 0.09644]}]},\n",
-       "  {'file': '0000/0672.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.5201, 0.4464, 0.177, 0.1771]}]},\n",
-       "  {'file': '0000/1354.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.3186, 0.404, 0.1929, 0.3579]}]},\n",
-       "  {'file': '0000/1218.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.315, 0.5055, 0.1318, 0.08487]}]},\n",
-       "  {'file': '0000/1219.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.1746, 0.3966, 0.3394, 0.3745]}]},\n",
-       "  {'file': '0000/1220.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.3064, 0.4464, 0.3504, 0.3708]}]},\n",
-       "  {'file': '0000/1221.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3125, 0.5682, 0.2197, 0.1568]}]},\n",
-       "  {'file': '0000/1222.jpg',\n",
-       "   'max_detection_conf': 0.983,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.983,\n",
-       "     'bbox': [0.01465, 0.3745, 0.5482, 0.5977]}]},\n",
-       "  {'file': '0000/1223.jpg',\n",
-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.906,\n",
-       "     'bbox': [0.07081, 0.4372, 0.09523, 0.2029]},\n",
-       "    {'category': '1', 'conf': 0.927, 'bbox': [0.2307, 0.4188, 0.1587, 0.1568]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.4102, 0.4169, 0.2185, 0.238]}]},\n",
-       "  {'file': '0000/1224.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.4407, 0.2158, 0.4859, 0.5202]}]},\n",
-       "  {'file': '0000/1225.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0, 0.4446, 0.2014, 0.5276]},\n",
-       "    {'category': '1', 'conf': 0.975, 'bbox': [0.387, 0.3007, 0.304, 0.5184]}]},\n",
-       "  {'file': '0000/1226.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.1196, 0.4926, 0.3357, 0.2804]}]},\n",
-       "  {'file': '0000/1227.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3284, 0.4594, 0.3015, 0.3099]}]},\n",
-       "  {'file': '0000/1228.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.901,\n",
-       "     'bbox': [0.4261, 0.4243, 0.1428, 0.273]},\n",
-       "    {'category': '1', 'conf': 0.938, 'bbox': [0.2075, 0.452, 0.1672, 0.2103]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.4285, 0.4132, 0.3308, 0.3302]}]},\n",
-       "  {'file': '0000/1229.jpg',\n",
-       "   'max_detection_conf': 0.911,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.911,\n",
-       "     'bbox': [0.6788, 0.3099, 0.3199, 0.3025]}]},\n",
-       "  {'file': '0000/1230.jpg',\n",
-       "   'max_detection_conf': 0.935,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0153,\n",
-       "     'bbox': [0.807, 0.8671, 0.04273, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0483,\n",
-       "     'bbox': [0.8339, 0.7343, 0.0354, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.935,\n",
-       "     'bbox': [0.6239, 0.6568, 0.07448, 0.1937]}]},\n",
-       "  {'file': '0000/1231.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4114, 0.535, 0.1978, 0.4243]}]},\n",
-       "  {'file': '0000/1232.jpg',\n",
-       "   'max_detection_conf': 0.692,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.26,\n",
-       "     'bbox': [0.3247, 0.6199, 0.1648, 0.09594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.692,\n",
-       "     'bbox': [0.326, 0.6254, 0.07692, 0.0904]}]},\n",
-       "  {'file': '0000/1233.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.4139, 0.6494, 0.3028, 0.1752]}]},\n",
-       "  {'file': '0000/1234.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3748, 0.5332, 0.1489, 0.1402]}]},\n",
-       "  {'file': '0000/1235.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.4725, 0.3634, 0.2429, 0.2583]}]},\n",
-       "  {'file': '0000/1236.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.2454, 0.4114, 0.3064, 0.2675]}]},\n",
-       "  {'file': '0000/1237.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0465,\n",
-       "     'bbox': [0, 0.4243, 0.08058, 0.2583]},\n",
-       "    {'category': '1', 'conf': 0.766, 'bbox': [0.05982, 0.4169, 0.188, 0.273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.1025, 0.4335, 0.5763, 0.5092]}]},\n",
-       "  {'file': '0000/1238.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0103,\n",
-       "     'bbox': [0.002442, 0.463, 0.1794, 0.3321]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0161,\n",
-       "     'bbox': [0.1233, 0.5202, 0.05738, 0.1383]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0655,\n",
-       "     'bbox': [0.04639, 0.4575, 0.1355, 0.2011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.362,\n",
-       "     'bbox': [0.1098, 0.4612, 0.07081, 0.1605]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.2588, 0.4889, 0.09768, 0.2767]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3308, 0.4852, 0.2673, 0.4852]}]},\n",
-       "  {'file': '0000/1239.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0302,\n",
-       "     'bbox': [0.6947, 0.4612, 0.1269, 0.1476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.3992, 0.5405, 0.1611, 0.1254]}]},\n",
-       "  {'file': '0000/1240.jpg',\n",
-       "   'max_detection_conf': 0.926,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.102,\n",
-       "     'bbox': [0.6691, 0.4741, 0.1208, 0.1512]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.926,\n",
-       "     'bbox': [0.4908, 0.4612, 0.1062, 0.1826]}]},\n",
-       "  {'file': '0000/1241.jpg',\n",
-       "   'max_detection_conf': 0.92,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.92,\n",
-       "     'bbox': [0.5763, 0.5424, 0.1147, 0.1051]}]},\n",
-       "  {'file': '0000/1242.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.2246, 0.4557, 0.2893, 0.2675]}]},\n",
-       "  {'file': '0000/1243.jpg',\n",
-       "   'max_detection_conf': 0.948,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.948,\n",
-       "     'bbox': [0.2527, 0.3671, 0.724, 0.6033]}]},\n",
-       "  {'file': '0000/1244.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0131,\n",
-       "     'bbox': [0.702, 0.4815, 0.1111, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0213,\n",
-       "     'bbox': [0.8644, 0.5239, 0.1074, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.4041, 0.4409, 0.271, 0.2398]}]},\n",
-       "  {'file': '0000/1245.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.785,\n",
-       "     'bbox': [0, 0.6346, 0.1025, 0.3376]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.5457, 0.3966, 0.2246, 0.2509]}]},\n",
-       "  {'file': '0000/1624.jpg',\n",
-       "   'max_detection_conf': 0.897,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.1,\n",
-       "     'bbox': [0.7191, 0.3302, 0.1489, 0.1457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.897,\n",
-       "     'bbox': [0.1562, 0.2435, 0.07692, 0.08302]}]},\n",
-       "  {'file': '0000/1625.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.1831, 0.3044, 0.4126, 0.3929]}]},\n",
-       "  {'file': '0000/1626.jpg',\n",
-       "   'max_detection_conf': 0.929,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.929,\n",
-       "     'bbox': [0.1684, 0.1494, 0.3406, 0.5332]}]},\n",
-       "  {'file': '0000/1627.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.42, 0.4114, 0.5531, 0.5627]}]},\n",
-       "  {'file': '0000/1628.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4725, 0.2767, 0.3724, 0.5166]}]},\n",
-       "  {'file': '0000/1629.jpg',\n",
-       "   'max_detection_conf': 0.917,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.917,\n",
-       "     'bbox': [0.009768, 0.03136, 0.6117, 0.9391]}]},\n",
-       "  {'file': '0000/1630.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.349,\n",
-       "     'bbox': [0.9218, 0.3856, 0.07814, 0.2435]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.01953, 0.3339, 0.4175, 0.369]}]},\n",
-       "  {'file': '0000/1631.jpg',\n",
-       "   'max_detection_conf': 0.924,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.924,\n",
-       "     'bbox': [0.2942, 0.3099, 0.3162, 0.3837]}]},\n",
-       "  {'file': '0000/1632.jpg',\n",
-       "   'max_detection_conf': 0.93,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0118,\n",
-       "     'bbox': [0.4798, 0.4298, 0.02442, 0.06826]},\n",
-       "    {'category': '1', 'conf': 0.58, 'bbox': [0.06593, 0.3505, 0.1404, 0.3007]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.93,\n",
-       "     'bbox': [0.06715, 0.3505, 0.2454, 0.3948]}]},\n",
-       "  {'file': '0000/1633.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.398, 0.4889, 0.08791, 0.1512]}]},\n",
-       "  {'file': '0000/1634.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.4713, 0.4907, 0.3076, 0.3357]}]},\n",
-       "  {'file': '0000/1635.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.2844, 0.2435, 0.2905, 0.6476]}]},\n",
-       "  {'file': '0000/1636.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.02808, 0.3616, 0.4432, 0.3542]}]},\n",
-       "  {'file': '0000/1637.jpg',\n",
-       "   'max_detection_conf': 0.89,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.89,\n",
-       "     'bbox': [0.03296, 0.6254, 0.5152, 0.3487]}]},\n",
-       "  {'file': '0000/1638.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.868,\n",
-       "     'bbox': [0.8827, 0.2859, 0.09523, 0.3154]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.238, 0.2693, 0.4713, 0.5922]}]},\n",
-       "  {'file': '0000/1639.jpg',\n",
-       "   'max_detection_conf': 0.834,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.834,\n",
-       "     'bbox': [0.02319, 0.297, 0.4517, 0.4114]}]},\n",
-       "  {'file': '0000/1640.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.2661, 0.2859, 0.3431, 0.404]}]},\n",
-       "  {'file': '0000/1641.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.177, 0.1734, 0.5323, 0.7988]}]},\n",
-       "  {'file': '0000/1642.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.021,\n",
-       "     'bbox': [0, 0.4944, 0.05982, 0.3154]},\n",
-       "    {'category': '1', 'conf': 0.282, 'bbox': [0, 0.4797, 0.05982, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.1062, 0.3191, 0.4078, 0.3966]}]},\n",
-       "  {'file': '0000/1643.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0106,\n",
-       "     'bbox': [0.459, 0.3247, 0.1929, 0.1992]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0721,\n",
-       "     'bbox': [0.08669, 0.3763, 0.02564, 0.04981]},\n",
-       "    {'category': '1', 'conf': 0.645, 'bbox': [0.01465, 0.4077, 0.0586, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.897,\n",
-       "     'bbox': [0.05128, 0.4243, 0.1831, 0.2859]},\n",
-       "    {'category': '1', 'conf': 0.925, 'bbox': [0.3858, 0.3247, 0.2649, 0.3357]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.1037, 0.2656, 0.4884, 0.4889]}]},\n",
-       "  {'file': '0000/1644.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.876,\n",
-       "     'bbox': [0.5189, 0.3708, 0.2124, 0.2343]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3833, 0.3837, 0.2844, 0.2841]}]},\n",
-       "  {'file': '0000/1645.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.2246, 0.3284, 0.4493, 0.3929]}]},\n",
-       "  {'file': '0000/1646.jpg',\n",
-       "   'max_detection_conf': 0.864,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.864,\n",
-       "     'bbox': [0.3357, 0.5738, 0.09645, 0.07195]}]},\n",
-       "  {'file': '0000/1647.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.3711, 0.4612, 0.1465, 0.1476]}]},\n",
-       "  {'file': '0000/1648.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.1343, 0.2398, 0.7032, 0.5756]}]},\n",
-       "  {'file': '0000/1649.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.644,\n",
-       "     'bbox': [0.1452, 0.3653, 0.1416, 0.1789]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.759,\n",
-       "     'bbox': [0.008547, 0.3542, 0.2026, 0.1771]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.01098, 0.4446, 0.2258, 0.3099]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.3186, 0.2546, 0.3162, 0.5738]}]},\n",
-       "  {'file': '0000/1650.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.045,\n",
-       "     'bbox': [0.001221, 0.3542, 0.04639, 0.1254]},\n",
-       "    {'category': '1', 'conf': 0.222, 'bbox': [0, 0.3523, 0.0586, 0.2509]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.403,\n",
-       "     'bbox': [0.02197, 0.3025, 0.1086, 0.3118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.582,\n",
-       "     'bbox': [0.02808, 0.3044, 0.2148, 0.3339]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.07448, 0.2693, 0.3772, 0.4354]}]},\n",
-       "  {'file': '0000/1651.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.04639, 0.2952, 0.5103, 0.5498]}]},\n",
-       "  {'file': '0000/1652.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0425,\n",
-       "     'bbox': [0.009768, 0.4243, 0.238, 0.2158]},\n",
-       "    {'category': '1', 'conf': 0.52, 'bbox': [0, 0.4243, 0.1123, 0.2195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.07936, 0.3357, 0.4297, 0.4317]}]},\n",
-       "  {'file': '0000/1653.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3406, 0.3505, 0.398, 0.3339]}]},\n",
-       "  {'file': '0000/1654.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.37,\n",
-       "     'bbox': [0, 0.4095, 0.06227, 0.1365]},\n",
-       "    {'category': '1', 'conf': 0.968, 'bbox': [0.2478, 0.3376, 0.3821, 0.3653]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.01831, 0.2841, 0.2442, 0.6863]}]},\n",
-       "  {'file': '0000/0673.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.5897, 0.4833, 0.2087, 0.3985]}]},\n",
-       "  {'file': '0000/0674.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.3113, 0.4501, 0.2051, 0.2361]}]},\n",
-       "  {'file': '0000/0675.jpg',\n",
-       "   'max_detection_conf': 0.85,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.85,\n",
-       "     'bbox': [0.4542, 0.4409, 0.1159, 0.118]}]},\n",
-       "  {'file': '0000/0676.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2211, 0.5883, 0.2488, 0.383]}]},\n",
-       "  {'file': '0000/0677.jpg',\n",
-       "   'max_detection_conf': 0.907,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.5505, 0.63, 0.08767, 0.1396]}]},\n",
-       "  {'file': '0000/0678.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0108,\n",
-       "     'bbox': [0.8088, 0.5489, 0.165, 0.4248]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0109,\n",
-       "     'bbox': [0.5703, 0.5513, 0.04739, 0.03937]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.013,\n",
-       "     'bbox': [0.5268, 0.5525, 0.09083, 0.1073]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0154,\n",
-       "     'bbox': [0.7211, 0.562, 0.04107, 0.06205]},\n",
-       "    {'category': '1', 'conf': 0.0186, 'bbox': [0.338, 0.5918, 0.1184, 0.2171]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.202,\n",
-       "     'bbox': [0.5624, 0.5501, 0.05529, 0.08233]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.781,\n",
-       "     'bbox': [0.09952, 0.6229, 0.1058, 0.1408]},\n",
-       "    {'category': '1', 'conf': 0.854, 'bbox': [0.015, 0.6121, 0.1184, 0.1813]},\n",
-       "    {'category': '1', 'conf': 0.867, 'bbox': [0.0703, 0.6634, 0.12, 0.1205]},\n",
-       "    {'category': '1', 'conf': 0.87, 'bbox': [0.3396, 0.5966, 0.12, 0.09665]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.2409, 0.6372, 0.2053, 0.2171]}]},\n",
-       "  {'file': '0000/0679.jpg',\n",
-       "   'max_detection_conf': 0.916,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0143,\n",
-       "     'bbox': [0.9344, 0.6181, 0.04739, 0.1062]},\n",
-       "    {'category': '1', 'conf': 0.84, 'bbox': [0.2014, 0.5906, 0.07977, 0.1002]},\n",
-       "    {'category': '1', 'conf': 0.873, 'bbox': [0.7077, 0.6193, 0.2132, 0.1766]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.916,\n",
-       "     'bbox': [0.3048, 0.6062, 0.1216, 0.1157]}]},\n",
-       "  {'file': '0000/0680.jpg',\n",
-       "   'max_detection_conf': 0.985,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.985,\n",
-       "     'bbox': [0.0007898, 0.6157, 0.4581, 0.3568]}]},\n",
-       "  {'file': '0000/0681.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.3364, 0.5095, 0.2448, 0.1968]}]},\n",
-       "  {'file': '0000/0682.jpg',\n",
-       "   'max_detection_conf': 0.905,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.905,\n",
-       "     'bbox': [0.431, 0.2047, 0.5543, 0.5221]}]},\n",
-       "  {'file': '0000/0683.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.05006, 0.3616, 0.5995, 0.6088]}]},\n",
-       "  {'file': '0000/0684.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.01587, 0.6512, 0.3284, 0.2583]}]},\n",
-       "  {'file': '0000/0685.jpg',\n",
-       "   'max_detection_conf': 0.877,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.872,\n",
-       "     'bbox': [0.536, 0.487, 0.1098, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.877,\n",
-       "     'bbox': [0.354, 0.5332, 0.2442, 0.2177]}]},\n",
-       "  {'file': '0000/0686.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.3431, 0.5719, 0.199, 0.1974]}]},\n",
-       "  {'file': '0000/0687.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.007326, 0.6752, 0.3956, 0.2952]}]},\n",
-       "  {'file': '0000/0688.jpg',\n",
-       "   'max_detection_conf': 0.927,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.3443, 0.5959, 0.1538, 0.1383]}]},\n",
-       "  {'file': '0000/0689.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.01709, 0.369, 0.6654, 0.6014]}]},\n",
-       "  {'file': '0000/0690.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.824,\n",
-       "     'bbox': [0.2051, 0.5608, 0.1611, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.4395, 0.4963, 0.1953, 0.1568]}]},\n",
-       "  {'file': '0000/0691.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4188, 0.4003, 0.1709, 0.2675]}]},\n",
-       "  {'file': '0000/0692.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.4652, 0.4926, 0.1929, 0.166]},\n",
-       "    {'category': '1', 'conf': 0.943, 'bbox': [0.293, 0.511, 0.1465, 0.1494]}]},\n",
-       "  {'file': '0000/1872.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2295, 0.1678, 0.7362, 0.7988]}]},\n",
-       "  {'file': '0000/1873.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.006105, 0.642, 0.3321, 0.3265]}]},\n",
-       "  {'file': '0000/1874.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.0354, 0.6309, 0.271, 0.3099]}]},\n",
-       "  {'file': '0000/1875.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.06349, 0.607, 0.3589, 0.2324]}]},\n",
-       "  {'file': '0000/1876.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.1868, 0.09963, 0.5836, 0.869]}]},\n",
-       "  {'file': '0000/1877.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0, 0.6365, 0.2625, 0.2712]}]},\n",
-       "  {'file': '0000/1878.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.009768, 0.6439, 0.2393, 0.297]}]},\n",
-       "  {'file': '0000/1879.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.008547, 0.6309, 0.1648, 0.3154]}]},\n",
-       "  {'file': '0000/1880.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.009768, 0.2804, 0.9108, 0.6918]}]},\n",
-       "  {'file': '0000/1881.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.004884, 0.38, 0.7045, 0.5166]}]},\n",
-       "  {'file': '0000/1882.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.1282, 0.4981, 0.5494, 0.3966]}]},\n",
-       "  {'file': '0000/1883.jpg',\n",
-       "   'max_detection_conf': 0.928,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.928,\n",
-       "     'bbox': [0.3968, 0.4963, 0.1587, 0.1808]}]},\n",
-       "  {'file': '0000/1884.jpg',\n",
-       "   'max_detection_conf': 0.902,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.902,\n",
-       "     'bbox': [0.238, 0.4963, 0.2783, 0.1734]}]},\n",
-       "  {'file': '0000/1885.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.419,\n",
-       "     'bbox': [0.7692, 0.5461, 0.04151, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.05128, 0.487, 0.4334, 0.4723]}]},\n",
-       "  {'file': '0000/1886.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '2',\n",
-       "     'conf': 0.0189,\n",
-       "     'bbox': [0, 0.03321, 0.04151, 0.2712]},\n",
-       "    {'category': '2', 'conf': 0.249, 'bbox': [0, 0.02952, 0.04273, 0.9372]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.07326, 0.5276, 0.5091, 0.4446]}]},\n",
-       "  {'file': '0000/1887.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.1709, 0.463, 0.2942, 0.5092]}]},\n",
-       "  {'file': '0000/1888.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.3199, 0.4815, 0.5177, 0.4907]}]},\n",
-       "  {'file': '0000/1889.jpg',\n",
-       "   'max_detection_conf': 0.19,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.188,\n",
-       "     'bbox': [0.4481, 0.5, 0.2356, 0.4151]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.19,\n",
-       "     'bbox': [0.4481, 0.5018, 0.2319, 0.1678]}]},\n",
-       "  {'file': '0000/1890.jpg',\n",
-       "   'max_detection_conf': 0.935,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.935,\n",
-       "     'bbox': [0.3504, 0.4649, 0.2124, 0.1918]}]},\n",
-       "  {'file': '0000/0932.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0118,\n",
-       "     'bbox': [0.1098, 0.4483, 0.03663, 0.06273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0185,\n",
-       "     'bbox': [0.1111, 0.4446, 0.1208, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0687,\n",
-       "     'bbox': [0.4456, 0.4741, 0.1062, 0.1162]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.158,\n",
-       "     'bbox': [0.4444, 0.4741, 0.03052, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.405,\n",
-       "     'bbox': [0.5103, 0.4704, 0.05372, 0.1697]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.415,\n",
-       "     'bbox': [0.1111, 0.4464, 0.06959, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.424,\n",
-       "     'bbox': [0.5128, 0.4704, 0.05006, 0.06642]},\n",
-       "    {'category': '1', 'conf': 0.915, 'bbox': [0.4493, 0.4741, 0.1452, 0.1881]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.3174, 0.4667, 0.1208, 0.225]}]},\n",
-       "  {'file': '0000/0933.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.465,\n",
-       "     'bbox': [0.7435, 0.8985, 0.2332, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.002442, 0.5756, 0.2454, 0.3966]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.3113, 0.487, 0.2368, 0.2952]}]},\n",
-       "  {'file': '0000/0934.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2893, 0.4557, 0.1916, 0.2361]}]},\n",
-       "  {'file': '0000/0935.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.773,\n",
-       "     'bbox': [0.6398, 0.5036, 0.1013, 0.1549]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.4322, 0.4833, 0.1965, 0.2011]}]},\n",
-       "  {'file': '0000/0936.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0128,\n",
-       "     'bbox': [0.06715, 0.4409, 0.08424, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.648,\n",
-       "     'bbox': [0.05738, 0.4409, 0.1123, 0.09963]},\n",
-       "    {'category': '1', 'conf': 0.783, 'bbox': [0.1697, 0.4298, 0.1465, 0.1328]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.2612, 0.4667, 0.2808, 0.2619]}]},\n",
-       "  {'file': '0000/0937.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3638, 0.4575, 0.2051, 0.2509]}]},\n",
-       "  {'file': '0000/0938.jpg',\n",
-       "   'max_detection_conf': 0.47,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0239,\n",
-       "     'bbox': [0.3382, 0.7011, 0.05616, 0.08487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.47,\n",
-       "     'bbox': [0.4493, 0.5867, 0.02319, 0.06273]}]},\n",
-       "  {'file': '0000/0939.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.2478, 0.4649, 0.2954, 0.2583]}]},\n",
-       "  {'file': '0000/0940.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0352,\n",
-       "     'bbox': [0.536, 0.5295, 0.03418, 0.03136]},\n",
-       "    {'category': '1', 'conf': 0.97, 'bbox': [0.2796, 0.511, 0.2283, 0.238]}]},\n",
-       "  {'file': '0000/0941.jpg',\n",
-       "   'max_detection_conf': 0.933,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0303,\n",
-       "     'bbox': [0.3418, 0.6937, 0.0525, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.933,\n",
-       "     'bbox': [0.4114, 0.6254, 0.06227, 0.1162]}]},\n",
-       "  {'file': '0000/0942.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.2405, 0.4686, 0.2698, 0.2822]}]},\n",
-       "  {'file': '0000/0943.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.3565, 0.4667, 0.2442, 0.225]}]},\n",
-       "  {'file': '0000/0944.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0105,\n",
-       "     'bbox': [0.5347, 0.5221, 0.0354, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0289,\n",
-       "     'bbox': [0.5238, 0.4483, 0.04761, 0.07749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.272,\n",
-       "     'bbox': [0.5225, 0.4483, 0.0354, 0.04797]},\n",
-       "    {'category': '1', 'conf': 0.552, 'bbox': [0, 0.428, 0.1001, 0.118]},\n",
-       "    {'category': '1', 'conf': 0.808, 'bbox': [0.1538, 0.4188, 0.1221, 0.1402]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.829,\n",
-       "     'bbox': [0.2661, 0.4428, 0.08791, 0.1162]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.885,\n",
-       "     'bbox': [0.01098, 0.535, 0.08669, 0.1162]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.3455, 0.4501, 0.1294, 0.2214]}]},\n",
-       "  {'file': '0000/0945.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0104,\n",
-       "     'bbox': [0.2771, 0.452, 0.04884, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0105,\n",
-       "     'bbox': [0.4969, 0.4557, 0.05616, 0.0535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0117,\n",
-       "     'bbox': [0.459, 0.4428, 0.04517, 0.08487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0164,\n",
-       "     'bbox': [0.4578, 0.4428, 0.07936, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0275,\n",
-       "     'bbox': [0.409, 0.5701, 0.03296, 0.07749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0465,\n",
-       "     'bbox': [0.525, 0.4981, 0.03785, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0816,\n",
-       "     'bbox': [0.4969, 0.4704, 0.03418, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.284,\n",
-       "     'bbox': [0.2991, 0.4557, 0.03418, 0.03321]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.362,\n",
-       "     'bbox': [0.5274, 0.4557, 0.02808, 0.04797]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.476,\n",
-       "     'bbox': [0.459, 0.4428, 0.03296, 0.03136]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.529,\n",
-       "     'bbox': [0.4981, 0.4667, 0.06349, 0.08671]},\n",
-       "    {'category': '1', 'conf': 0.743, 'bbox': [0.02564, 0.4298, 0.1184, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.764,\n",
-       "     'bbox': [0.3968, 0.4741, 0.0354, 0.07195]},\n",
-       "    {'category': '1', 'conf': 0.951, 'bbox': [0.4334, 0.463, 0.1184, 0.2841]},\n",
-       "    {'category': '1', 'conf': 0.97, 'bbox': [0.1831, 0.452, 0.2564, 0.3154]}]},\n",
-       "  {'file': '0000/0946.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.4322, 0.4428, 0.1623, 0.2103]}]},\n",
-       "  {'file': '0000/0947.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0144,\n",
-       "     'bbox': [0.597, 0.4557, 0.1123, 0.08671]},\n",
-       "    {'category': '1', 'conf': 0.0289, 'bbox': [0, 0.4538, 0.06593, 0.1291]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0351,\n",
-       "     'bbox': [0.01709, 0.4667, 0.04884, 0.07011]},\n",
-       "    {'category': '1', 'conf': 0.44, 'bbox': [0.597, 0.4575, 0.0525, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.812,\n",
-       "     'bbox': [0.02075, 0.4059, 0.1526, 0.1439]},\n",
-       "    {'category': '1', 'conf': 0.876, 'bbox': [0.2271, 0.4298, 0.1159, 0.1236]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.3992, 0.4612, 0.177, 0.1937]}]},\n",
-       "  {'file': '0000/0948.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.4407, 0.4686, 0.1111, 0.1863]}]},\n",
-       "  {'file': '0000/0949.jpg',\n",
-       "   'max_detection_conf': 0.899,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.899,\n",
-       "     'bbox': [0.4761, 0.5719, 0.1111, 0.1107]}]},\n",
-       "  {'file': '0000/0950.jpg',\n",
-       "   'max_detection_conf': 0.889,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0602,\n",
-       "     'bbox': [0.547, 0.5719, 0.06471, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.123,\n",
-       "     'bbox': [0.5653, 0.6088, 0.04884, 0.04981]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.392,\n",
-       "     'bbox': [0.5323, 0.5221, 0.0354, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.889,\n",
-       "     'bbox': [0.4774, 0.511, 0.04761, 0.07011]}]},\n",
-       "  {'file': '0000/0951.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.4861, 0.534, 0.1179, 0.2615]}]},\n",
-       "  {'file': '0000/0952.jpg',\n",
-       "   'max_detection_conf': 0.908,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.1965, 0.6285, 0.1601, 0.1318]}]},\n",
-       "  {'file': '0000/0953.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.2852, 0.6395, 0.1048, 0.1208]}]},\n",
-       "  {'file': '0000/0954.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.6288, 0.6329, 0.1455, 0.1054]}]},\n",
-       "  {'file': '0000/0955.jpg',\n",
-       "   'max_detection_conf': 0.907,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.0524, 0.6967, 0.1935, 0.1384]}]},\n",
-       "  {'file': '0000/0956.jpg',\n",
-       "   'max_detection_conf': 0.766,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.766,\n",
-       "     'bbox': [0.5473, 0.6219, 0.09898, 0.08791]}]},\n",
-       "  {'file': '0000/0957.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.6593, 0.5626, 0.2183, 0.4109]}]},\n",
-       "  {'file': '0000/0958.jpg',\n",
-       "   'max_detection_conf': 0.932,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.932,\n",
-       "     'bbox': [0.01018, 0.745, 0.5298, 0.2285]}]},\n",
-       "  {'file': '0000/0959.jpg',\n",
-       "   'max_detection_conf': 0.901,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.901,\n",
-       "     'bbox': [0.3871, 0.4989, 0.1848, 0.2]}]},\n",
-       "  {'file': '0000/0960.jpg',\n",
-       "   'max_detection_conf': 0.339,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0283,\n",
-       "     'bbox': [0.3813, 0.5692, 0.08005, 0.07032]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.339,\n",
-       "     'bbox': [0.425, 0.567, 0.03493, 0.07252]}]},\n",
-       "  {'file': '0000/0961.jpg',\n",
-       "   'max_detection_conf': 0.798,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0458,\n",
-       "     'bbox': [0.4381, 0.5208, 0.06259, 0.1274]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.798,\n",
-       "     'bbox': [0.4643, 0.5582, 0.03639, 0.0945]}]},\n",
-       "  {'file': '0000/0962.jpg',\n",
-       "   'max_detection_conf': 0.889,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.889,\n",
-       "     'bbox': [0.3712, 0.5464, 0.1585, 0.1239]}]},\n",
-       "  {'file': '0000/0963.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.5018, 0.5436, 0.1361, 0.1971]}]},\n",
-       "  {'file': '0000/0964.jpg',\n",
-       "   'max_detection_conf': 0.821,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0135,\n",
-       "     'bbox': [0.472, 0.4169, 0.04104, 0.04788]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.821,\n",
-       "     'bbox': [0.5223, 0.6225, 0.0485, 0.09577]}]},\n",
-       "  {'file': '0000/0965.jpg',\n",
-       "   'max_detection_conf': 0.911,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0135,\n",
-       "     'bbox': [0.4235, 0.4056, 0.02425, 0.06478]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.911,\n",
-       "     'bbox': [0.4458, 0.5802, 0.09701, 0.1408]}]},\n",
-       "  {'file': '0000/0966.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.4352, 0.5274, 0.2154, 0.1912]}]},\n",
-       "  {'file': '0000/0501.jpg',\n",
-       "   'max_detection_conf': 0.879,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.879,\n",
-       "     'bbox': [0.4456, 0.5608, 0.105, 0.1033]}]},\n",
-       "  {'file': '0000/0502.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.4065, 0.5442, 0.1514, 0.2472]}]},\n",
-       "  {'file': '0000/0503.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.2661, 0.5904, 0.2551, 0.1439]}]},\n",
-       "  {'file': '0000/0504.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.2539, 0.5535, 0.1684, 0.1033]}]},\n",
-       "  {'file': '0000/0505.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.3089, 0.5092, 0.2185, 0.1918]}]},\n",
-       "  {'file': '0000/0506.jpg',\n",
-       "   'max_detection_conf': 0.93,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.779,\n",
-       "     'bbox': [0.4078, 0.345, 0.08302, 0.225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.93,\n",
-       "     'bbox': [0.4761, 0.3007, 0.1245, 0.3616]}]},\n",
-       "  {'file': '0000/0507.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.577,\n",
-       "     'bbox': [0.3101, 0.3523, 0.06105, 0.1476]},\n",
-       "    {'category': '1', 'conf': 0.884, 'bbox': [0.3406, 0.3745, 0.1306, 0.2324]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.4566, 0.2804, 0.1306, 0.4391]}]},\n",
-       "  {'file': '0000/0508.jpg',\n",
-       "   'max_detection_conf': 0.906,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.783,\n",
-       "     'bbox': [0.6263, 0.3265, 0.105, 0.297]},\n",
-       "    {'category': '1', 'conf': 0.85, 'bbox': [0.4163, 0.2398, 0.1159, 0.3542]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.906,\n",
-       "     'bbox': [0.4346, 0.3597, 0.2197, 0.3228]}]},\n",
-       "  {'file': '0000/0509.jpg',\n",
-       "   'max_detection_conf': 0.698,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0366,\n",
-       "     'bbox': [0.5726, 0.1623, 0.398, 0.4815]},\n",
-       "    {'category': '1', 'conf': 0.354, 'bbox': [0.2051, 0.2675, 0.2893, 0.7011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.692,\n",
-       "     'bbox': [0.01098, 0.1826, 0.2771, 0.7712]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.698,\n",
-       "     'bbox': [0.3003, 0.1605, 0.6715, 0.8099]}]},\n",
-       "  {'file': '0000/0510.jpg',\n",
-       "   'max_detection_conf': 0.865,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0108,\n",
-       "     'bbox': [0.001221, 0.4114, 0.2918, 0.3929]},\n",
-       "    {'category': '1', 'conf': 0.0118, 'bbox': [0.663, 0.3413, 0.2258, 0.3726]},\n",
-       "    {'category': '1', 'conf': 0.0132, 'bbox': [0.1953, 0.4612, 0.1758, 0.356]},\n",
-       "    {'category': '1', 'conf': 0.0133, 'bbox': [0.768, 0.3819, 0.1282, 0.3044]},\n",
-       "    {'category': '1', 'conf': 0.259, 'bbox': [0.1404, 0.4667, 0.1562, 0.3413]},\n",
-       "    {'category': '1', 'conf': 0.647, 'bbox': [0.271, 0.5553, 0.1074, 0.2804]},\n",
-       "    {'category': '1', 'conf': 0.716, 'bbox': [0.8522, 0.214, 0.1306, 0.7546]},\n",
-       "    {'category': '1', 'conf': 0.809, 'bbox': [0.6568, 0.4317, 0.21, 0.5387]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.865,\n",
-       "     'bbox': [0.3626, 0.3357, 0.5115, 0.6309]}]},\n",
-       "  {'file': '0000/0511.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.398, 0.6328, 0.3028, 0.3007]}]},\n",
-       "  {'file': '0000/0512.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.4603, 0.5147, 0.1245, 0.1402]}]},\n",
-       "  {'file': '0000/0513.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.3406, 0.2933, 0.2344, 0.3247]}]},\n",
-       "  {'file': '0000/0514.jpg',\n",
-       "   'max_detection_conf': 0.793,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.782,\n",
-       "     'bbox': [0.7179, 0.5885, 0.08302, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.793,\n",
-       "     'bbox': [0.481, 0.6217, 0.09523, 0.142]}]},\n",
-       "  {'file': '0000/0515.jpg',\n",
-       "   'max_detection_conf': 0.859,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.859,\n",
-       "     'bbox': [0.4297, 0.5295, 0.05738, 0.09409]}]},\n",
-       "  {'file': '0000/0516.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.007326, 0.5276, 0.6422, 0.4391]}]},\n",
-       "  {'file': '0000/0517.jpg',\n",
-       "   'max_detection_conf': 0.934,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.934,\n",
-       "     'bbox': [0.5164, 0.6531, 0.2002, 0.2214]}]},\n",
-       "  {'file': '0000/0518.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0112,\n",
-       "     'bbox': [0.442, 0.4409, 0.09035, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.021,\n",
-       "     'bbox': [0.8998, 0.2896, 0.04639, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.493,\n",
-       "     'bbox': [0.009768, 0.7214, 0.1013, 0.1752]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.5372, 0.5369, 0.2722, 0.2933]}]},\n",
-       "  {'file': '0000/0519.jpg',\n",
-       "   'max_detection_conf': 0.826,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0507,\n",
-       "     'bbox': [0.4188, 0.4428, 0.1855, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.826,\n",
-       "     'bbox': [0.3321, 0.4501, 0.1245, 0.1771]}]},\n",
-       "  {'file': '0000/0520.jpg',\n",
-       "   'max_detection_conf': 0.927,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.3272, 0.3653, 0.1269, 0.2453]}]},\n",
-       "  {'file': '0000/0014.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0231,\n",
-       "     'bbox': [0.4407, 0.6586, 0.02808, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0334,\n",
-       "     'bbox': [0.442, 0.6586, 0.0525, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0491,\n",
-       "     'bbox': [0.4407, 0.6549, 0.05616, 0.1974]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.816,\n",
-       "     'bbox': [0.2844, 0.6531, 0.05494, 0.2066]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0.3724, 0.6678, 0.04151, 0.1881]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.1562, 0.6863, 0.07448, 0.2527]},\n",
-       "    {'category': '1', 'conf': 0.937, 'bbox': [0.3846, 0.6476, 0.1111, 0.2195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.4908, 0.6568, 0.1196, 0.2435]}]},\n",
-       "  {'file': '0000/0015.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.715,\n",
-       "     'bbox': [0.2759, 0.6383, 0.0818, 0.1402]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.836,\n",
-       "     'bbox': [0.4774, 0.6125, 0.09157, 0.2324]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.5653, 0.6808, 0.06471, 0.2232]},\n",
-       "    {'category': '1', 'conf': 0.949, 'bbox': [0.354, 0.6402, 0.1013, 0.2435]},\n",
-       "    {'category': '1', 'conf': 0.953, 'bbox': [0.4065, 0.618, 0.105, 0.297]}]},\n",
-       "  {'file': '0000/0016.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0219,\n",
-       "     'bbox': [0.4297, 0.6457, 0.02319, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.051,\n",
-       "     'bbox': [0.4334, 0.6309, 0.05006, 0.0535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.057,\n",
-       "     'bbox': [0.4224, 0.6328, 0.06105, 0.1365]},\n",
-       "    {'category': '1', 'conf': 0.75, 'bbox': [0.4151, 0.6457, 0.04517, 0.1845]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.883,\n",
-       "     'bbox': [0.6117, 0.6476, 0.04761, 0.1734]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.897,\n",
-       "     'bbox': [0.4273, 0.6328, 0.09523, 0.2047]},\n",
-       "    {'category': '1', 'conf': 0.949, 'bbox': [0.5299, 0.6789, 0.0586, 0.1605]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.3137, 0.6808, 0.1098, 0.1697]}]},\n",
-       "  {'file': '0000/0017.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.776,\n",
-       "     'bbox': [0.6239, 0.6494, 0.03907, 0.1678]},\n",
-       "    {'category': '1', 'conf': 0.94, 'bbox': [0.6947, 0.5996, 0.08547, 0.2121]},\n",
-       "    {'category': '1', 'conf': 0.946, 'bbox': [0.3931, 0.6494, 0.1172, 0.1789]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.492, 0.6328, 0.1208, 0.2472]}]},\n",
-       "  {'file': '0000/0018.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0124,\n",
-       "     'bbox': [0.4993, 0.6365, 0.03052, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0148,\n",
-       "     'bbox': [0.4444, 0.6346, 0.0586, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0161,\n",
-       "     'bbox': [0.4737, 0.7546, 0.02564, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0362,\n",
-       "     'bbox': [0.492, 0.6383, 0.05494, 0.1734]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.11,\n",
-       "     'bbox': [0.5006, 0.6383, 0.02075, 0.04428]},\n",
-       "    {'category': '1', 'conf': 0.851, 'bbox': [0.3663, 0.607, 0.08669, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.924,\n",
-       "     'bbox': [0.3553, 0.6955, 0.09523, 0.2011]},\n",
-       "    {'category': '1', 'conf': 0.944, 'bbox': [0.536, 0.6383, 0.08913, 0.225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.4432, 0.6051, 0.1074, 0.2915]}]},\n",
-       "  {'file': '0000/0019.jpg',\n",
-       "   'max_detection_conf': 0.903,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0765,\n",
-       "     'bbox': [0.5177, 0.6531, 0.04761, 0.1476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.496,\n",
-       "     'bbox': [0.5103, 0.6512, 0.05372, 0.06826]},\n",
-       "    {'category': '1', 'conf': 0.665, 'bbox': [0.575, 0.6678, 0.06105, 0.1642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.825,\n",
-       "     'bbox': [0.3418, 0.6476, 0.05982, 0.2066]},\n",
-       "    {'category': '1', 'conf': 0.891, 'bbox': [0.3943, 0.6402, 0.1208, 0.1734]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.903,\n",
-       "     'bbox': [0.5238, 0.6199, 0.09279, 0.2232]}]},\n",
-       "  {'file': '0000/0020.jpg',\n",
-       "   'max_detection_conf': 0.932,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.011,\n",
-       "     'bbox': [0.5347, 0.6162, 0.03907, 0.1937]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0323,\n",
-       "     'bbox': [0.4065, 0.6678, 0.0757, 0.1789]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0546,\n",
-       "     'bbox': [0.4676, 0.5977, 0.1001, 0.2232]},\n",
-       "    {'category': '1', 'conf': 0.639, 'bbox': [0.47, 0.5977, 0.08669, 0.1088]},\n",
-       "    {'category': '1', 'conf': 0.676, 'bbox': [0.431, 0.6568, 0.06471, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.792,\n",
-       "     'bbox': [0.5409, 0.6236, 0.03174, 0.08487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.824,\n",
-       "     'bbox': [0.7326, 0.5922, 0.04395, 0.2011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.842,\n",
-       "     'bbox': [0.4004, 0.6752, 0.04029, 0.1715]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.932,\n",
-       "     'bbox': [0.4578, 0.6568, 0.1355, 0.238]}]},\n",
-       "  {'file': '0000/0021.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0764,\n",
-       "     'bbox': [0.3321, 0.6512, 0.1025, 0.08487]},\n",
-       "    {'category': '1', 'conf': 0.0788, 'bbox': [0.387, 0.618, 0.07692, 0.1162]},\n",
-       "    {'category': '1', 'conf': 0.418, 'bbox': [0.3333, 0.6291, 0.1282, 0.1697]},\n",
-       "    {'category': '1', 'conf': 0.753, 'bbox': [0.387, 0.6365, 0.06837, 0.2121]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.869,\n",
-       "     'bbox': [0.7216, 0.6014, 0.04761, 0.1937]},\n",
-       "    {'category': '1', 'conf': 0.873, 'bbox': [0.3894, 0.6623, 0.1391, 0.2103]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.5286, 0.6254, 0.1135, 0.2011]}]},\n",
-       "  {'file': '0000/0022.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.132,\n",
-       "     'bbox': [0.2796, 0.6992, 0.04761, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.804,\n",
-       "     'bbox': [0.2087, 0.6549, 0.05616, 0.1568]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.819,\n",
-       "     'bbox': [0.2796, 0.6992, 0.06959, 0.1715]},\n",
-       "    {'category': '1', 'conf': 0.882, 'bbox': [0.3663, 0.642, 0.05128, 0.2121]},\n",
-       "    {'category': '1', 'conf': 0.942, 'bbox': [0.503, 0.6531, 0.1318, 0.2416]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.4432, 0.6623, 0.1013, 0.1863]}]},\n",
-       "  {'file': '0000/0023.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.556,\n",
-       "     'bbox': [0.4383, 0.6254, 0.04273, 0.06273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.631,\n",
-       "     'bbox': [0.7777, 0.5774, 0.05128, 0.1586]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.887,\n",
-       "     'bbox': [0.1941, 0.6494, 0.08058, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.914,\n",
-       "     'bbox': [0.5921, 0.6033, 0.04273, 0.2084]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.5164, 0.6715, 0.06105, 0.1549]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.3736, 0.6476, 0.1159, 0.2029]}]},\n",
-       "  {'file': '0000/0024.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0288,\n",
-       "     'bbox': [0.6935, 0.5719, 0.0586, 0.04981]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.707,\n",
-       "     'bbox': [0.6923, 0.6107, 0.08791, 0.1236]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.4273, 0.6494, 0.1147, 0.225]}]},\n",
-       "  {'file': '0000/0025.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.017,\n",
-       "     'bbox': [0.3272, 0.6808, 0.04639, 0.2011]},\n",
-       "    {'category': '1', 'conf': 0.9, 'bbox': [0.1721, 0.6605, 0.1782, 0.2675]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.4358, 0.6494, 0.1159, 0.2453]}]},\n",
-       "  {'file': '0000/0026.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.876,\n",
-       "     'bbox': [0.2673, 0.6143, 0.0757, 0.2361]},\n",
-       "    {'category': '1', 'conf': 0.935, 'bbox': [0.1343, 0.6309, 0.0586, 0.2601]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.4566, 0.6125, 0.06471, 0.2103]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.5347, 0.6531, 0.06227, 0.1752]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.3577, 0.6383, 0.0989, 0.2306]}]},\n",
-       "  {'file': '0000/0027.jpg',\n",
-       "   'max_detection_conf': 0.916,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0186,\n",
-       "     'bbox': [0.2796, 0.5369, 0.04761, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0685,\n",
-       "     'bbox': [0.5262, 0.5904, 0.05616, 0.07011]},\n",
-       "    {'category': '1', 'conf': 0.916, 'bbox': [0, 0.5239, 0.2002, 0.4391]}]},\n",
-       "  {'file': '0000/0028.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0146,\n",
-       "     'bbox': [0, 0.4022, 0.07936, 0.5627]},\n",
-       "    {'category': '1', 'conf': 0.0202, 'bbox': [0, 0.4022, 0.07936, 0.2158]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.481, 0.6051, 0.0818, 0.1107]}]},\n",
-       "  {'file': '0000/0029.jpg',\n",
-       "   'max_detection_conf': 0.916,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0305,\n",
-       "     'bbox': [0.293, 0.5608, 0.05372, 0.08856]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.916,\n",
-       "     'bbox': [0.4493, 0.5885, 0.04395, 0.09409]}]},\n",
-       "  {'file': '0000/0030.jpg',\n",
-       "   'max_detection_conf': 0.414,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.023,\n",
-       "     'bbox': [0.5641, 0.6199, 0.0354, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.414,\n",
-       "     'bbox': [0.4945, 0.559, 0.0354, 0.09963]}]},\n",
-       "  {'file': '0000/0031.jpg',\n",
-       "   'max_detection_conf': 0.859,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0383,\n",
-       "     'bbox': [0.4542, 0.6014, 0.05616, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0396,\n",
-       "     'bbox': [0.5482, 0.6051, 0.05738, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.859,\n",
-       "     'bbox': [0.409, 0.6014, 0.1013, 0.1125]}]},\n",
-       "  {'file': '0000/0032.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.01098, 0.5166, 0.3785, 0.3597]}]},\n",
-       "  {'file': '0000/0033.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.1746, 0.5424, 0.3601, 0.4077]}]},\n",
-       "  {'file': '0000/0034.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0104,\n",
-       "     'bbox': [0, 0.3099, 0.07448, 0.3394]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.3638, 0.4815, 0.1916, 0.2527]}]},\n",
-       "  {'file': '0000/0035.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.2832, 0.5424, 0.398, 0.3745]}]},\n",
-       "  {'file': '0000/0036.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0155,\n",
-       "     'bbox': [0.001221, 0.321, 0.05982, 0.2509]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.001221, 0.5498, 0.3125, 0.4225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.2478, 0.6051, 0.2967, 0.3653]}]},\n",
-       "  {'file': '0000/0037.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0, 0.6273, 0.4053, 0.3431]}]},\n",
-       "  {'file': '0000/0038.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.2783, 0.4963, 0.3174, 0.345]}]},\n",
-       "  {'file': '0000/0039.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.4505, 0.511, 0.1782, 0.2287]}]},\n",
-       "  {'file': '0000/0040.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0115,\n",
-       "     'bbox': [0, 0.3247, 0.06715, 0.3228]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.2918, 0.5092, 0.2148, 0.3007]}]},\n",
-       "  {'file': '0000/0041.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0, 0.5627, 0.3211, 0.4077]}]},\n",
-       "  {'file': '0000/0042.jpg',\n",
-       "   'max_detection_conf': 0.981,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.981,\n",
-       "     'bbox': [0.2808, 0.5369, 0.5164, 0.38]}]},\n",
-       "  {'file': '0000/0043.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3199, 0.4077, 0.4346, 0.4833]}]},\n",
-       "  {'file': '0000/0044.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.4749, 0.4151, 0.1245, 0.3763]}]},\n",
-       "  {'file': '0000/1677.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0409,\n",
-       "     'bbox': [0.9279, 0.7435, 0.07203, 0.2306]},\n",
-       "    {'category': '1', 'conf': 0.98, 'bbox': [0.2808, 0.5055, 0.326, 0.3856]}]},\n",
-       "  {'file': '0000/1678.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.876,\n",
-       "     'bbox': [0.5177, 0.4667, 0.1172, 0.3782]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.5824, 0.4059, 0.1062, 0.2878]}]},\n",
-       "  {'file': '0000/1679.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.4371, 0.38, 0.2942, 0.321]}]},\n",
-       "  {'file': '0000/1680.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3516, 0.5535, 0.1611, 0.1586]}]},\n",
-       "  {'file': '0000/1681.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.4908, 0.4391, 0.2844, 0.2712]}]},\n",
-       "  {'file': '0000/1682.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.909,\n",
-       "     'bbox': [0.6788, 0.3708, 0.315, 0.3948]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.2942, 0.3173, 0.3589, 0.3782]}]},\n",
-       "  {'file': '0000/1683.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.01098, 0.03505, 0.9023, 0.9354]}]},\n",
-       "  {'file': '0000/1684.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.3479, 0.4741, 0.21, 0.1881]}]},\n",
-       "  {'file': '0000/1685.jpg',\n",
-       "   'max_detection_conf': 0.921,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.921,\n",
-       "     'bbox': [0.6263, 0.4833, 0.1367, 0.09594]}]},\n",
-       "  {'file': '0000/1686.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.6312, 0.03505, 0.3687, 0.69]}]},\n",
-       "  {'file': '0000/1687.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0, 0.1309, 0.2722, 0.4649]}]},\n",
-       "  {'file': '0000/1688.jpg',\n",
-       "   'max_detection_conf': 0.921,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.921,\n",
-       "     'bbox': [0.001221, 0.03136, 0.3418, 0.8284]}]},\n",
-       "  {'file': '0000/1689.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.01465, 0.03136, 0.5738, 0.6494]}]},\n",
-       "  {'file': '0000/1690.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.5177, 0.03136, 0.4749, 0.7158]}]},\n",
-       "  {'file': '0000/1691.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.3772, 0.607, 0.271, 0.3597]}]},\n",
-       "  {'file': '0000/1692.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.06227, 0.5738, 0.4407, 0.3653]}]},\n",
-       "  {'file': '0000/1693.jpg',\n",
-       "   'max_detection_conf': 0.894,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.894,\n",
-       "     'bbox': [0.5982, 0.5774, 0.1684, 0.1586]}]},\n",
-       "  {'file': '0000/1694.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.367,\n",
-       "     'bbox': [0.01343, 0.1771, 0.08913, 0.09594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.431,\n",
-       "     'bbox': [0.4285, 0.06088, 0.06349, 0.1162]},\n",
-       "    {'category': '1', 'conf': 0.965, 'bbox': [0.271, 0.5202, 0.376, 0.3653]}]},\n",
-       "  {'file': '0000/1695.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.812,\n",
-       "     'bbox': [0.3089, 0.3892, 0.1697, 0.2822]},\n",
-       "    {'category': '1', 'conf': 0.954, 'bbox': [0.3882, 0.4335, 0.2271, 0.3376]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.1697, 0.4095, 0.2881, 0.3302]}]},\n",
-       "  {'file': '0000/0992.jpg',\n",
-       "   'max_detection_conf': 0.921,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.921,\n",
-       "     'bbox': [0.0354, 0.5129, 0.3369, 0.4501]}]},\n",
-       "  {'file': '0000/0993.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.4371, 0.3929, 0.0818, 0.2084]}]},\n",
-       "  {'file': '0000/0994.jpg',\n",
-       "   'max_detection_conf': 0.885,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.885,\n",
-       "     'bbox': [0.01098, 0.4428, 0.3089, 0.2878]}]},\n",
-       "  {'file': '0000/0995.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.3601, 0.4132, 0.2332, 0.3745]}]},\n",
-       "  {'file': '0000/0996.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0, 0.4778, 0.2014, 0.2988]},\n",
-       "    {'category': '1', 'conf': 0.881, 'bbox': [0.6971, 0.4132, 0.2576, 0.2546]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.2539, 0.4132, 0.3687, 0.2878]}]},\n",
-       "  {'file': '0000/0997.jpg',\n",
-       "   'max_detection_conf': 0.933,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.933,\n",
-       "     'bbox': [0.6984, 0.4667, 0.2112, 0.2029]}]},\n",
-       "  {'file': '0000/0998.jpg',\n",
-       "   'max_detection_conf': 0.892,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.892,\n",
-       "     'bbox': [0.3626, 0.607, 0.1001, 0.1217]}]},\n",
-       "  {'file': '0000/0999.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.4175, 0.4225, 0.2356, 0.2343]}]},\n",
-       "  {'file': '0000/1000.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.917,\n",
-       "     'bbox': [0.4273, 0.4243, 0.1013, 0.2749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.1159, 0.4428, 0.3748, 0.3081]}]},\n",
-       "  {'file': '0000/1001.jpg',\n",
-       "   'max_detection_conf': 0.898,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.026,\n",
-       "     'bbox': [0.492, 0.4335, 0.2759, 0.4649]},\n",
-       "    {'category': '1', 'conf': 0.854, 'bbox': [0.7838, 0.4372, 0.1086, 0.2767]},\n",
-       "    {'category': '1', 'conf': 0.883, 'bbox': [0.4737, 0.4372, 0.2991, 0.1918]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.898,\n",
-       "     'bbox': [0.315, 0.5018, 0.5824, 0.4704]}]},\n",
-       "  {'file': '0000/1002.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.4273, 0.4778, 0.1807, 0.3357]}]},\n",
-       "  {'file': '0000/1003.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.1672, 0.4741, 0.249, 0.3228]}]},\n",
-       "  {'file': '0000/1004.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.01709, 0.5184, 0.3772, 0.2527]}]},\n",
-       "  {'file': '0000/1005.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.5909, 0.4243, 0.409, 0.5479]}]},\n",
-       "  {'file': '0000/1006.jpg',\n",
-       "   'max_detection_conf': 0.852,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.847,\n",
-       "     'bbox': [0.4151, 0.5092, 0.06593, 0.08118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.852,\n",
-       "     'bbox': [0.2967, 0.5461, 0.03418, 0.09225]}]},\n",
-       "  {'file': '0000/1007.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.009768, 0.03136, 0.5677, 0.7214]}]},\n",
-       "  {'file': '0000/1008.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.5543, 0.4575, 0.1941, 0.1586]}]},\n",
-       "  {'file': '0000/1009.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.106,\n",
-       "     'bbox': [0.3321, 0.4225, 0.2258, 0.3726]},\n",
-       "    {'category': '1', 'conf': 0.733, 'bbox': [0.3394, 0.4225, 0.2185, 0.1678]},\n",
-       "    {'category': '1', 'conf': 0.909, 'bbox': [0.04273, 0.4317, 0.3369, 0.5]},\n",
-       "    {'category': '1', 'conf': 0.969, 'bbox': [0.3223, 0.4594, 0.597, 0.511]}]},\n",
-       "  {'file': '0000/1138.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.7118, 0.3265, 0.2673, 0.3542]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.4749, 0.3616, 0.1343, 0.2269]}]},\n",
-       "  {'file': '0000/1139.jpg',\n",
-       "   'max_detection_conf': 0.92,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0152,\n",
-       "     'bbox': [0.9328, 0.3265, 0.04273, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0172,\n",
-       "     'bbox': [0.2881, 0.3542, 0.1147, 0.08487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.044,\n",
-       "     'bbox': [0.9377, 0.3007, 0.06227, 0.1236]},\n",
-       "    {'category': '1', 'conf': 0.202, 'bbox': [0.923, 0.3265, 0.07203, 0.1531]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.664,\n",
-       "     'bbox': [0.6446, 0.3302, 0.1257, 0.09409]},\n",
-       "    {'category': '1', 'conf': 0.686, 'bbox': [0.2478, 0.2952, 0.3186, 0.4833]},\n",
-       "    {'category': '1', 'conf': 0.851, 'bbox': [0.1648, 0.6309, 0.304, 0.3394]},\n",
-       "    {'category': '1', 'conf': 0.894, 'bbox': [0.4652, 0.404, 0.3394, 0.463]},\n",
-       "    {'category': '1', 'conf': 0.904, 'bbox': [0.7106, 0.3173, 0.2014, 0.3044]},\n",
-       "    {'category': '1', 'conf': 0.92, 'bbox': [0, 0.3062, 0.2332, 0.6439]}]},\n",
-       "  {'file': '0000/1140.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.2661, 0.2952, 0.2576, 0.3007]}]},\n",
-       "  {'file': '0000/1141.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.243,\n",
-       "     'bbox': [0.8656, 0.3339, 0.07326, 0.08671]},\n",
-       "    {'category': '1', 'conf': 0.63, 'bbox': [0.525, 0.3523, 0.04029, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.73,\n",
-       "     'bbox': [0.4688, 0.3542, 0.03907, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.769,\n",
-       "     'bbox': [0.8803, 0.3118, 0.09645, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.2344, 0.3653, 0.2967, 0.2546]}]},\n",
-       "  {'file': '0000/1142.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.3846, 0.5092, 0.07814, 0.1143]}]},\n",
-       "  {'file': '0000/1143.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.4175, 0.5977, 0.1062, 0.3726]}]},\n",
-       "  {'file': '0000/1144.jpg',\n",
-       "   'max_detection_conf': 0.88,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.88,\n",
-       "     'bbox': [0.5531, 0.4852, 0.1111, 0.09409]}]},\n",
-       "  {'file': '0000/1145.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3858, 0.4077, 0.1245, 0.2066]}]},\n",
-       "  {'file': '0000/1146.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0185,\n",
-       "     'bbox': [0, 0.3228, 0.03418, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.4529, 0.4944, 0.1782, 0.1254]}]},\n",
-       "  {'file': '0000/1147.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.2967, 0.3671, 0.2466, 0.2509]}]},\n",
-       "  {'file': '0000/1148.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0129,\n",
-       "     'bbox': [0.001221, 0.3376, 0.03907, 0.08302]},\n",
-       "    {'category': '1', 'conf': 0.868, 'bbox': [0.6752, 0.7878, 0.3028, 0.1845]},\n",
-       "    {'category': '1', 'conf': 0.949, 'bbox': [0.6556, 0.3966, 0.199, 0.4206]},\n",
-       "    {'category': '1', 'conf': 0.951, 'bbox': [0.492, 0.3634, 0.1562, 0.214]}]},\n",
-       "  {'file': '0000/1149.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0154,\n",
-       "     'bbox': [0.004884, 0.7066, 0.1501, 0.1549]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.4102, 0.369, 0.4993, 0.6033]}]},\n",
-       "  {'file': '0000/1150.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.304, 0.4446, 0.47, 0.4778]}]},\n",
-       "  {'file': '0000/1151.jpg',\n",
-       "   'max_detection_conf': 0.845,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.845,\n",
-       "     'bbox': [0.3431, 0.5239, 0.1135, 0.07011]}]},\n",
-       "  {'file': '0000/1152.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0123,\n",
-       "     'bbox': [0.5103, 0.4889, 0.04395, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.4566, 0.4686, 0.0989, 0.1642]}]},\n",
-       "  {'file': '0000/1153.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4456, 0.511, 0.1831, 0.2749]}]},\n",
-       "  {'file': '0000/1154.jpg',\n",
-       "   'max_detection_conf': 0.672,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.355,\n",
-       "     'bbox': [0.4322, 0.5332, 0.05372, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.672,\n",
-       "     'bbox': [0.2905, 0.618, 0.0525, 0.07933]}]},\n",
-       "  {'file': '0000/1155.jpg',\n",
-       "   'max_detection_conf': 0.913,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.913,\n",
-       "     'bbox': [0.2258, 0.03136, 0.7545, 0.9372]}]},\n",
-       "  {'file': '0000/0654.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.2985, 0.5214, 0.1753, 0.1706]}]},\n",
-       "  {'file': '0000/0655.jpg',\n",
-       "   'max_detection_conf': 0.888,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.888,\n",
-       "     'bbox': [0.597, 0.4501, 0.133, 0.142]}]},\n",
-       "  {'file': '0000/0656.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.5763, 0.5147, 0.3272, 0.3173]}]},\n",
-       "  {'file': '0000/0657.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2673, 0.4354, 0.1452, 0.1808]}]},\n",
-       "  {'file': '0000/0658.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.1367, 0.02952, 0.8522, 0.9428]}]},\n",
-       "  {'file': '0000/0659.jpg',\n",
-       "   'max_detection_conf': 0.659,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0134,\n",
-       "     'bbox': [0.4481, 0.2712, 0.03296, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.476,\n",
-       "     'bbox': [0.757, 0.4538, 0.05982, 0.07749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.659,\n",
-       "     'bbox': [0.5152, 0.4317, 0.05006, 0.08302]}]},\n",
-       "  {'file': '0000/0660.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.2967, 0.4409, 0.26, 0.4225]}]},\n",
-       "  {'file': '0000/0661.jpg',\n",
-       "   'max_detection_conf': 0.878,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0129,\n",
-       "     'bbox': [0.199, 0.3948, 0.04761, 0.04428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.055,\n",
-       "     'bbox': [0.3687, 0.4151, 0.02808, 0.05166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0635,\n",
-       "     'bbox': [0.3894, 0.3726, 0.03785, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.318,\n",
-       "     'bbox': [0.1611, 0.3948, 0.08669, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.699,\n",
-       "     'bbox': [0.4542, 0.4003, 0.09645, 0.08118]},\n",
-       "    {'category': '1', 'conf': 0.831, 'bbox': [0.2417, 0.4169, 0.1184, 0.1199]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.878,\n",
-       "     'bbox': [0.01343, 0.4354, 0.177, 0.1586]}]},\n",
-       "  {'file': '0000/0662.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0101,\n",
-       "     'bbox': [0.9267, 0.3929, 0.04517, 0.2619]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.912,\n",
-       "     'bbox': [0.2332, 0.4704, 0.07814, 0.1217]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4065, 0.3597, 0.3614, 0.6107]}]},\n",
-       "  {'file': '0000/0663.jpg',\n",
-       "   'max_detection_conf': 0.934,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0814,\n",
-       "     'bbox': [0.5372, 0.4483, 0.04639, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.934,\n",
-       "     'bbox': [0.442, 0.3985, 0.07081, 0.1309]}]},\n",
-       "  {'file': '0000/0664.jpg',\n",
-       "   'max_detection_conf': 0.921,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0165,\n",
-       "     'bbox': [0.4102, 0.4298, 0.06959, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0172,\n",
-       "     'bbox': [0.6923, 0.3966, 0.05372, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0313,\n",
-       "     'bbox': [0.8205, 0.3966, 0.1062, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.921,\n",
-       "     'bbox': [0.4114, 0.4206, 0.1465, 0.1346]}]},\n",
-       "  {'file': '0000/0665.jpg',\n",
-       "   'max_detection_conf': 0.899,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0398,\n",
-       "     'bbox': [0.133, 0.4723, 0.1013, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0427,\n",
-       "     'bbox': [0.04395, 0.4741, 0.1062, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.859,\n",
-       "     'bbox': [0.2686, 0.4723, 0.06837, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.899,\n",
-       "     'bbox': [0.4224, 0.4225, 0.1575, 0.1476]}]},\n",
-       "  {'file': '0000/0666.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0145,\n",
-       "     'bbox': [0.1929, 0.4409, 0.07448, 0.118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0333,\n",
-       "     'bbox': [0.2673, 0.5424, 0.03418, 0.04612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.069,\n",
-       "     'bbox': [0.1941, 0.4409, 0.05494, 0.07195]},\n",
-       "    {'category': '1', 'conf': 0.672, 'bbox': [0.2087, 0.4649, 0.1196, 0.1291]},\n",
-       "    {'category': '1', 'conf': 0.919, 'bbox': [0.1501, 0.4575, 0.1221, 0.1937]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.3357, 0.4261, 0.1831, 0.1826]}]},\n",
-       "  {'file': '0000/0667.jpg',\n",
-       "   'max_detection_conf': 0.881,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0213,\n",
-       "     'bbox': [0.1074, 0.225, 0.01343, 0.02767]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0268,\n",
-       "     'bbox': [0.9279, 0.3966, 0.07203, 0.3726]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0729,\n",
-       "     'bbox': [0.9291, 0.4003, 0.04151, 0.2675]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.695,\n",
-       "     'bbox': [0.2869, 0.4335, 0.04395, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.835,\n",
-       "     'bbox': [0.3015, 0.4612, 0.08058, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.881,\n",
-       "     'bbox': [0.4822, 0.4594, 0.08302, 0.1346]}]},\n",
-       "  {'file': '0000/0668.jpg',\n",
-       "   'max_detection_conf': 0.9,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.9,\n",
-       "     'bbox': [0.4554, 0.4114, 0.1294, 0.1107]}]},\n",
-       "  {'file': '0000/0669.jpg',\n",
-       "   'max_detection_conf': 0.922,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.016,\n",
-       "     'bbox': [0.9328, 0.5571, 0.04151, 0.1439]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0165,\n",
-       "     'bbox': [0.8119, 0.5442, 0.0757, 0.1162]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0233,\n",
-       "     'bbox': [0.5811, 0.5073, 0.155, 0.07749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0233,\n",
-       "     'bbox': [0.365, 0.4538, 0.03663, 0.04981]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.102,\n",
-       "     'bbox': [0.7838, 0.5424, 0.09157, 0.07749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.129,\n",
-       "     'bbox': [0.6068, 0.5885, 0.04639, 0.08671]},\n",
-       "    {'category': '1', 'conf': 0.245, 'bbox': [0.9365, 0.5664, 0.05616, 0.19]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.284,\n",
-       "     'bbox': [0.7826, 0.5424, 0.09768, 0.1752]},\n",
-       "    {'category': '1', 'conf': 0.449, 'bbox': [0.3699, 0.4649, 0.1562, 0.1512]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.586,\n",
-       "     'bbox': [0.5286, 0.4594, 0.1013, 0.09963]},\n",
-       "    {'category': '1', 'conf': 0.605, 'bbox': [0.5811, 0.5092, 0.155, 0.1678]},\n",
-       "    {'category': '1', 'conf': 0.76, 'bbox': [0.2661, 0.4612, 0.05128, 0.1033]},\n",
-       "    {'category': '1', 'conf': 0.784, 'bbox': [0.8119, 0.5756, 0.1379, 0.1549]},\n",
-       "    {'category': '1', 'conf': 0.919, 'bbox': [0.6288, 0.5461, 0.2063, 0.1826]},\n",
-       "    {'category': '1', 'conf': 0.922, 'bbox': [0.5054, 0.5571, 0.1159, 0.118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.922,\n",
-       "     'bbox': [0.2222, 0.4944, 0.2832, 0.2195]}]},\n",
-       "  {'file': '0000/0670.jpg',\n",
-       "   'max_detection_conf': 0.895,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.895,\n",
-       "     'bbox': [0.3369, 0.4483, 0.1672, 0.142]}]},\n",
-       "  {'file': '0000/0671.jpg',\n",
-       "   'max_detection_conf': 0.164,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0226,\n",
-       "     'bbox': [0, 0.2656, 0.1257, 0.2767]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.164,\n",
-       "     'bbox': [0.4615, 0.4317, 0.1062, 0.08856]}]},\n",
-       "  {'file': '0000/0694.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.004884, 0.09225, 0.9462, 0.8302]}]},\n",
-       "  {'file': '0000/0695.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.1684, 0.5977, 0.2087, 0.1974]}]},\n",
-       "  {'file': '0000/0696.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.1257, 0.5774, 0.271, 0.2287]}]},\n",
-       "  {'file': '0000/0697.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.05494, 0.6051, 0.3211, 0.2472]}]},\n",
-       "  {'file': '0000/0698.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0165,\n",
-       "     'bbox': [0.01343, 0.03136, 0.3565, 0.7158]},\n",
-       "    {'category': '1', 'conf': 0.0337, 'bbox': [0.1672, 0.5996, 0.1269, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.01343, 0.03136, 0.8046, 0.9372]}]},\n",
-       "  {'file': '0000/0699.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.2918, 0.5701, 0.09279, 0.1623]}]},\n",
-       "  {'file': '0000/0700.jpg',\n",
-       "   'max_detection_conf': 0.49,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0103,\n",
-       "     'bbox': [0.8327, 0.4594, 0.07814, 0.09409]},\n",
-       "    {'category': '1', 'conf': 0.1, 'bbox': [0.8327, 0.4372, 0.1282, 0.1273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.49,\n",
-       "     'bbox': [0.8327, 0.4206, 0.1672, 0.2214]}]},\n",
-       "  {'file': '0000/0701.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.01098, 0.6402, 0.2905, 0.2306]}]},\n",
-       "  {'file': '0000/0702.jpg',\n",
-       "   'max_detection_conf': 0.896,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.896,\n",
-       "     'bbox': [0.6141, 0.4575, 0.3858, 0.5147]}]},\n",
-       "  {'file': '0000/0703.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.01587, 0.4778, 0.6007, 0.4944]}]},\n",
-       "  {'file': '0000/0704.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0147,\n",
-       "     'bbox': [0.8681, 0.6014, 0.1208, 0.08487]},\n",
-       "    {'category': '1', 'conf': 0.0163, 'bbox': [0.442, 0.5682, 0.1611, 0.214]},\n",
-       "    {'category': '1', 'conf': 0.835, 'bbox': [0.5128, 0.5682, 0.1098, 0.1845]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.4273, 0.5479, 0.1001, 0.2656]}]},\n",
-       "  {'file': '0000/0705.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.5103, 0.4649, 0.2124, 0.297]}]},\n",
-       "  {'file': '0000/0706.jpg',\n",
-       "   'max_detection_conf': 0.778,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0222,\n",
-       "     'bbox': [0.2442, 0.6328, 0.2466, 0.1734]},\n",
-       "    {'category': '1', 'conf': 0.486, 'bbox': [0.1404, 0.6346, 0.4725, 0.3357]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.778,\n",
-       "     'bbox': [0.5079, 0.5258, 0.1709, 0.1955]}]},\n",
-       "  {'file': '0000/0707.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0272,\n",
-       "     'bbox': [0.6202, 0.535, 0.09768, 0.1439]},\n",
-       "    {'category': '1', 'conf': 0.896, 'bbox': [0.3418, 0.5276, 0.1697, 0.2546]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.4835, 0.511, 0.2344, 0.2269]}]},\n",
-       "  {'file': '0000/0708.jpg',\n",
-       "   'max_detection_conf': 0.865,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0133,\n",
-       "     'bbox': [0.4774, 0.5701, 0.08669, 0.1402]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.018,\n",
-       "     'bbox': [0.3589, 0.5405, 0.09645, 0.08856]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.865,\n",
-       "     'bbox': [0.4774, 0.5442, 0.1623, 0.1678]}]},\n",
-       "  {'file': '0000/0709.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.2006, 0.519, 0.53, 0.4081]}]},\n",
-       "  {'file': '0000/0710.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.2442, 0.5313, 0.2222, 0.1678]}]},\n",
-       "  {'file': '0000/0711.jpg',\n",
-       "   'max_detection_conf': 0.929,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.929,\n",
-       "     'bbox': [0.4981, 0.4704, 0.2576, 0.4132]}]},\n",
-       "  {'file': '0000/0712.jpg',\n",
-       "   'max_detection_conf': 0.928,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.928,\n",
-       "     'bbox': [0.5078, 0.5393, 0.1611, 0.1205]}]},\n",
-       "  {'file': '0000/0713.jpg',\n",
-       "   'max_detection_conf': 0.898,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.886,\n",
-       "     'bbox': [0.06715, 0.6217, 0.1978, 0.1955]},\n",
-       "    {'category': '1', 'conf': 0.898, 'bbox': [0.63, 0.5166, 0.1477, 0.1863]}]},\n",
-       "  {'file': '0000/0522.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0278,\n",
-       "     'bbox': [0.8998, 0.5129, 0.05128, 0.06642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.1733, 0.4151, 0.199, 0.3118]}]},\n",
-       "  {'file': '0000/0523.jpg',\n",
-       "   'max_detection_conf': 0.913,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0209,\n",
-       "     'bbox': [0.2515, 0.5885, 0.1062, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0301,\n",
-       "     'bbox': [0.8901, 0.3394, 0.02442, 0.04612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.913,\n",
-       "     'bbox': [0.6312, 0.4464, 0.1245, 0.1439]}]},\n",
-       "  {'file': '0000/0524.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0146,\n",
-       "     'bbox': [0.6862, 0.5369, 0.03907, 0.05166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0232,\n",
-       "     'bbox': [0.5787, 0.535, 0.03296, 0.04243]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.178,\n",
-       "     'bbox': [0.7032, 0.5996, 0.1965, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.3956, 0.4778, 0.08424, 0.1162]}]},\n",
-       "  {'file': '0000/0525.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.4664, 0.356, 0.2173, 0.19]}]},\n",
-       "  {'file': '0000/0526.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.01221, 0.2896, 0.8632, 0.6808]}]},\n",
-       "  {'file': '0000/0527.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.3626, 0.4594, 0.1221, 0.118]}]},\n",
-       "  {'file': '0000/0528.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.1294, 0.3247, 0.5238, 0.4797]}]},\n",
-       "  {'file': '0000/0529.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.4639, 0.476, 0.1746, 0.2546]}]},\n",
-       "  {'file': '0000/0530.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.004884, 0.02952, 0.6617, 0.9354]}]},\n",
-       "  {'file': '0000/0531.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.2881, 0.3265, 0.2063, 0.2472]}]},\n",
-       "  {'file': '0000/0532.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.3443, 0.3247, 0.1391, 0.2324]}]},\n",
-       "  {'file': '0000/0533.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.4175, 0.238, 0.1562, 0.2509]}]},\n",
-       "  {'file': '0000/0534.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0477,\n",
-       "     'bbox': [0.7423, 0.2915, 0.01465, 0.02398]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.2515, 0.1402, 0.409, 0.4833]}]},\n",
-       "  {'file': '0000/0535.jpg',\n",
-       "   'max_detection_conf': 0.929,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.115,\n",
-       "     'bbox': [0.01221, 0.5811, 0.2063, 0.3542]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.929,\n",
-       "     'bbox': [0.293, 0.3966, 0.1428, 0.1752]}]},\n",
-       "  {'file': '0000/0536.jpg',\n",
-       "   'max_detection_conf': 0.748,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0131,\n",
-       "     'bbox': [0.4603, 0.1845, 0.0293, 0.02767]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0139,\n",
-       "     'bbox': [0.8998, 0.3118, 0.04884, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0177,\n",
-       "     'bbox': [0.3418, 0.5811, 0.04761, 0.0369]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.748,\n",
-       "     'bbox': [0.3211, 0.4409, 0.04761, 0.0738]}]},\n",
-       "  {'file': '0000/0537.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.4151, 0.4428, 0.1575, 0.19]}]},\n",
-       "  {'file': '0000/0538.jpg',\n",
-       "   'max_detection_conf': 0.876,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.876,\n",
-       "     'bbox': [0.3699, 0.5147, 0.0757, 0.2195]}]},\n",
-       "  {'file': '0000/0539.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.3797, 0.428, 0.2527, 0.2195]}]},\n",
-       "  {'file': '0000/0540.jpg',\n",
-       "   'max_detection_conf': 0.0262,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0129,\n",
-       "     'bbox': [0.398, 0.5571, 0.03052, 0.06273]},\n",
-       "    {'category': '1', 'conf': 0.0241, 'bbox': [0.2258, 0.4428, 0.2039, 0.214]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0262,\n",
-       "     'bbox': [0.3699, 0.4815, 0.0586, 0.1402]}]},\n",
-       "  {'file': '0000/0860.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.4273, 0.5848, 0.2466, 0.3856]}]},\n",
-       "  {'file': '0000/0861.jpg',\n",
-       "   'max_detection_conf': 0.921,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.161,\n",
-       "     'bbox': [0.3394, 0.4778, 0.02319, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.921,\n",
-       "     'bbox': [0.2905, 0.5055, 0.1562, 0.1789]}]},\n",
-       "  {'file': '0000/0862.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.2698, 0.5239, 0.3748, 0.3044]}]},\n",
-       "  {'file': '0000/0863.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.2942, 0.5479, 0.2442, 0.3062]}]},\n",
-       "  {'file': '0000/0864.jpg',\n",
-       "   'max_detection_conf': 0.887,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.887,\n",
-       "     'bbox': [0.0757, 0.6051, 0.2551, 0.107]}]},\n",
-       "  {'file': '0000/0865.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.2783, 0.535, 0.3992, 0.4354]}]},\n",
-       "  {'file': '0000/0866.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.1233, 0.404, 0.7887, 0.5664]}]},\n",
-       "  {'file': '0000/0867.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0148,\n",
-       "     'bbox': [0.6251, 0.5516, 0.04151, 0.03505]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0157,\n",
-       "     'bbox': [0.1978, 0.5461, 0.05616, 0.04797]},\n",
-       "    {'category': '1', 'conf': 0.793, 'bbox': [0.2564, 0.4852, 0.1013, 0.1051]},\n",
-       "    {'category': '1', 'conf': 0.951, 'bbox': [0.3211, 0.5848, 0.1941, 0.2509]},\n",
-       "    {'category': '1', 'conf': 0.956, 'bbox': [0, 0.4612, 0.3382, 0.476]}]},\n",
-       "  {'file': '0000/0868.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.793,\n",
-       "     'bbox': [0, 0.725, 0.1367, 0.2472]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.293, 0.4704, 0.2356, 0.2767]}]},\n",
-       "  {'file': '0000/0869.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.803,\n",
-       "     'bbox': [0.1233, 0.4797, 0.1282, 0.1014]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.2271, 0.5461, 0.547, 0.4243]}]},\n",
-       "  {'file': '0000/0870.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.4957, 0.5332, 0.1489, 0.1494]}]},\n",
-       "  {'file': '0000/0871.jpg',\n",
-       "   'max_detection_conf': 0.895,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0516,\n",
-       "     'bbox': [0.07936, 0.5571, 0.0989, 0.06457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.895,\n",
-       "     'bbox': [0.2759, 0.6291, 0.07814, 0.07564]}]},\n",
-       "  {'file': '0000/0872.jpg',\n",
-       "   'max_detection_conf': 0.889,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.889,\n",
-       "     'bbox': [0.5921, 0.6125, 0.1343, 0.1051]}]},\n",
-       "  {'file': '0000/0873.jpg',\n",
-       "   'max_detection_conf': 0.883,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.244,\n",
-       "     'bbox': [0.5555, 0.5479, 0.03418, 0.04428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.883,\n",
-       "     'bbox': [0.4188, 0.583, 0.1172, 0.1199]}]},\n",
-       "  {'file': '0000/0874.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0148,\n",
-       "     'bbox': [0.07448, 0.5498, 0.08913, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.059,\n",
-       "     'bbox': [0.05006, 0.4815, 0.05372, 0.07011]},\n",
-       "    {'category': '1', 'conf': 0.865, 'bbox': [0.4871, 0.4889, 0.1025, 0.107]},\n",
-       "    {'category': '1', 'conf': 0.94, 'bbox': [0.4078, 0.535, 0.1636, 0.1752]}]},\n",
-       "  {'file': '0000/0875.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0376,\n",
-       "     'bbox': [0.07936, 0.5498, 0.1025, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.087,\n",
-       "     'bbox': [0.8803, 0.3837, 0.04151, 0.09225]},\n",
-       "    {'category': '1', 'conf': 0.333, 'bbox': [0.1892, 0.476, 0.06471, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.505,\n",
-       "     'bbox': [0.6324, 0.5221, 0.04395, 0.05904]},\n",
-       "    {'category': '1', 'conf': 0.842, 'bbox': [0.6825, 0.5276, 0.1416, 0.1512]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.3846, 0.5442, 0.1379, 0.2435]}]},\n",
-       "  {'file': '0000/0876.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0228,\n",
-       "     'bbox': [0.7728, 0.5701, 0.1123, 0.1291]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0337,\n",
-       "     'bbox': [0.6336, 0.487, 0.03296, 0.04059]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0471,\n",
-       "     'bbox': [0.6324, 0.5239, 0.04273, 0.05904]},\n",
-       "    {'category': '1', 'conf': 0.215, 'bbox': [0.8827, 0.38, 0.03785, 0.09963]},\n",
-       "    {'category': '1', 'conf': 0.951, 'bbox': [0.42, 0.5184, 0.1636, 0.1734]}]},\n",
-       "  {'file': '0000/0877.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0564,\n",
-       "     'bbox': [0.6166, 0.5239, 0.04639, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3064, 0.559, 0.2234, 0.2084]}]},\n",
-       "  {'file': '0000/0878.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0129,\n",
-       "     'bbox': [0.9255, 0.8394, 0.07326, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.108,\n",
-       "     'bbox': [0.6434, 0.5387, 0.04517, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.3418, 0.559, 0.2881, 0.2158]}]},\n",
-       "  {'file': '0000/1603.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.514, 0.5461, 0.2612, 0.2785]}]},\n",
-       "  {'file': '0000/1604.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.2649, 0.2361, 0.4102, 0.5756]}]},\n",
-       "  {'file': '0000/1605.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2185, 0.4225, 0.3284, 0.225]}]},\n",
-       "  {'file': '0000/1606.jpg',\n",
-       "   'max_detection_conf': 0.908,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0278,\n",
-       "     'bbox': [0.6459, 0.5442, 0.07448, 0.07933]},\n",
-       "    {'category': '1', 'conf': 0.889, 'bbox': [0.3797, 0.4963, 0.2002, 0.1678]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.7423, 0.4704, 0.07326, 0.2047]}]},\n",
-       "  {'file': '0000/1607.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3858, 0.511, 0.1562, 0.142]}]},\n",
-       "  {'file': '0000/1608.jpg',\n",
-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.3858, 0.4575, 0.177, 0.1789]}]},\n",
-       "  {'file': '0000/1609.jpg',\n",
-       "   'max_detection_conf': 0.737,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.737,\n",
-       "     'bbox': [0.4029, 0.5793, 0.06471, 0.08118]}]},\n",
-       "  {'file': '0000/1610.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.67,\n",
-       "     'bbox': [0.01953, 0.5184, 0.1037, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.894,\n",
-       "     'bbox': [0.2564, 0.5166, 0.1025, 0.08856]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3614, 0.5092, 0.1855, 0.1199]}]},\n",
-       "  {'file': '0000/1611.jpg',\n",
-       "   'max_detection_conf': 0.914,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0104,\n",
-       "     'bbox': [0.5409, 0.5424, 0.0818, 0.09409]},\n",
-       "    {'category': '1', 'conf': 0.914, 'bbox': [0.3492, 0.511, 0.1807, 0.118]}]},\n",
-       "  {'file': '0000/1612.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.06837, 0.4225, 0.4603, 0.4077]}]},\n",
-       "  {'file': '0000/1613.jpg',\n",
-       "   'max_detection_conf': 0.939,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0128,\n",
-       "     'bbox': [0.1953, 0.5793, 0.008547, 0.01476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0175,\n",
-       "     'bbox': [0.2454, 0.5922, 0.008547, 0.01107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0953,\n",
-       "     'bbox': [0.293, 0.5664, 0.05616, 0.0369]},\n",
-       "    {'category': '1', 'conf': 0.9, 'bbox': [0.3565, 0.4907, 0.1697, 0.1365]},\n",
-       "    {'category': '1', 'conf': 0.91, 'bbox': [0.3455, 0.6088, 0.0818, 0.1365]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.939,\n",
-       "     'bbox': [0.1758, 0.6512, 0.09157, 0.2306]}]},\n",
-       "  {'file': '0000/1614.jpg',\n",
-       "   'max_detection_conf': 0.922,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0124,\n",
-       "     'bbox': [0.5677, 0.6605, 0.07692, 0.04059]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.886,\n",
-       "     'bbox': [0.5775, 0.5608, 0.08913, 0.07933]},\n",
-       "    {'category': '1', 'conf': 0.89, 'bbox': [0.5506, 0.6088, 0.08913, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.922,\n",
-       "     'bbox': [0.4285, 0.463, 0.1587, 0.1642]}]},\n",
-       "  {'file': '0000/1615.jpg',\n",
-       "   'max_detection_conf': 0.918,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.918,\n",
-       "     'bbox': [0.4029, 0.5036, 0.1306, 0.1162]}]},\n",
-       "  {'file': '0000/1616.jpg',\n",
-       "   'max_detection_conf': 0.92,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.92,\n",
-       "     'bbox': [0.07345, 0.8353, 0.1097, 0.1384]}]},\n",
-       "  {'file': '0000/1617.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0126,\n",
-       "     'bbox': [0.945, 0.4981, 0.05494, 0.1771]},\n",
-       "    {'category': '1', 'conf': 0.213, 'bbox': [0.945, 0.4649, 0.05494, 0.1273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.2478, 0.3505, 0.08424, 0.08487]}]},\n",
-       "  {'file': '0000/1618.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0499,\n",
-       "     'bbox': [0.9145, 0.5442, 0.05982, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.316,\n",
-       "     'bbox': [0.9108, 0.4557, 0.06471, 0.1494]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.2771, 0.3487, 0.07692, 0.1014]}]},\n",
-       "  {'file': '0000/1619.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.2759, 0.2712, 0.06959, 0.09409]}]},\n",
-       "  {'file': '0000/1620.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.1221, 0.369, 0.09523, 0.09594]}]},\n",
-       "  {'file': '0000/1621.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0202,\n",
-       "     'bbox': [0.7924, 0.3856, 0.1562, 0.1291]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.06959, 0.3284, 0.1025, 0.2546]}]},\n",
-       "  {'file': '0000/1622.jpg',\n",
-       "   'max_detection_conf': 0.927,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.919,\n",
-       "     'bbox': [0.06105, 0.3523, 0.05616, 0.1125]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.757, 0.5664, 0.0757, 0.1568]}]},\n",
-       "  {'file': '0000/0910.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.3675, 0.4261, 0.2222, 0.273]}]},\n",
-       "  {'file': '0000/0911.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.3858, 0.6273, 0.1477, 0.1678]}]},\n",
-       "  {'file': '0000/0912.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.4358, 0.3745, 0.2649, 0.2564]}]},\n",
-       "  {'file': '0000/0913.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.26, 0.3542, 0.2466, 0.2804]}]},\n",
-       "  {'file': '0000/0914.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.06349, 0.3339, 0.4603, 0.5092]}]},\n",
-       "  {'file': '0000/0915.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0397,\n",
-       "     'bbox': [0.5787, 0.4483, 0.0354, 0.04428]},\n",
-       "    {'category': '1', 'conf': 0.831, 'bbox': [0.42, 0.4114, 0.04761, 0.08118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.481, 0.4317, 0.07692, 0.2232]}]},\n",
-       "  {'file': '0000/0916.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4139, 0.3782, 0.2295, 0.2896]}]},\n",
-       "  {'file': '0000/0917.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.643,\n",
-       "     'bbox': [0.1587, 0.6157, 0.04818, 0.07517]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.4375, 0.5906, 0.0932, 0.2171]}]},\n",
-       "  {'file': '0000/0918.jpg',\n",
-       "   'max_detection_conf': 0.868,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0115,\n",
-       "     'bbox': [0.02685, 0.4546, 0.1026, 0.06801]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.794,\n",
-       "     'bbox': [0.02764, 0.5835, 0.05055, 0.1217]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.868,\n",
-       "     'bbox': [0.4312, 0.5811, 0.0774, 0.1742]}]},\n",
-       "  {'file': '0000/0919.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0134,\n",
-       "     'bbox': [0.3492, 0.3874, 0.03785, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0231,\n",
-       "     'bbox': [0.6471, 0.3874, 0.03663, 0.06088]},\n",
-       "    {'category': '1', 'conf': 0.0517, 'bbox': [0.1184, 0.356, 0.0818, 0.1143]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.176,\n",
-       "     'bbox': [0.5799, 0.4464, 0.03663, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.712,\n",
-       "     'bbox': [0.3943, 0.3782, 0.06837, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.797,\n",
-       "     'bbox': [0.4749, 0.4151, 0.05616, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.221, 0.4852, 0.3992, 0.3763]}]},\n",
-       "  {'file': '0000/0920.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.847,\n",
-       "     'bbox': [0.4761, 0.3837, 0.05128, 0.1033]},\n",
-       "    {'category': '1', 'conf': 0.9, 'bbox': [0, 0.3468, 0.1587, 0.1826]},\n",
-       "    {'category': '1', 'conf': 0.948, 'bbox': [0.5286, 0.3542, 0.1233, 0.1365]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.3748, 0.4243, 0.1916, 0.2306]}]},\n",
-       "  {'file': '0000/0921.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.023,\n",
-       "     'bbox': [0, 0.8376, 0.07936, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.031,\n",
-       "     'bbox': [0.4884, 0.3616, 0.08547, 0.06088]},\n",
-       "    {'category': '1', 'conf': 0.382, 'bbox': [0, 0.714, 0.09645, 0.2583]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.773,\n",
-       "     'bbox': [0.2454, 0.369, 0.09279, 0.06273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.851,\n",
-       "     'bbox': [0.4859, 0.3634, 0.08913, 0.1346]},\n",
-       "    {'category': '1', 'conf': 0.885, 'bbox': [0, 0.404, 0.1709, 0.369]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.06349, 0.4225, 0.4664, 0.5498]}]},\n",
-       "  {'file': '0000/0922.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0121,\n",
-       "     'bbox': [0.3113, 0.3782, 0.03296, 0.05904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0153,\n",
-       "     'bbox': [0.3601, 0.3911, 0.07326, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0186,\n",
-       "     'bbox': [0.5115, 0.3911, 0.07692, 0.1808]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0441,\n",
-       "     'bbox': [0.5885, 0.3819, 0.03907, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0451,\n",
-       "     'bbox': [0.03663, 0.3708, 0.0757, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0458,\n",
-       "     'bbox': [0.4017, 0.3966, 0.03296, 0.06273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0708,\n",
-       "     'bbox': [0.2783, 0.3763, 0.05006, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.27,\n",
-       "     'bbox': [0.001221, 0.3671, 0.1098, 0.1494]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.292,\n",
-       "     'bbox': [0.1721, 0.3782, 0.06959, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.333,\n",
-       "     'bbox': [0.2466, 0.3763, 0.06227, 0.08118]},\n",
-       "    {'category': '1', 'conf': 0.689, 'bbox': [0.4444, 0.38, 0.08547, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.703,\n",
-       "     'bbox': [0.5225, 0.3966, 0.06715, 0.08118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.781,\n",
-       "     'bbox': [0.4896, 0.4594, 0.07203, 0.1568]},\n",
-       "    {'category': '1', 'conf': 0.899, 'bbox': [0.1953, 0.4612, 0.1086, 0.166]},\n",
-       "    {'category': '1', 'conf': 0.908, 'bbox': [0.3821, 0.4538, 0.1111, 0.3487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.5579, 0.4225, 0.2454, 0.3653]}]},\n",
-       "  {'file': '0000/0923.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0372,\n",
-       "     'bbox': [0.02564, 0.3505, 0.1147, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.768,\n",
-       "     'bbox': [0.6727, 0.3948, 0.09523, 0.1531]},\n",
-       "    {'category': '1', 'conf': 0.906, 'bbox': [0.6178, 0.5479, 0.3614, 0.4261]},\n",
-       "    {'category': '1', 'conf': 0.921, 'bbox': [0.4676, 0.3708, 0.1672, 0.2177]},\n",
-       "    {'category': '1', 'conf': 0.946, 'bbox': [0.221, 0.369, 0.1221, 0.3044]}]},\n",
-       "  {'file': '0000/0924.jpg',\n",
-       "   'max_detection_conf': 0.934,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.01,\n",
-       "     'bbox': [0.7326, 0.4981, 0.2442, 0.476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0152,\n",
-       "     'bbox': [0.2905, 0.3892, 0.0818, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.024,\n",
-       "     'bbox': [0.5152, 0.404, 0.03785, 0.06642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0475,\n",
-       "     'bbox': [0.2002, 0.3911, 0.1269, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0684,\n",
-       "     'bbox': [0.3931, 0.3911, 0.1159, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0731,\n",
-       "     'bbox': [0.2881, 0.3929, 0.04029, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.103,\n",
-       "     'bbox': [0.2612, 0.4188, 0.06471, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.213,\n",
-       "     'bbox': [0.5177, 0.4022, 0.07692, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.291,\n",
-       "     'bbox': [0.4444, 0.4298, 0.06715, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.614,\n",
-       "     'bbox': [0.3943, 0.3911, 0.05372, 0.07564]},\n",
-       "    {'category': '1', 'conf': 0.839, 'bbox': [0.4273, 0.4907, 0.547, 0.4815]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.934,\n",
-       "     'bbox': [0.1343, 0.4612, 0.4126, 0.3597]}]},\n",
-       "  {'file': '0000/0925.jpg',\n",
-       "   'max_detection_conf': 0.916,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.916,\n",
-       "     'bbox': [0.5604, 0.4907, 0.09645, 0.166]}]},\n",
-       "  {'file': '0000/0926.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.4603, 0.3782, 0.2197, 0.2896]}]},\n",
-       "  {'file': '0000/0927.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.3504, 0.38, 0.2991, 0.2583]}]},\n",
-       "  {'file': '0000/0928.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.2393, 0.5996, 0.1587, 0.1217]}]},\n",
-       "  {'file': '0000/0929.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.431, 0.5018, 0.08547, 0.2121]}]},\n",
-       "  {'file': '0000/0930.jpg',\n",
-       "   'max_detection_conf': 0.904,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.904,\n",
-       "     'bbox': [0.4371, 0.5092, 0.06837, 0.2066]}]},\n",
-       "  {'file': '0000/1831.jpg',\n",
-       "   'max_detection_conf': 0.884,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.884,\n",
-       "     'bbox': [0.4542, 0.5073, 0.06837, 0.0738]}]},\n",
-       "  {'file': '0000/1832.jpg',\n",
-       "   'max_detection_conf': 0.9,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.9,\n",
-       "     'bbox': [0.5006, 0.5147, 0.05616, 0.07195]}]},\n",
-       "  {'file': '0000/1833.jpg',\n",
-       "   'max_detection_conf': 0.879,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.879,\n",
-       "     'bbox': [0.5665, 0.5387, 0.05738, 0.07011]}]},\n",
-       "  {'file': '0000/1834.jpg',\n",
-       "   'max_detection_conf': 0.908,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.5653, 0.5332, 0.06105, 0.07749]}]},\n",
-       "  {'file': '0000/1835.jpg',\n",
-       "   'max_detection_conf': 0.854,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.854,\n",
-       "     'bbox': [0.575, 0.5092, 0.04395, 0.0904]}]},\n",
-       "  {'file': '0000/1836.jpg',\n",
-       "   'max_detection_conf': 0.835,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0246,\n",
-       "     'bbox': [0, 0.04428, 0.1135, 0.9243]},\n",
-       "    {'category': '1', 'conf': 0.119, 'bbox': [0.7008, 0.4963, 0.0525, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.835,\n",
-       "     'bbox': [0.5372, 0.5055, 0.06471, 0.07933]}]},\n",
-       "  {'file': '0000/1837.jpg',\n",
-       "   'max_detection_conf': 0.901,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.901,\n",
-       "     'bbox': [0.3455, 0.4538, 0.2698, 0.1678]}]},\n",
-       "  {'file': '0000/1838.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2637, 0.463, 0.4969, 0.2988]}]},\n",
-       "  {'file': '0000/1839.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0, 0.2767, 0.514, 0.5184]}]},\n",
-       "  {'file': '0000/1840.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.06471, 0.1162, 0.9108, 0.8542]}]},\n",
-       "  {'file': '0000/1841.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0102,\n",
-       "     'bbox': [0, 0.0369, 0.01709, 0.1402]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.1416, 0.4261, 0.4261, 0.4686]}]},\n",
-       "  {'file': '0000/1842.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.01098, 0.607, 0.4835, 0.3634]}]},\n",
-       "  {'file': '0000/1843.jpg',\n",
-       "   'max_detection_conf': 0.84,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.84,\n",
-       "     'bbox': [0.01465, 0.03321, 0.9645, 0.9372]}]},\n",
-       "  {'file': '0000/1844.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.3412, 0.6217, 0.1161, 0.1861]}]},\n",
-       "  {'file': '0000/1845.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0113,\n",
-       "     'bbox': [0.7216, 0.8468, 0.02686, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.5567, 0.6881, 0.2271, 0.2841]}]},\n",
-       "  {'file': '0000/1846.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2967, 0.6014, 0.221, 0.2675]}]},\n",
-       "  {'file': '0000/1847.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.05494, 0.4815, 0.6654, 0.487]}]},\n",
-       "  {'file': '0000/1848.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.6056, 0.3948, 0.3931, 0.4464]}]},\n",
-       "  {'file': '0000/1849.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.3882, 0.4852, 0.282, 0.3284]}]},\n",
-       "  {'file': '0000/1377.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.2857, 0.4428, 0.3919, 0.3523]}]},\n",
-       "  {'file': '0000/1378.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.5982, 0.4022, 0.3858, 0.3929]}]},\n",
-       "  {'file': '0000/1379.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.2844, 0.3874, 0.3894, 0.4741]}]},\n",
-       "  {'file': '0000/1380.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.3101, 0.6402, 0.5152, 0.3321]}]},\n",
-       "  {'file': '0000/1381.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.1831, 0.4833, 0.3382, 0.1937]}]},\n",
-       "  {'file': '0000/1382.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.4346, 0.5036, 0.4749, 0.4667]}]},\n",
-       "  {'file': '0000/1383.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.3785, 0.5313, 0.1733, 0.2232]}]},\n",
-       "  {'file': '0000/1384.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.4078, 0.5295, 0.07081, 0.2121]}]},\n",
-       "  {'file': '0000/1385.jpg',\n",
-       "   'max_detection_conf': 0.923,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.923,\n",
-       "     'bbox': [0.3601, 0.6107, 0.1025, 0.1236]}]},\n",
-       "  {'file': '0000/1386.jpg',\n",
-       "   'max_detection_conf': 0.851,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.851,\n",
-       "     'bbox': [0.3553, 0.463, 0.5909, 0.5092]}]},\n",
-       "  {'file': '0000/1387.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0192,\n",
-       "     'bbox': [0.1746, 0.6033, 0.08302, 0.118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0217,\n",
-       "     'bbox': [0.8461, 0.5258, 0.1233, 0.1494]},\n",
-       "    {'category': '1', 'conf': 0.0263, 'bbox': [0, 0.5867, 0.04761, 0.1549]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0935,\n",
-       "     'bbox': [0.007326, 0.5848, 0.07692, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.698,\n",
-       "     'bbox': [0.07814, 0.5811, 0.1758, 0.1457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.3308, 0.559, 0.09401, 0.2121]}]},\n",
-       "  {'file': '0000/1388.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.2478, 0.5516, 0.4517, 0.3505]}]},\n",
-       "  {'file': '0000/1389.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.1855, 0.511, 0.608, 0.4612]}]},\n",
-       "  {'file': '0000/1390.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.2307, 0.5258, 0.3724, 0.3745]}]},\n",
-       "  {'file': '0000/1391.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.1965, 0.5442, 0.3724, 0.3542]}]},\n",
-       "  {'file': '0000/1392.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.177, 0.3357, 0.6703, 0.5774]}]},\n",
-       "  {'file': '0000/1393.jpg',\n",
-       "   'max_detection_conf': 0.947,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.947,\n",
-       "     'bbox': [0.4517, 0.5313, 0.2564, 0.356]}]},\n",
-       "  {'file': '0000/1394.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.3846, 0.5424, 0.1379, 0.2638]}]},\n",
-       "  {'file': '0000/1395.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0136,\n",
-       "     'bbox': [0.6752, 0.487, 0.06471, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0139,\n",
-       "     'bbox': [0.4627, 0.5369, 0.0586, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0157,\n",
-       "     'bbox': [0.768, 0.5313, 0.0354, 0.04612]},\n",
-       "    {'category': '1', 'conf': 0.0309, 'bbox': [0, 0.5461, 0.03052, 0.3191]},\n",
-       "    {'category': '1', 'conf': 0.0374, 'bbox': [0.7753, 0.5239, 0.1269, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0423,\n",
-       "     'bbox': [0.6764, 0.4944, 0.1172, 0.2453]},\n",
-       "    {'category': '1', 'conf': 0.044, 'bbox': [0.6764, 0.487, 0.1159, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.103,\n",
-       "     'bbox': [0.7765, 0.5258, 0.05494, 0.04797]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.122,\n",
-       "     'bbox': [0.7851, 0.5221, 0.1147, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.685,\n",
-       "     'bbox': [0.9059, 0.5276, 0.09401, 0.3302]},\n",
-       "    {'category': '1', 'conf': 0.863, 'bbox': [0.7838, 0.5682, 0.1391, 0.2638]},\n",
-       "    {'category': '1', 'conf': 0.89, 'bbox': [0.5225, 0.6549, 0.06471, 0.2583]},\n",
-       "    {'category': '1', 'conf': 0.971, 'bbox': [0.4761, 0.5313, 0.2625, 0.3247]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.07448, 0.5073, 0.3137, 0.463]}]},\n",
-       "  {'file': '0000/1396.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.01831, 0.1476, 0.7496, 0.7804]}]},\n",
-       "  {'file': '0000/0172.jpg',\n",
-       "   'max_detection_conf': 0.831,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0102,\n",
-       "     'bbox': [0.5579, 0.2601, 0.06959, 0.04428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0107,\n",
-       "     'bbox': [0.1477, 0.6642, 0.0586, 0.06273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0561,\n",
-       "     'bbox': [0.3833, 0.5461, 0.04395, 0.04981]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.831,\n",
-       "     'bbox': [0.3833, 0.5405, 0.09157, 0.05535]}]},\n",
-       "  {'file': '0000/0173.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0109,\n",
-       "     'bbox': [0.2515, 0.4391, 0.08058, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0116,\n",
-       "     'bbox': [0.1831, 0.4317, 0.1135, 0.1512]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.012,\n",
-       "     'bbox': [0.6275, 0.4741, 0.06593, 0.2343]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0123,\n",
-       "     'bbox': [0.6678, 0.5055, 0.05616, 0.1199]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.031,\n",
-       "     'bbox': [0.6935, 0.6678, 0.04761, 0.08487]},\n",
-       "    {'category': '1', 'conf': 0.059, 'bbox': [0.6752, 0.4907, 0.1636, 0.2121]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0626,\n",
-       "     'bbox': [0.6935, 0.6512, 0.09157, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0717,\n",
-       "     'bbox': [0.6446, 0.4778, 0.04395, 0.1383]},\n",
-       "    {'category': '1', 'conf': 0.1, 'bbox': [0.2539, 0.4372, 0.1452, 0.1328]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.128,\n",
-       "     'bbox': [0.2417, 0.4354, 0.05494, 0.09409]},\n",
-       "    {'category': '1', 'conf': 0.47, 'bbox': [0.3601, 0.4501, 0.04151, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.483,\n",
-       "     'bbox': [0.6446, 0.476, 0.04273, 0.05904]},\n",
-       "    {'category': '1', 'conf': 0.575, 'bbox': [0.6764, 0.4926, 0.1062, 0.1383]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.854,\n",
-       "     'bbox': [0.6007, 0.5276, 0.08058, 0.1955]},\n",
-       "    {'category': '1', 'conf': 0.866, 'bbox': [0.7484, 0.5055, 0.1355, 0.2416]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.4285, 0.5608, 0.1343, 0.3487]}]},\n",
-       "  {'file': '0000/0174.jpg',\n",
-       "   'max_detection_conf': 0.778,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0101,\n",
-       "     'bbox': [0.8351, 0.5258, 0.04761, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0104,\n",
-       "     'bbox': [0.7142, 0.5073, 0.09768, 0.1402]},\n",
-       "    {'category': '1', 'conf': 0.011, 'bbox': [0.1233, 0.4612, 0.1111, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0143,\n",
-       "     'bbox': [0.3711, 0.4981, 0.08669, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0157,\n",
-       "     'bbox': [0.5628, 0.4649, 0.07448, 0.07933]},\n",
-       "    {'category': '1', 'conf': 0.019, 'bbox': [0.2722, 0.5, 0.05372, 0.1291]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0208,\n",
-       "     'bbox': [0.7326, 0.511, 0.0757, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.021,\n",
-       "     'bbox': [0.7545, 0.5018, 0.07081, 0.1125]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0246,\n",
-       "     'bbox': [0.1965, 0.4538, 0.09157, 0.1014]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0478,\n",
-       "     'bbox': [0.1904, 0.463, 0.04639, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0611,\n",
-       "     'bbox': [0.7692, 0.5147, 0.04273, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0704,\n",
-       "     'bbox': [0.2747, 0.4575, 0.08302, 0.1678]},\n",
-       "    {'category': '1', 'conf': 0.079, 'bbox': [0.597, 0.463, 0.04273, 0.06826]},\n",
-       "    {'category': '1', 'conf': 0.221, 'bbox': [0.1978, 0.4575, 0.1538, 0.1383]},\n",
-       "    {'category': '1', 'conf': 0.424, 'bbox': [0.7704, 0.5129, 0.1098, 0.1125]},\n",
-       "    {'category': '1', 'conf': 0.771, 'bbox': [0.4407, 0.4649, 0.07081, 0.142]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.778,\n",
-       "     'bbox': [0.5641, 0.4649, 0.07203, 0.1974]}]},\n",
-       "  {'file': '0000/0175.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.5213, 0.5295, 0.2967, 0.2988]}]},\n",
-       "  {'file': '0000/0176.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0644,\n",
-       "     'bbox': [0.7423, 0.5147, 0.05372, 0.09778]},\n",
-       "    {'category': '1', 'conf': 0.734, 'bbox': [0.5152, 0.4926, 0.1599, 0.1586]},\n",
-       "    {'category': '1', 'conf': 0.782, 'bbox': [0.5567, 0.6697, 0.1709, 0.2195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.1562, 0.5202, 0.4798, 0.4298]}]},\n",
-       "  {'file': '0000/0177.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0376,\n",
-       "     'bbox': [0.3675, 0.463, 0.07081, 0.1014]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.4358, 0.4741, 0.1452, 0.1346]}]},\n",
-       "  {'file': '0000/0178.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0115,\n",
-       "     'bbox': [0.7936, 0.5258, 0.03418, 0.06088]},\n",
-       "    {'category': '1', 'conf': 0.0535, 'bbox': [0.7936, 0.5036, 0.105, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.12,\n",
-       "     'bbox': [0.7948, 0.5092, 0.06227, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.4444, 0.4649, 0.1416, 0.1439]}]},\n",
-       "  {'file': '0000/0179.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.4688, 0.476, 0.1269, 0.1346]}]},\n",
-       "  {'file': '0000/0180.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.4688, 0.4686, 0.1208, 0.2306]}]},\n",
-       "  {'file': '0000/0181.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.25,\n",
-       "     'bbox': [0.7264, 0.4114, 0.08058, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.2332, 0.4852, 0.3333, 0.2952]}]},\n",
-       "  {'file': '0000/0182.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0533,\n",
-       "     'bbox': [0.2857, 0.3413, 0.04273, 0.04981]},\n",
-       "    {'category': '1', 'conf': 0.916, 'bbox': [0.2637, 0.476, 0.1098, 0.19]},\n",
-       "    {'category': '1', 'conf': 0.94, 'bbox': [0.6678, 0.559, 0.09768, 0.238]},\n",
-       "    {'category': '1', 'conf': 0.94, 'bbox': [0.42, 0.4464, 0.08547, 0.2398]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.4505, 0.5221, 0.1465, 0.2121]}]},\n",
-       "  {'file': '0000/0183.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.886,\n",
-       "     'bbox': [0.04517, 0.487, 0.1013, 0.2214]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.917,\n",
-       "     'bbox': [0.4529, 0.5442, 0.06715, 0.2601]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.492, 0.4926, 0.1037, 0.2066]}]},\n",
-       "  {'file': '0000/0184.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.4652, 0.4501, 0.07203, 0.1918]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.2661, 0.4778, 0.1355, 0.2306]}]},\n",
-       "  {'file': '0000/0185.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.3931, 0.5092, 0.2661, 0.4612]}]},\n",
-       "  {'file': '0000/0186.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4652, 0.214, 0.5177, 0.6697]}]},\n",
-       "  {'file': '0000/0187.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4871, 0.4649, 0.1721, 0.5073]}]},\n",
-       "  {'file': '0000/0188.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.5152, 0.4428, 0.1001, 0.3431]}]},\n",
-       "  {'file': '0000/0189.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.5152, 0.4926, 0.1416, 0.4538]}]},\n",
-       "  {'file': '0000/0190.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.5689, 0.4907, 0.2405, 0.4797]}]},\n",
-       "  {'file': '0000/1486.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.008547, 0.0904, 0.6605, 0.6143]}]},\n",
-       "  {'file': '0000/1487.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.4383, 0.5166, 0.2161, 0.4538]}]},\n",
-       "  {'file': '0000/1488.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.2258, 0.3745, 0.4529, 0.5073]}]},\n",
-       "  {'file': '0000/1489.jpg',\n",
-       "   'max_detection_conf': 0.896,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0106,\n",
-       "     'bbox': [0.5054, 0.5258, 0.1233, 0.2287]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0137,\n",
-       "     'bbox': [0.3992, 0.5202, 0.2759, 0.3837]},\n",
-       "    {'category': '1', 'conf': 0.0149, 'bbox': [0.4102, 0.4852, 0.1978, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0169,\n",
-       "     'bbox': [0.3235, 0.5535, 0.09645, 0.1107]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.3296, 0.5369, 0.2466, 0.2435]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.3296, 0.5276, 0.1831, 0.1143]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.5555, 0.4852, 0.0525, 0.04428]},\n",
-       "    {'category': '1', 'conf': 0.678, 'bbox': [0.3247, 0.4833, 0.2832, 0.1752]},\n",
-       "    {'category': '1', 'conf': 0.891, 'bbox': [0.2319, 0.3302, 0.5396, 0.6402]},\n",
-       "    {'category': '1', 'conf': 0.894, 'bbox': [0.6739, 0.4815, 0.1733, 0.3228]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.896,\n",
-       "     'bbox': [0.2527, 0.5018, 0.1672, 0.1881]}]},\n",
-       "  {'file': '0000/1490.jpg',\n",
-       "   'max_detection_conf': 0.948,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.939,\n",
-       "     'bbox': [0.1697, 0.5405, 0.3101, 0.369]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.948,\n",
-       "     'bbox': [0.4407, 0.5202, 0.5567, 0.452]}]},\n",
-       "  {'file': '0000/1491.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.766,\n",
-       "     'bbox': [0.8681, 0.3929, 0.1318, 0.4409]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.3821, 0.4557, 0.2112, 0.2841]}]},\n",
-       "  {'file': '0000/1492.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.3186, 0.4778, 0.2649, 0.4132]}]},\n",
-       "  {'file': '0000/1493.jpg',\n",
-       "   'max_detection_conf': 0.874,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0.5824, 0.6402, 0.2063, 0.1088]}]},\n",
-       "  {'file': '0000/1494.jpg',\n",
-       "   'max_detection_conf': 0.931,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.014,\n",
-       "     'bbox': [0.3467, 0.5147, 0.07814, 0.1734]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.667,\n",
-       "     'bbox': [0.3858, 0.5276, 0.04029, 0.1586]},\n",
-       "    {'category': '1', 'conf': 0.841, 'bbox': [0.2429, 0.5239, 0.1037, 0.297]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.4212, 0.5129, 0.07936, 0.2601]}]},\n",
-       "  {'file': '0000/1495.jpg',\n",
-       "   'max_detection_conf': 0.981,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.981,\n",
-       "     'bbox': [0.1709, 0.1125, 0.8083, 0.8597]}]},\n",
-       "  {'file': '0000/1496.jpg',\n",
-       "   'max_detection_conf': 0.982,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.982,\n",
-       "     'bbox': [0.02564, 0.238, 0.5262, 0.7343]}]},\n",
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-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.1697, 0.583, 0.3626, 0.38]}]},\n",
-       "  {'file': '0000/1498.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.4041, 0.5885, 0.21, 0.1808]}]},\n",
-       "  {'file': '0000/1499.jpg',\n",
-       "   'max_detection_conf': 0.932,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.932,\n",
-       "     'bbox': [0.4749, 0.6309, 0.06349, 0.2121]}]},\n",
-       "  {'file': '0000/1500.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.6068, 0.6291, 0.3663, 0.3431]}]},\n",
-       "  {'file': '0000/1501.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3443, 0.583, 0.2124, 0.1642]}]},\n",
-       "  {'file': '0000/1502.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.07448, 0.1291, 0.315, 0.6346]}]},\n",
-       "  {'file': '0000/1503.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.7069, 0.3376, 0.2148, 0.3136]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.503, 0.3597, 0.2796, 0.2546]}]},\n",
-       "  {'file': '0000/1504.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0252,\n",
-       "     'bbox': [0.6666, 0.4704, 0.07448, 0.2564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.1831, 0.1365, 0.6141, 0.8339]}]},\n",
-       "  {'file': '0000/1460.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.1672, 0.5498, 0.3516, 0.2749]}]},\n",
-       "  {'file': '0000/1461.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0344,\n",
-       "     'bbox': [0, 0.1457, 0.1245, 0.4797]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0367,\n",
-       "     'bbox': [0.06837, 0.5571, 0.1391, 0.214]},\n",
-       "    {'category': '1', 'conf': 0.0567, 'bbox': [0, 0.3173, 0.1257, 0.19]},\n",
-       "    {'category': '1', 'conf': 0.196, 'bbox': [0, 0.2712, 0.1245, 0.6937]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.5006, 0.4833, 0.3235, 0.2952]}]},\n",
-       "  {'file': '0000/1462.jpg',\n",
-       "   'max_detection_conf': 0.92,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0105,\n",
-       "     'bbox': [0, 0.3302, 0.1282, 0.2988]},\n",
-       "    {'category': '1', 'conf': 0.92, 'bbox': [0.2503, 0.3874, 0.586, 0.4409]}]},\n",
-       "  {'file': '0000/1463.jpg',\n",
-       "   'max_detection_conf': 0.878,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.878,\n",
-       "     'bbox': [0.01587, 0.6531, 0.8278, 0.3191]}]},\n",
-       "  {'file': '0000/1464.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0, 0.5442, 0.3394, 0.38]}]},\n",
-       "  {'file': '0000/1465.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.2759, 0.5239, 0.3907, 0.3044]}]},\n",
-       "  {'file': '0000/1466.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0135,\n",
-       "     'bbox': [0.2063, 0.5756, 0.04273, 0.1125]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.442, 0.6014, 0.0757, 0.2011]}]},\n",
-       "  {'file': '0000/1467.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.4468, 0.4372, 0.3113, 0.3819]}]},\n",
-       "  {'file': '0000/1468.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.3162, 0.452, 0.3333, 0.3892]}]},\n",
-       "  {'file': '0000/1469.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.6031, 0.6402, 0.26, 0.2859]}]},\n",
-       "  {'file': '0000/1470.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3137, 0.2988, 0.1318, 0.3708]}]},\n",
-       "  {'file': '0000/1471.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.09157, 0.09963, 0.7203, 0.6088]}]},\n",
-       "  {'file': '0000/1472.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4041, 0.2933, 0.575, 0.5977]}]},\n",
-       "  {'file': '0000/1473.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.6227, 0.3505, 0.3369, 0.3523]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.3015, 0.345, 0.3308, 0.3394]}]},\n",
-       "  {'file': '0000/1474.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0168,\n",
-       "     'bbox': [0.05128, 0.594, 0.1868, 0.1309]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0325,\n",
-       "     'bbox': [0.1733, 0.6199, 0.07936, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0361,\n",
-       "     'bbox': [0.4505, 0.3191, 0.4212, 0.4206]},\n",
-       "    {'category': '1', 'conf': 0.84, 'bbox': [0.6764, 0.3173, 0.1965, 0.4022]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.009768, 0.2269, 0.7399, 0.7324]}]},\n",
-       "  {'file': '0000/1475.jpg',\n",
-       "   'max_detection_conf': 0.677,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.102,\n",
-       "     'bbox': [0.7997, 0.5996, 0.07203, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.677,\n",
-       "     'bbox': [0.757, 0.5369, 0.1147, 0.1531]}]},\n",
-       "  {'file': '0000/1476.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0, 0.2988, 0.2844, 0.3745]}]},\n",
-       "  {'file': '0000/1477.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3479, 0.4833, 0.2148, 0.356]}]},\n",
-       "  {'file': '0000/1478.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.1391, 0.5018, 0.2429, 0.1752]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.8021, 0.3856, 0.1721, 0.2915]}]},\n",
-       "  {'file': '0000/1479.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.2783, 0.3025, 0.3907, 0.4206]}]},\n",
-       "  {'file': '0000/1480.jpg',\n",
-       "   'max_detection_conf': 0.552,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0585,\n",
-       "     'bbox': [0.009768, 0.5018, 0.3162, 0.2915]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.552,\n",
-       "     'bbox': [0.3455, 0.02398, 0.6275, 0.9428]}]},\n",
-       "  {'file': '0000/1481.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.07326, 0.1808, 0.7081, 0.7915]}]},\n",
-       "  {'file': '0000/1482.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.2637, 0.249, 0.575, 0.5682]}]},\n",
-       "  {'file': '0000/1483.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.594,\n",
-       "     'bbox': [0.1037, 0.4188, 0.04029, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3919, 0.356, 0.1428, 0.2878]}]},\n",
-       "  {'file': '0000/1484.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
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-       "  {'file': '0000/1851.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.2409, 0.2505, 0.353, 0.4331]}]},\n",
-       "  {'file': '0000/1852.jpg',\n",
-       "   'max_detection_conf': 0.927,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.4114, 0.5055, 0.09523, 0.1236]}]},\n",
-       "  {'file': '0000/1853.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.003663, 0.03136, 0.6263, 0.9391]}]},\n",
-       "  {'file': '0000/1854.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.3577, 0.4206, 0.1819, 0.273]}]},\n",
-       "  {'file': '0000/1855.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.741,\n",
-       "     'bbox': [0.8632, 0.142, 0.09279, 0.1365]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.4322, 0.428, 0.2161, 0.1826]}]},\n",
-       "  {'file': '0000/1856.jpg',\n",
-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.3003, 0.4317, 0.1868, 0.1402]}]},\n",
-       "  {'file': '0000/1857.jpg',\n",
-       "   'max_detection_conf': 0.873,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.873,\n",
-       "     'bbox': [0.3321, 0.4317, 0.1355, 0.0904]}]},\n",
-       "  {'file': '0000/1858.jpg',\n",
-       "   'max_detection_conf': 0.849,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.849,\n",
-       "     'bbox': [0.3992, 0.4667, 0.144, 0.09778]}]},\n",
-       "  {'file': '0000/1859.jpg',\n",
-       "   'max_detection_conf': 0.888,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.888,\n",
-       "     'bbox': [0.4029, 0.4612, 0.1196, 0.1236]}]},\n",
-       "  {'file': '0000/1860.jpg',\n",
-       "   'max_detection_conf': 0.905,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.905,\n",
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-       "  {'file': '0000/1861.jpg',\n",
-       "   'max_detection_conf': 0.982,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.982,\n",
-       "     'bbox': [0.01831, 0.3653, 0.6605, 0.6051]}]},\n",
-       "  {'file': '0000/1862.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.008547, 0.4151, 0.8815, 0.5553]}]},\n",
-       "  {'file': '0000/1863.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.459, 0.4354, 0.1794, 0.1845]}]},\n",
-       "  {'file': '0000/1864.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.2698, 0.4852, 0.2026, 0.1937]}]},\n",
-       "  {'file': '0000/1865.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.2637, 0.4926, 0.2014, 0.1845]}]},\n",
-       "  {'file': '0000/1866.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.4713, 0.5166, 0.4688, 0.4557]}]},\n",
-       "  {'file': '0000/1867.jpg',\n",
-       "   'max_detection_conf': 0.741,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0195,\n",
-       "     'bbox': [0.3247, 0.5055, 0.0525, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.164,\n",
-       "     'bbox': [0.923, 0.1623, 0.03052, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.321,\n",
-       "     'bbox': [0.2967, 0.5036, 0.08302, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.34,\n",
-       "     'bbox': [0.3785, 0.5092, 0.03174, 0.04428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.741,\n",
-       "     'bbox': [0.3711, 0.5608, 0.06105, 0.07195]}]},\n",
-       "  {'file': '0000/1868.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.01709, 0.1125, 0.9633, 0.8579]}]},\n",
-       "  {'file': '0000/1869.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.008547, 0.249, 0.9377, 0.7195]}]},\n",
-       "  {'file': '0000/1870.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.1526, 0.5018, 0.2503, 0.1808]}]},\n",
-       "  {'file': '0000/0566.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0156,\n",
-       "     'bbox': [0.7326, 0.5885, 0.08058, 0.2287]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0264,\n",
-       "     'bbox': [0.8974, 0.5848, 0.08913, 0.1568]},\n",
-       "    {'category': '1', 'conf': 0.0339, 'bbox': [0.8437, 0.5719, 0.155, 0.2749]},\n",
-       "    {'category': '1', 'conf': 0.637, 'bbox': [0.7362, 0.4114, 0.238, 0.4594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.879,\n",
-       "     'bbox': [0.01221, 0.4483, 0.1648, 0.4354]},\n",
-       "    {'category': '1', 'conf': 0.952, 'bbox': [0.2173, 0.3579, 0.1953, 0.5332]},\n",
-       "    {'category': '1', 'conf': 0.96, 'bbox': [0.4078, 0.452, 0.2051, 0.4501]}]},\n",
-       "  {'file': '0000/0567.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.4334, 0.5166, 0.1343, 0.1752]}]},\n",
-       "  {'file': '0000/0568.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0121,\n",
-       "     'bbox': [0.9225, 0.4236, 0.07582, 0.1026]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4265, 0.4677, 0.1113, 0.2159]}]},\n",
-       "  {'file': '0000/0569.jpg',\n",
-       "   'max_detection_conf': 0.911,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.911,\n",
-       "     'bbox': [0.3894, 0.5774, 0.09279, 0.08856]}]},\n",
-       "  {'file': '0000/0570.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.3797, 0.4317, 0.1733, 0.2693]}]},\n",
-       "  {'file': '0000/0571.jpg',\n",
-       "   'max_detection_conf': 0.914,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.914,\n",
-       "     'bbox': [0.3882, 0.5608, 0.1233, 0.1217]}]},\n",
-       "  {'file': '0000/0572.jpg',\n",
-       "   'max_detection_conf': 0.905,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.905,\n",
-       "     'bbox': [0.4224, 0.5424, 0.08424, 0.1199]}]},\n",
-       "  {'file': '0000/0573.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.3479, 0.5424, 0.2051, 0.1678]}]},\n",
-       "  {'file': '0000/0574.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0185,\n",
-       "     'bbox': [0.3272, 0.4446, 0.02319, 0.06642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.4151, 0.5793, 0.144, 0.1162]}]},\n",
-       "  {'file': '0000/0575.jpg',\n",
-       "   'max_detection_conf': 0.884,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.884,\n",
-       "     'bbox': [0.4126, 0.5719, 0.08058, 0.09225]}]},\n",
-       "  {'file': '0000/0576.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.4065, 0.4077, 0.2197, 0.2029]}]},\n",
-       "  {'file': '0000/0577.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.4273, 0.4095, 0.2271, 0.2214]}]},\n",
-       "  {'file': '0000/0578.jpg',\n",
-       "   'max_detection_conf': 0.773,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.773,\n",
-       "     'bbox': [0.4395, 0.5774, 0.08302, 0.06273]}]},\n",
-       "  {'file': '0000/0579.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.3418, 0.4335, 0.1648, 0.1937]}]},\n",
-       "  {'file': '0000/0580.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.5262, 0.4667, 0.1733, 0.1992]}]},\n",
-       "  {'file': '0000/0581.jpg',\n",
-       "   'max_detection_conf': 0.812,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.812,\n",
-       "     'bbox': [0.4395, 0.5645, 0.08913, 0.1236]}]},\n",
-       "  {'file': '0000/0582.jpg',\n",
-       "   'max_detection_conf': 0.802,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.802,\n",
-       "     'bbox': [0.4395, 0.4944, 0.2246, 0.1937]}]},\n",
-       "  {'file': '0000/0583.jpg',\n",
-       "   'max_detection_conf': 0.878,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.878,\n",
-       "     'bbox': [0.4383, 0.5073, 0.1037, 0.1088]}]},\n",
-       "  {'file': '0000/0584.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.4078, 0.5461, 0.1514, 0.1236]}]},\n",
-       "  {'file': '0000/0585.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.4188, 0.6014, 0.1282, 0.1605]}]},\n",
-       "  {'file': '0000/0586.jpg',\n",
-       "   'max_detection_conf': 0.82,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.82,\n",
-       "     'bbox': [0.492, 0.5461, 0.06715, 0.1291]}]},\n",
-       "  {'file': '0000/0587.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.4065, 0.4926, 0.199, 0.1789]}]},\n",
-       "  {'file': '0000/0588.jpg',\n",
-       "   'max_detection_conf': 0.866,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.866,\n",
-       "     'bbox': [0.4261, 0.535, 0.06349, 0.1143]}]},\n",
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-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.4358, 0.5811, 0.1355, 0.1236]}]},\n",
-       "  {'file': '0000/0590.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.2295, 0.487, 0.326, 0.2011]}]},\n",
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-       "   'max_detection_conf': 0.931,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.3785, 0.5719, 0.1367, 0.1051]}]},\n",
-       "  {'file': '0000/0592.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.4004, 0.4612, 0.1575, 0.1439]}]},\n",
-       "  {'file': '0000/0593.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3516, 0.583, 0.354, 0.3874]}]},\n",
-       "  {'file': '0000/0594.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.4334, 0.4428, 0.1452, 0.1955]}]},\n",
-       "  {'file': '0000/0595.jpg',\n",
-       "   'max_detection_conf': 0.923,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0108,\n",
-       "     'bbox': [0.5921, 0.511, 0.09645, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0113,\n",
-       "     'bbox': [0.2503, 0.6808, 0.1074, 0.06826]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4261, 0.618, 0.05494, 0.0369]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0187,\n",
-       "     'bbox': [0.4126, 0.5645, 0.2124, 0.1549]},\n",
-       "    {'category': '1',\n",
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-       "    {'category': '1',\n",
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-       "     'bbox': [0.009768, 0.725, 0.2075, 0.2472]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0391,\n",
-       "     'bbox': [0.3443, 0.6199, 0.1636, 0.1236]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0599,\n",
-       "     'bbox': [0.3431, 0.6236, 0.1758, 0.3321]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4285, 0.5959, 0.09645, 0.06088]},\n",
-       "    {'category': '1', 'conf': 0.0676, 'bbox': [0.3479, 0.5774, 0.2295, 0.19]},\n",
-       "    {'category': '1',\n",
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-       "    {'category': '1', 'conf': 0.257, 'bbox': [0.3479, 0.5682, 0.3479, 0.3985]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.5177, 0.5645, 0.07448, 0.08302]},\n",
-       "    {'category': '1', 'conf': 0.4, 'bbox': [0.3455, 0.6365, 0.07814, 0.1107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.765,\n",
-       "     'bbox': [0.01221, 0.6808, 0.4432, 0.2915]},\n",
-       "    {'category': '1', 'conf': 0.825, 'bbox': [0.5934, 0.4575, 0.2014, 0.2029]},\n",
-       "    {'category': '1', 'conf': 0.854, 'bbox': [0.4859, 0.6365, 0.2576, 0.3339]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.923,\n",
-       "     'bbox': [0.6678, 0.4077, 0.3284, 0.5627]}]},\n",
-       "  {'file': '0000/0596.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0105,\n",
-       "     'bbox': [0.5299, 0.3523, 0.08302, 0.118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0145,\n",
-       "     'bbox': [0.4078, 0.3708, 0.05372, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0177,\n",
-       "     'bbox': [0.459, 0.3413, 0.05738, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0232,\n",
-       "     'bbox': [0.4078, 0.3708, 0.06715, 0.142]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.6312, 0.2343, 0.06959, 0.1346]},\n",
-       "    {'category': '1', 'conf': 0.061, 'bbox': [0.3992, 0.476, 0.0586, 0.1033]},\n",
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-       "    {'category': '1', 'conf': 0.375, 'bbox': [0.4603, 0.345, 0.08302, 0.1346]},\n",
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-       "    {'category': '1', 'conf': 0.612, 'bbox': [0.2539, 0.3837, 0.2124, 0.1125]},\n",
-       "    {'category': '1', 'conf': 0.705, 'bbox': [0.8315, 0.1439, 0.1147, 0.1826]},\n",
-       "    {'category': '1', 'conf': 0.926, 'bbox': [0.1709, 0.4409, 0.2783, 0.2749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.6947, 0.1937, 0.1672, 0.3523]}]},\n",
-       "  {'file': '0000/0597.jpg',\n",
-       "   'max_detection_conf': 0.889,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.889,\n",
-       "     'bbox': [0.4261, 0.5036, 0.1098, 0.09963]}]},\n",
-       "  {'file': '0000/0598.jpg',\n",
-       "   'max_detection_conf': 0.817,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.817,\n",
-       "     'bbox': [0.4896, 0.4944, 0.1172, 0.09225]}]},\n",
-       "  {'file': '0000/0599.jpg',\n",
-       "   'max_detection_conf': 0.874,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0.3956, 0.5129, 0.1159, 0.09225]}]},\n",
-       "  {'file': '0000/0600.jpg',\n",
-       "   'max_detection_conf': 0.794,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.794,\n",
-       "     'bbox': [0.47, 0.487, 0.07936, 0.07564]}]},\n",
-       "  {'file': '0000/0414.jpg',\n",
-       "   'max_detection_conf': 0.927,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.927,\n",
-       "     'bbox': [0.4212, 0.4501, 0.1562, 0.166]}]},\n",
-       "  {'file': '0000/0415.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.1514, 0.3321, 0.293, 0.273]}]},\n",
-       "  {'file': '0000/0416.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.2893, 0.2103, 0.3333, 0.4944]}]},\n",
-       "  {'file': '0000/0417.jpg',\n",
-       "   'max_detection_conf': 0.915,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.915,\n",
-       "     'bbox': [0.4358, 0.2453, 0.2991, 0.4575]}]},\n",
-       "  {'file': '0000/0418.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.579,\n",
-       "     'bbox': [0.2796, 0.3431, 0.04151, 0.09963]},\n",
-       "    {'category': '1', 'conf': 0.893, 'bbox': [0, 0.5885, 0.1599, 0.3653]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3516, 0.5719, 0.2307, 0.3985]}]},\n",
-       "  {'file': '0000/0419.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3394, 0.5719, 0.3028, 0.3966]}]},\n",
-       "  {'file': '0000/0420.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.2405, 0.6107, 0.2478, 0.3597]}]},\n",
-       "  {'file': '0000/0421.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.414,\n",
-       "     'bbox': [0.7973, 0.3616, 0.1404, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.538,\n",
-       "     'bbox': [0.8815, 0.5239, 0.08058, 0.1199]},\n",
-       "    {'category': '1', 'conf': 0.692, 'bbox': [0.7582, 0.4261, 0.1758, 0.2546]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.4981, 0.3653, 0.3846, 0.3782]}]},\n",
-       "  {'file': '0000/0422.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.007326, 0.5073, 0.9682, 0.463]}]},\n",
-       "  {'file': '0000/0423.jpg',\n",
-       "   'max_detection_conf': 0.903,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0448,\n",
-       "     'bbox': [0.2393, 0.4483, 0.5616, 0.5166]},\n",
-       "    {'category': '1', 'conf': 0.832, 'bbox': [0.5579, 0.4483, 0.249, 0.2878]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.903,\n",
-       "     'bbox': [0.3797, 0.6328, 0.5897, 0.3376]}]},\n",
-       "  {'file': '0000/0424.jpg',\n",
-       "   'max_detection_conf': 0.896,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.896,\n",
-       "     'bbox': [0.1709, 0.5073, 0.2063, 0.4428]}]},\n",
-       "  {'file': '0000/0425.jpg',\n",
-       "   'max_detection_conf': 0.933,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.933,\n",
-       "     'bbox': [0.4334, 0.5369, 0.1941, 0.1143]}]},\n",
-       "  {'file': '0000/0426.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.5115, 0.5018, 0.2332, 0.3653]}]},\n",
-       "  {'file': '0000/0427.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.2515, 0.3634, 0.3125, 0.2952]}]},\n",
-       "  {'file': '0000/0428.jpg',\n",
-       "   'max_detection_conf': 0.908,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.823,\n",
-       "     'bbox': [0.2356, 0.4501, 0.2429, 0.4169]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.01343, 0.4391, 0.1684, 0.2047]}]},\n",
-       "  {'file': '0000/0429.jpg',\n",
-       "   'max_detection_conf': 0.948,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.948,\n",
-       "     'bbox': [0.3113, 0.4704, 0.2039, 0.3431]}]},\n",
-       "  {'file': '0000/0430.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.2466, 0.321, 0.2637, 0.2712]}]},\n",
-       "  {'file': '0000/0431.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.3553, 0.3265, 0.3894, 0.3247]}]},\n",
-       "  {'file': '0000/0432.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4992, 0.4176, 0.2827, 0.3054]}]},\n",
-       "  {'file': '0000/0433.jpg',\n",
-       "   'max_detection_conf': 0.891,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.891,\n",
-       "     'bbox': [0.6729, 0.3842, 0.08609, 0.1455]}]},\n",
-       "  {'file': '0000/0434.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.01184, 0.3854, 0.5616, 0.5859]}]},\n",
-       "  {'file': '0000/0435.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.372, 0.3961, 0.2361, 0.2875]}]},\n",
-       "  {'file': '0000/0436.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.2172, 0.6778, 0.4044, 0.2935]}]},\n",
-       "  {'file': '0000/0437.jpg',\n",
-       "   'max_detection_conf': 0.522,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.013,\n",
-       "     'bbox': [0.06227, 0.7767, 0.0818, 0.08671]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.522,\n",
-       "     'bbox': [0.4188, 0.5424, 0.05128, 0.05166]}]},\n",
-       "  {'file': '0000/1083.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.3455, 0.4243, 0.1965, 0.214]}]},\n",
-       "  {'file': '0000/1084.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4505, 0.3985, 0.3357, 0.3929]}]},\n",
-       "  {'file': '0000/1085.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.802,\n",
-       "     'bbox': [0.6141, 0.3763, 0.1098, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.3223, 0.4409, 0.1721, 0.2011]}]},\n",
-       "  {'file': '0000/1086.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3797, 0.4206, 0.1892, 0.3228]}]},\n",
-       "  {'file': '0000/1087.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.4676, 0.345, 0.238, 0.2398]}]},\n",
-       "  {'file': '0000/1088.jpg',\n",
-       "   'max_detection_conf': 0.924,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0107,\n",
-       "     'bbox': [0.6788, 0.3339, 0.1098, 0.09594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0313,\n",
-       "     'bbox': [0.6446, 0.3394, 0.04639, 0.02767]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0396,\n",
-       "     'bbox': [0.7374, 0.3339, 0.1123, 0.1254]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0629,\n",
-       "     'bbox': [0.5103, 0.3413, 0.1098, 0.1697]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.108,\n",
-       "     'bbox': [0.7374, 0.3339, 0.05128, 0.0535]},\n",
-       "    {'category': '1', 'conf': 0.35, 'bbox': [0.7362, 0.3339, 0.05372, 0.1199]},\n",
-       "    {'category': '1', 'conf': 0.74, 'bbox': [0.5091, 0.345, 0.1098, 0.07564]},\n",
-       "    {'category': '1', 'conf': 0.815, 'bbox': [0.6446, 0.3394, 0.1147, 0.1273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.924,\n",
-       "     'bbox': [0.5079, 0.3634, 0.1831, 0.1918]}]},\n",
-       "  {'file': '0000/1089.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0374,\n",
-       "     'bbox': [0.5531, 0.345, 0.03663, 0.04612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0534,\n",
-       "     'bbox': [0.6227, 0.3302, 0.06349, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0878,\n",
-       "     'bbox': [0.5567, 0.3431, 0.05372, 0.06273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0889,\n",
-       "     'bbox': [0.6214, 0.3247, 0.03663, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.259,\n",
-       "     'bbox': [0.5592, 0.3302, 0.09401, 0.0904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.4029, 0.356, 0.09157, 0.2416]}]},\n",
-       "  {'file': '0000/1090.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.5238, 0.3542, 0.2625, 0.3394]}]},\n",
-       "  {'file': '0000/1091.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.1367, 0.3726, 0.3724, 0.4981]}]},\n",
-       "  {'file': '0000/1092.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.4175, 0.3487, 0.1135, 0.2343]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.2527, 0.3708, 0.1245, 0.3007]}]},\n",
-       "  {'file': '0000/1093.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0323,\n",
-       "     'bbox': [0.3797, 0.3597, 0.04639, 0.04797]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0425,\n",
-       "     'bbox': [0.4151, 0.3653, 0.02808, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.106,\n",
-       "     'bbox': [0.735, 0.3634, 0.04761, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.164,\n",
-       "     'bbox': [0.3858, 0.3634, 0.05738, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.557,\n",
-       "     'bbox': [0.0757, 0.3874, 0.07814, 0.1494]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.683,\n",
-       "     'bbox': [0.3479, 0.3579, 0.08302, 0.1125]},\n",
-       "    {'category': '1', 'conf': 0.891, 'bbox': [0.6105, 0.3302, 0.1355, 0.1476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.923,\n",
-       "     'bbox': [0.008547, 0.5092, 0.1233, 0.4612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.4371, 0.345, 0.1746, 0.2195]}]},\n",
-       "  {'file': '0000/1094.jpg',\n",
-       "   'max_detection_conf': 0.623,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0156,\n",
-       "     'bbox': [0.7614, 0.4379, 0.09004, 0.1181]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0165,\n",
-       "     'bbox': [0.8878, 0.3031, 0.1003, 0.1575]},\n",
-       "    {'category': '1', 'conf': 0.158, 'bbox': [0.6145, 0.3424, 0.199, 0.1121]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.385,\n",
-       "     'bbox': [0.744, 0.3687, 0.06951, 0.07637]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.623,\n",
-       "     'bbox': [0.1729, 0.07279, 0.8191, 0.8878]}]},\n",
-       "  {'file': '0000/1095.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0, 0.5774, 0.4676, 0.3948]}]},\n",
-       "  {'file': '0000/1096.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.4725, 0.3302, 0.249, 0.3099]}]},\n",
-       "  {'file': '0000/1097.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4358, 0.3911, 0.1892, 0.2029]}]},\n",
-       "  {'file': '0000/1098.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2503, 0.3357, 0.7252, 0.6346]}]},\n",
-       "  {'file': '0000/1099.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4676, 0.3856, 0.133, 0.2785]}]},\n",
-       "  {'file': '0000/1100.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.459, 0.3357, 0.5128, 0.6365]}]},\n",
-       "  {'file': '0000/1101.jpg',\n",
-       "   'max_detection_conf': 0.925,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.925,\n",
-       "     'bbox': [0.08791, 0.3616, 0.8962, 0.6088]}]},\n",
-       "  {'file': '0000/1102.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.4993, 0.476, 0.3272, 0.2361]}]},\n",
-       "  {'file': '0000/1103.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0201,\n",
-       "     'bbox': [0.08791, 0.4594, 0.04884, 0.04981]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.398, 0.5276, 0.07692, 0.1863]}]},\n",
-       "  {'file': '0000/1104.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.4517, 0.3653, 0.525, 0.607]}]},\n",
-       "  {'file': '0000/1105.jpg',\n",
-       "   'max_detection_conf': 0.981,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.981,\n",
-       "     'bbox': [0.2136, 0.4003, 0.2625, 0.3118]}]},\n",
-       "  {'file': '0000/1106.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.2649, 0.404, 0.3418, 0.2988]}]},\n",
-       "  {'file': '0000/1107.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.481, 0.3874, 0.2466, 0.2693]}]},\n",
-       "  {'file': '0000/1108.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4078, 0.3911, 0.21, 0.225]}]},\n",
-       "  {'file': '0000/1109.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.1196, 0.2638, 0.851, 0.7066]}]},\n",
-       "  {'file': '0000/1110.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4969, 0.369, 0.3467, 0.6033]}]},\n",
-       "  {'file': '0000/1111.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3235, 0.5369, 0.5335, 0.4354]}]},\n",
-       "  {'file': '0000/1112.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.514, 0.3782, 0.1868, 0.2472]}]},\n",
-       "  {'file': '0000/1113.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.3907, 0.2767, 0.2893, 0.4077]}]},\n",
-       "  {'file': '0000/1114.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.009768, 0.5811, 0.315, 0.1568]}]},\n",
-       "  {'file': '0000/1115.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.1416, 0.4003, 0.3943, 0.3726]}]},\n",
-       "  {'file': '0000/0299.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.442, 0.428, 0.2551, 0.2084]}]},\n",
-       "  {'file': '0000/0300.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.4517, 0.4649, 0.2893, 0.356]}]},\n",
-       "  {'file': '0000/0301.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.1623, 0.6051, 0.3968, 0.3653]}]},\n",
-       "  {'file': '0000/0302.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.014,\n",
-       "     'bbox': [0, 0.7472, 0.2014, 0.2269]},\n",
-       "    {'category': '1', 'conf': 0.136, 'bbox': [0, 0.452, 0.1965, 0.5166]},\n",
-       "    {'category': '1', 'conf': 0.339, 'bbox': [0, 0.4538, 0.1062, 0.2933]},\n",
-       "    {'category': '1', 'conf': 0.925, 'bbox': [0.4847, 0.3616, 0.0989, 0.2214]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2271, 0.4907, 0.3199, 0.4095]}]},\n",
-       "  {'file': '0000/0303.jpg',\n",
-       "   'max_detection_conf': 0.834,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.834,\n",
-       "     'bbox': [0.5164, 0.4649, 0.105, 0.1143]}]},\n",
-       "  {'file': '0000/0304.jpg',\n",
-       "   'max_detection_conf': 0.885,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.885,\n",
-       "     'bbox': [0.4273, 0.5479, 0.1257, 0.1125]}]},\n",
-       "  {'file': '0000/0305.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.1514, 0.6291, 0.3235, 0.3007]}]},\n",
-       "  {'file': '0000/0306.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.949,\n",
-       "     'bbox': [0.5384, 0.5608, 0.06349, 0.1881]}]},\n",
-       "  {'file': '0000/0307.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.728,\n",
-       "     'bbox': [0.575, 0.5295, 0.06471, 0.09409]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.1318, 0.6273, 0.2991, 0.2767]}]},\n",
-       "  {'file': '0000/0308.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0134,\n",
-       "     'bbox': [0, 0.607, 0.03907, 0.1937]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0272,\n",
-       "     'bbox': [0.1709, 0.5627, 0.04151, 0.06642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0687,\n",
-       "     'bbox': [0.01221, 0.594, 0.08302, 0.1328]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.358,\n",
-       "     'bbox': [0.4139, 0.5424, 0.04151, 0.02767]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.853,\n",
-       "     'bbox': [0.01465, 0.5885, 0.1904, 0.2343]},\n",
-       "    {'category': '1', 'conf': 0.935, 'bbox': [0.1904, 0.5811, 0.1159, 0.2084]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.3516, 0.5442, 0.1941, 0.1974]}]},\n",
-       "  {'file': '0000/0309.jpg',\n",
-       "   'max_detection_conf': 0.906,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0122,\n",
-       "     'bbox': [0.06349, 0.5461, 0.1147, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.906,\n",
-       "     'bbox': [0.06227, 0.5424, 0.2686, 0.2416]}]},\n",
-       "  {'file': '0000/0310.jpg',\n",
-       "   'max_detection_conf': 0.918,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.918,\n",
-       "     'bbox': [0.4554, 0.4501, 0.1343, 0.2619]}]},\n",
-       "  {'file': '0000/0311.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0133,\n",
-       "     'bbox': [0.177, 0.5258, 0.02319, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.6019, 0.297, 0.3711, 0.6752]}]},\n",
-       "  {'file': '0000/0312.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.2417, 0.3929, 0.6813, 0.5774]}]},\n",
-       "  {'file': '0000/0313.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.758,\n",
-       "     'bbox': [0.5018, 0.5479, 0.1404, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3833, 0.5959, 0.5946, 0.3745]}]},\n",
-       "  {'file': '0000/0314.jpg',\n",
-       "   'max_detection_conf': 0.931,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.859,\n",
-       "     'bbox': [0.07814, 0.5516, 0.2124, 0.1937]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.3772, 0.5516, 0.1196, 0.2656]}]},\n",
-       "  {'file': '0000/0315.jpg',\n",
-       "   'max_detection_conf': 0.926,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.794,\n",
-       "     'bbox': [0.481, 0.5369, 0.1013, 0.1863]},\n",
-       "    {'category': '1', 'conf': 0.865, 'bbox': [0.3833, 0.5221, 0.1391, 0.166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.912,\n",
-       "     'bbox': [0.2161, 0.5387, 0.08791, 0.2084]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.926,\n",
-       "     'bbox': [0.2857, 0.6457, 0.1953, 0.2232]}]},\n",
-       "  {'file': '0000/0316.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.2136, 0.5369, 0.2258, 0.1863]}]},\n",
-       "  {'file': '0000/0317.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.3186, 0.5885, 0.5164, 0.3819]}]},\n",
-       "  {'file': '0000/0318.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2673, 0.5664, 0.5702, 0.404]}]},\n",
-       "  {'file': '0000/0319.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.831,\n",
-       "     'bbox': [0.6507, 0.5645, 0.1855, 0.1568]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.08547, 0.535, 0.2722, 0.4354]}]},\n",
-       "  {'file': '0000/0320.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.796,\n",
-       "     'bbox': [0.6507, 0.535, 0.1892, 0.19]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.4065, 0.6162, 0.1953, 0.3542]}]},\n",
-       "  {'file': '0000/0321.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.2942, 0.2693, 0.6874, 0.7029]}]},\n",
-       "  {'file': '0000/0322.jpg',\n",
-       "   'max_detection_conf': 0.908,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.4371, 0.5535, 0.1086, 0.2472]}]},\n",
-       "  {'file': '0000/0323.jpg',\n",
-       "   'max_detection_conf': 0.936,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.897,\n",
-       "     'bbox': [0.5262, 0.5664, 0.09645, 0.1918]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.4139, 0.559, 0.1086, 0.2361]}]},\n",
-       "  {'file': '0000/1436.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.007326, 0.06642, 0.6703, 0.9059]}]},\n",
-       "  {'file': '0000/1437.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0102,\n",
-       "     'bbox': [0, 0.7749, 0.04761, 0.1771]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3406, 0.5701, 0.1648, 0.1789]}]},\n",
-       "  {'file': '0000/1438.jpg',\n",
-       "   'max_detection_conf': 0.904,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.904,\n",
-       "     'bbox': [0.304, 0.6273, 0.06959, 0.1826]}]},\n",
-       "  {'file': '0000/1439.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0211,\n",
-       "     'bbox': [0.6031, 0.6088, 0.06471, 0.2121]},\n",
-       "    {'category': '1', 'conf': 0.244, 'bbox': [0.6019, 0.714, 0.03052, 0.1051]},\n",
-       "    {'category': '1', 'conf': 0.906, 'bbox': [0.4566, 0.5959, 0.1306, 0.1549]},\n",
-       "    {'category': '1', 'conf': 0.923, 'bbox': [0.2735, 0.6346, 0.144, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.6043, 0.5405, 0.365, 0.4335]}]},\n",
-       "  {'file': '0000/1440.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.2161, 0.6162, 0.05372, 0.1734]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.4407, 0.535, 0.1111, 0.2472]}]},\n",
-       "  {'file': '0000/1441.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0104,\n",
-       "     'bbox': [0.5555, 0.4261, 0.0818, 0.2343]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0363,\n",
-       "     'bbox': [0.5347, 0.4225, 0.05128, 0.1549]},\n",
-       "    {'category': '1', 'conf': 0.172, 'bbox': [0.5604, 0.4261, 0.0354, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.301,\n",
-       "     'bbox': [0.5592, 0.4261, 0.06593, 0.1328]},\n",
-       "    {'category': '1', 'conf': 0.857, 'bbox': [0.5323, 0.4188, 0.1111, 0.5018]},\n",
-       "    {'category': '1', 'conf': 0.954, 'bbox': [0.2356, 0.4428, 0.2002, 0.4723]},\n",
-       "    {'category': '1', 'conf': 0.972, 'bbox': [0.3638, 0.5424, 0.271, 0.428]}]},\n",
-       "  {'file': '0000/1442.jpg',\n",
-       "   'max_detection_conf': 0.917,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.917,\n",
-       "     'bbox': [0.1599, 0.666, 0.2783, 0.1568]}]},\n",
-       "  {'file': '0000/1443.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.3772, 0.6088, 0.1233, 0.1752]}]},\n",
-       "  {'file': '0000/1444.jpg',\n",
-       "   'max_detection_conf': 0.906,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.906,\n",
-       "     'bbox': [0.006105, 0.2158, 0.5921, 0.6697]}]},\n",
-       "  {'file': '0000/1445.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0204,\n",
-       "     'bbox': [0.01098, 0.2915, 0.07936, 0.09409]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0442,\n",
-       "     'bbox': [0.05616, 0.5516, 0.1575, 0.2214]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4884, 0.4852, 0.1355, 0.249]}]},\n",
-       "  {'file': '0000/1446.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0193,\n",
-       "     'bbox': [0.01953, 0.2933, 0.07448, 0.09409]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0203,\n",
-       "     'bbox': [0.05982, 0.5535, 0.1587, 0.2232]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3137, 0.5018, 0.3321, 0.321]}]},\n",
-       "  {'file': '0000/1447.jpg',\n",
-       "   'max_detection_conf': 0.932,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.932,\n",
-       "     'bbox': [0.01465, 0.2915, 0.7008, 0.6734]}]},\n",
-       "  {'file': '0000/1448.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.3406, 0.4077, 0.2503, 0.38]}]},\n",
-       "  {'file': '0000/1449.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0177,\n",
-       "     'bbox': [0.07936, 0.5664, 0.1343, 0.2177]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.3199, 0.5258, 0.1587, 0.1162]}]},\n",
-       "  {'file': '0000/1450.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0258,\n",
-       "     'bbox': [0.04517, 0.5682, 0.1721, 0.3911]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.4822, 0.5092, 0.2637, 0.3228]}]},\n",
-       "  {'file': '0000/1451.jpg',\n",
-       "   'max_detection_conf': 0.849,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0109,\n",
-       "     'bbox': [0.1819, 0.4557, 0.05372, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0118,\n",
-       "     'bbox': [0.8205, 0.5313, 0.1123, 0.1826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.062,\n",
-       "     'bbox': [0.6324, 0.4538, 0.03907, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.342,\n",
-       "     'bbox': [0.2808, 0.4704, 0.07936, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.849,\n",
-       "     'bbox': [0.4713, 0.4446, 0.09401, 0.1125]}]},\n",
-       "  {'file': '0000/1452.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0137,\n",
-       "     'bbox': [0.08058, 0.5516, 0.133, 0.2343]},\n",
-       "    {'category': '1', 'conf': 0.0602, 'bbox': [0, 0.5553, 0.2148, 0.4169]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.4774, 0.4963, 0.3064, 0.3044]}]},\n",
-       "  {'file': '0000/1453.jpg',\n",
-       "   'max_detection_conf': 0.922,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0108,\n",
-       "     'bbox': [0, 0.4778, 0.1025, 0.273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.922,\n",
-       "     'bbox': [0.3846, 0.5608, 0.08669, 0.07933]}]},\n",
-       "  {'file': '0000/1454.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.271, 0.38, 0.3846, 0.4003]}]},\n",
-       "  {'file': '0000/1455.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0375,\n",
-       "     'bbox': [0.01098, 0.3634, 0.1172, 0.1383]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0547,\n",
-       "     'bbox': [0.06959, 0.5664, 0.1514, 0.2066]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.4688, 0.452, 0.1868, 0.2896]}]},\n",
-       "  {'file': '0000/1456.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0143,\n",
-       "     'bbox': [0.006105, 0.5701, 0.2295, 0.3431]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0404,\n",
-       "     'bbox': [0.01343, 0.3653, 0.1111, 0.1273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0509,\n",
-       "     'bbox': [0.08424, 0.5664, 0.1416, 0.2435]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.4896, 0.476, 0.2808, 0.2915]}]},\n",
-       "  {'file': '0000/1457.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0937,\n",
-       "     'bbox': [0.01953, 0.3653, 0.1025, 0.1309]},\n",
-       "    {'category': '1', 'conf': 0.43, 'bbox': [0.03663, 0.5664, 0.2136, 0.2398]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3113, 0.4926, 0.3492, 0.321]}]},\n",
-       "  {'file': '0000/1458.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.133,\n",
-       "     'bbox': [0.08547, 0.5682, 0.133, 0.2011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2063, 0.4557, 0.4432, 0.3726]}]},\n",
-       "  {'file': '0000/0393.jpg',\n",
-       "   'max_detection_conf': 0.769,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.769,\n",
-       "     'bbox': [0.4346, 0.5701, 0.07081, 0.1217]}]},\n",
-       "  {'file': '0000/0394.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.2905, 0.4612, 0.3785, 0.3118]}]},\n",
-       "  {'file': '0000/0395.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0159,\n",
-       "     'bbox': [0.9352, 0.4114, 0.04273, 0.08487]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0203,\n",
-       "     'bbox': [0.9352, 0.3856, 0.06471, 0.2509]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0833,\n",
-       "     'bbox': [0.9352, 0.3542, 0.06471, 0.1476]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.05372, 0.5184, 0.3492, 0.2583]}]},\n",
-       "  {'file': '0000/0396.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.7851, 0.4889, 0.1611, 0.1549]}]},\n",
-       "  {'file': '0000/0397.jpg',\n",
-       "   'max_detection_conf': 0.926,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.926,\n",
-       "     'bbox': [0.6971, 0.4815, 0.1941, 0.1918]}]},\n",
-       "  {'file': '0000/0398.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.4957, 0.4243, 0.4664, 0.463]}]},\n",
-       "  {'file': '0000/0399.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.3372, 0.6443, 0.5442, 0.3281]}]},\n",
-       "  {'file': '0000/0400.jpg',\n",
-       "   'max_detection_conf': 0.907,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.5909, 0.1365, 0.3919, 0.5424]}]},\n",
-       "  {'file': '0000/0401.jpg',\n",
-       "   'max_detection_conf': 0.928,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.928,\n",
-       "     'bbox': [0.7545, 0.5018, 0.1819, 0.2066]}]},\n",
-       "  {'file': '0000/0402.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.4151, 0.2988, 0.2222, 0.4612]}]},\n",
-       "  {'file': '0000/0403.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.878,\n",
-       "     'bbox': [0.6593, 0.4667, 0.1868, 0.2601]},\n",
-       "    {'category': '1', 'conf': 0.97, 'bbox': [0.105, 0.4501, 0.3638, 0.3284]}]},\n",
-       "  {'file': '0000/0404.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.3101, 0.5073, 0.2673, 0.2343]}]},\n",
-       "  {'file': '0000/0405.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0136,\n",
-       "     'bbox': [0.9841, 0.4889, 0.01587, 0.04797]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.811,\n",
-       "     'bbox': [0.8437, 0.4944, 0.05616, 0.07011]},\n",
-       "    {'category': '1', 'conf': 0.963, 'bbox': [0.42, 0.4704, 0.21, 0.1752]}]},\n",
-       "  {'file': '0000/0406.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.03174, 0.5221, 0.4761, 0.4483]}]},\n",
-       "  {'file': '0000/0407.jpg',\n",
-       "   'max_detection_conf': 0.929,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0253,\n",
-       "     'bbox': [0.4835, 0.7619, 0.05006, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0374,\n",
-       "     'bbox': [0.9181, 0.8708, 0.05372, 0.1014]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.929,\n",
-       "     'bbox': [0.3687, 0.5387, 0.09157, 0.2269]}]},\n",
-       "  {'file': '0000/0408.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0127,\n",
-       "     'bbox': [0.8974, 0.8505, 0.07692, 0.1217]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.4835, 0.4833, 0.1648, 0.166]}]},\n",
-       "  {'file': '0000/0409.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0142,\n",
-       "     'bbox': [0.9072, 0.856, 0.07081, 0.1162]},\n",
-       "    {'category': '1', 'conf': 0.937, 'bbox': [0.249, 0.6273, 0.2539, 0.321]}]},\n",
-       "  {'file': '0000/0410.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.3174, 0.3394, 0.3101, 0.3505]}]},\n",
-       "  {'file': '0000/0411.jpg',\n",
-       "   'max_detection_conf': 0.883,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.883,\n",
-       "     'bbox': [0.1233, 0.1808, 0.862, 0.7896]}]},\n",
-       "  {'file': '0000/0412.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0234,\n",
-       "     'bbox': [0.8901, 0.8302, 0.105, 0.142]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.1807, 0.4833, 0.2344, 0.1789]}]},\n",
-       "  {'file': '0000/1752.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.02319, 0.4077, 0.5995, 0.5627]}]},\n",
-       "  {'file': '0000/1753.jpg',\n",
-       "   'max_detection_conf': 0.922,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.264,\n",
-       "     'bbox': [0.2808, 0.4944, 0.08058, 0.1771]},\n",
-       "    {'category': '1', 'conf': 0.853, 'bbox': [0.2063, 0.4188, 0.1672, 0.2546]},\n",
-       "    {'category': '1', 'conf': 0.922, 'bbox': [0.26, 0.4852, 0.2576, 0.3247]}]},\n",
-       "  {'file': '0000/1754.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.016,\n",
-       "     'bbox': [0.4456, 0.5867, 0.06715, 0.08671]},\n",
-       "    {'category': '1', 'conf': 0.848, 'bbox': [0.5347, 0.5092, 0.199, 0.1494]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.2918, 0.4446, 0.2442, 0.2527]}]},\n",
-       "  {'file': '0000/1755.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.1318, 0.3819, 0.47, 0.5369]}]},\n",
-       "  {'file': '0000/1756.jpg',\n",
-       "   'max_detection_conf': 0.948,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.948,\n",
-       "     'bbox': [0.2344, 0.6088, 0.3223, 0.2103]}]},\n",
-       "  {'file': '0000/1757.jpg',\n",
-       "   'max_detection_conf': 0.667,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0216,\n",
-       "     'bbox': [0.3736, 0.2601, 0.04395, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.667,\n",
-       "     'bbox': [0.2576, 0.4981, 0.1147, 0.09225]}]},\n",
-       "  {'file': '0000/1758.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0484,\n",
-       "     'bbox': [0.9462, 0.6309, 0.03052, 0.0535]},\n",
-       "    {'category': '1', 'conf': 0.949, 'bbox': [0.02442, 0.5516, 0.155, 0.249]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.4212, 0.5332, 0.1782, 0.1752]}]},\n",
-       "  {'file': '0000/1759.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.2087, 0.5756, 0.2625, 0.2601]}]},\n",
-       "  {'file': '0000/1760.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.1282, 0.3819, 0.7277, 0.5701]}]},\n",
-       "  {'file': '0000/1761.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.3003, 0.4741, 0.4639, 0.4298]}]},\n",
-       "  {'file': '0000/1762.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.003663, 0.4446, 0.5836, 0.5258]}]},\n",
-       "  {'file': '0000/1763.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.003663, 0.2306, 0.6288, 0.7195]}]},\n",
-       "  {'file': '0000/1764.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.01465, 0.3357, 0.934, 0.6365]}]},\n",
-       "  {'file': '0000/1765.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.249, 0.4409, 0.4004, 0.3856]}]},\n",
-       "  {'file': '0000/1766.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.008547, 0.3782, 0.9536, 0.594]}]},\n",
-       "  {'file': '0000/1767.jpg',\n",
-       "   'max_detection_conf': 0.932,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.932,\n",
-       "     'bbox': [0.2319, 0.02214, 0.746, 0.9428]}]},\n",
-       "  {'file': '0000/1768.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.004884, 0.08671, 0.6617, 0.6512]}]},\n",
-       "  {'file': '0000/1769.jpg',\n",
-       "   'max_detection_conf': 0.909,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.909,\n",
-       "     'bbox': [0.008547, 0.404, 0.1623, 0.4003]}]},\n",
-       "  {'file': '0000/1770.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3137, 0.7047, 0.3101, 0.2656]}]},\n",
-       "  {'file': '0000/1771.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.2271, 0.476, 0.282, 0.2343]}]},\n",
-       "  {'file': '0000/1772.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.4273, 0.5055, 0.2112, 0.1512]}]},\n",
-       "  {'file': '0000/1773.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.789,\n",
-       "     'bbox': [0.07203, 0.4981, 0.03418, 0.05904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.2356, 0.7084, 0.3528, 0.2619]}]},\n",
-       "  {'file': '0000/1774.jpg',\n",
-       "   'max_detection_conf': 0.786,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.786,\n",
-       "     'bbox': [0.3455, 0.5147, 0.08058, 0.07564]}]},\n",
-       "  {'file': '0000/1775.jpg',\n",
-       "   'max_detection_conf': 0.867,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.867,\n",
-       "     'bbox': [0.6727, 0.2804, 0.3137, 0.3579]}]},\n",
-       "  {'file': '0000/1776.jpg',\n",
-       "   'max_detection_conf': 0.903,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.903,\n",
-       "     'bbox': [0.3137, 0.4907, 0.2967, 0.2343]}]},\n",
-       "  {'file': '0000/1777.jpg',\n",
-       "   'max_detection_conf': 0.883,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.883,\n",
-       "     'bbox': [0.2063, 0.487, 0.1672, 0.1328]}]},\n",
-       "  {'file': '0000/1778.jpg',\n",
-       "   'max_detection_conf': 0.874,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0.3333, 0.4704, 0.2783, 0.2306]}]},\n",
-       "  {'file': '0000/1779.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.4835, 0.4575, 0.1184, 0.1494]}]},\n",
-       "  {'file': '0000/1780.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.498,\n",
-       "     'bbox': [0.3052, 0.4649, 0.03663, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.4004, 0.5055, 0.4688, 0.4649]}]},\n",
-       "  {'file': '0000/1781.jpg',\n",
-       "   'max_detection_conf': 0.765,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.765,\n",
-       "     'bbox': [0.4322, 0.5332, 0.06349, 0.06273]}]},\n",
-       "  {'file': '0000/0013.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.934,\n",
-       "     'bbox': [0.1575, 0.6549, 0.07448, 0.2915]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.948,\n",
-       "     'bbox': [0.4529, 0.6088, 0.05982, 0.2564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.5274, 0.6383, 0.07936, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.315, 0.6771, 0.1343, 0.1955]}]},\n",
-       "  {'file': '0000/0045.jpg',\n",
-       "   'max_detection_conf': 0.846,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.846,\n",
-       "     'bbox': [0.42, 0.4778, 0.06227, 0.1033]}]},\n",
-       "  {'file': '0000/0078.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.919,\n",
-       "     'bbox': [0.5421, 0.4372, 0.2869, 0.3708]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.2783, 0.5332, 0.3663, 0.3985]}]},\n",
-       "  {'file': '0000/0112.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0225,\n",
-       "     'bbox': [0.8669, 0.3044, 0.1025, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0379,\n",
-       "     'bbox': [0.7203, 0.5719, 0.1843, 0.1955]},\n",
-       "    {'category': '1', 'conf': 0.0485, 'bbox': [0.757, 0.4501, 0.2148, 0.1494]},\n",
-       "    {'category': '1', 'conf': 0.124, 'bbox': [0.862, 0.5276, 0.1135, 0.4464]},\n",
-       "    {'category': '1', 'conf': 0.192, 'bbox': [0.7228, 0.511, 0.2527, 0.4649]},\n",
-       "    {'category': '1', 'conf': 0.705, 'bbox': [0.7521, 0.452, 0.221, 0.3228]},\n",
-       "    {'category': '1', 'conf': 0.939, 'bbox': [0.326, 0.3321, 0.3333, 0.2343]},\n",
-       "    {'category': '1', 'conf': 0.972, 'bbox': [0.304, 0.487, 0.4664, 0.4852]}]},\n",
-       "  {'file': '0000/0146.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0146,\n",
-       "     'bbox': [0.8937, 0.7767, 0.1025, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0413,\n",
-       "     'bbox': [0.9279, 0.7047, 0.07203, 0.2398]},\n",
-       "    {'category': '1', 'conf': 0.897, 'bbox': [0.2637, 0.4778, 0.1782, 0.3118]},\n",
-       "    {'category': '1', 'conf': 0.901, 'bbox': [0.1379, 0.4354, 0.2051, 0.3044]},\n",
-       "    {'category': '1', 'conf': 0.906, 'bbox': [0.5274, 0.4852, 0.1697, 0.2619]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.387, 0.4981, 0.4664, 0.4741]}]},\n",
-       "  {'file': '0000/0171.jpg',\n",
-       "   'max_detection_conf': 0.857,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.857,\n",
-       "     'bbox': [0.2417, 0.5922, 0.2051, 0.1402]}]},\n",
-       "  {'file': '0000/0191.jpg',\n",
-       "   'max_detection_conf': 0.918,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.918,\n",
-       "     'bbox': [0.4761, 0.5682, 0.07936, 0.118]}]},\n",
-       "  {'file': '0000/0215.jpg',\n",
-       "   'max_detection_conf': 0.595,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.595,\n",
-       "     'bbox': [0.4627, 0.5313, 0.08058, 0.08487]}]},\n",
-       "  {'file': '0000/0238.jpg',\n",
-       "   'max_detection_conf': 0.639,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0103,\n",
-       "     'bbox': [0.3956, 0.5092, 0.03174, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0132,\n",
-       "     'bbox': [0.4297, 0.476, 0.04884, 0.06088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0144,\n",
-       "     'bbox': [0.2954, 0.4926, 0.07936, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0167,\n",
-       "     'bbox': [0.4273, 0.4963, 0.03907, 0.05904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.488,\n",
-       "     'bbox': [0.2954, 0.4944, 0.1257, 0.09594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.639,\n",
-       "     'bbox': [0.4261, 0.4981, 0.05616, 0.09778]}]},\n",
-       "  {'file': '0000/0274.jpg',\n",
-       "   'max_detection_conf': 0.908,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0643,\n",
-       "     'bbox': [0.6218, 0.3984, 0.03025, 0.04568]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.17,\n",
-       "     'bbox': [0.6235, 0.3984, 0.05714, 0.05329]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.812,\n",
-       "     'bbox': [0.4689, 0.505, 0.09411, 0.09137]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.881,\n",
-       "     'bbox': [0.4285, 0.4416, 0.04873, 0.07614]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.885,\n",
-       "     'bbox': [0.2655, 0.4492, 0.08235, 0.09137]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.4974, 0.4441, 0.09579, 0.08883]}]},\n",
-       "  {'file': '0000/0298.jpg',\n",
-       "   'max_detection_conf': 0.922,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.922,\n",
-       "     'bbox': [0.4969, 0.476, 0.1098, 0.09963]}]},\n",
-       "  {'file': '0000/0324.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0368,\n",
-       "     'bbox': [0.4346, 0.5498, 0.07448, 0.0904]},\n",
-       "    {'category': '1', 'conf': 0.0788, 'bbox': [0.608, 0.5553, 0.0989, 0.1254]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.609,\n",
-       "     'bbox': [0.4676, 0.5516, 0.03907, 0.0738]},\n",
-       "    {'category': '1', 'conf': 0.928, 'bbox': [0.4041, 0.5608, 0.1172, 0.2195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.05982, 0.5904, 0.2136, 0.38]}]},\n",
-       "  {'file': '0000/0358.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0129,\n",
-       "     'bbox': [0.4481, 0.4003, 0.1098, 0.1402]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0131,\n",
-       "     'bbox': [0.7484, 0.4077, 0.09645, 0.08302]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4407, 0.3892, 0.05128, 0.05535]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4468, 0.3782, 0.0757, 0.04428]},\n",
-       "    {'category': '1', 'conf': 0.022, 'bbox': [0.4847, 0.38, 0.08791, 0.03505]},\n",
-       "    {'category': '1',\n",
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-       "    {'category': '1',\n",
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-       "     'bbox': [0.8388, 0.4501, 0.04151, 0.0535]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4493, 0.3874, 0.1599, 0.09409]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.3394, 0.3837, 0.06349, 0.07749]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.6312, 0.3874, 0.06349, 0.05535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0534,\n",
-       "     'bbox': [0.5579, 0.3782, 0.09157, 0.03136]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4639, 0.3837, 0.07814, 0.05535]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.5494, 0.3874, 0.04761, 0.02398]},\n",
-       "    {'category': '1', 'conf': 0.186, 'bbox': [0.4908, 0.38, 0.03785, 0.03321]},\n",
-       "    {'category': '1', 'conf': 0.247, 'bbox': [0.3394, 0.3837, 0.1184, 0.1107]},\n",
-       "    {'category': '1', 'conf': 0.296, 'bbox': [0.4395, 0.404, 0.0293, 0.04059]},\n",
-       "    {'category': '1', 'conf': 0.425, 'bbox': [0.724, 0.4059, 0.1538, 0.1199]},\n",
-       "    {'category': '1', 'conf': 0.488, 'bbox': [0.5518, 0.38, 0.1416, 0.06457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.503,\n",
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-       "    {'category': '1', 'conf': 0.847, 'bbox': [0.4957, 0.4391, 0.1477, 0.3819]},\n",
-       "    {'category': '1', 'conf': 0.945, 'bbox': [0.4371, 0.4059, 0.2112, 0.2453]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.6166, 0.4335, 0.1733, 0.5387]}]},\n",
-       "  {'file': '0000/0392.jpg',\n",
-       "   'max_detection_conf': 0.901,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.901,\n",
-       "     'bbox': [0.4346, 0.5701, 0.1245, 0.1107]}]},\n",
-       "  {'file': '0000/0413.jpg',\n",
-       "   'max_detection_conf': 0.854,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.854,\n",
-       "     'bbox': [0.6788, 0.5073, 0.3211, 0.3357]}]},\n",
-       "  {'file': '0000/0438.jpg',\n",
-       "   'max_detection_conf': 0.0409,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.024,\n",
-       "     'bbox': [0.06959, 0.7767, 0.08302, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0409,\n",
-       "     'bbox': [0.3614, 0.1088, 0.01221, 0.02214]}]},\n",
-       "  {'file': '0000/0471.jpg',\n",
-       "   'max_detection_conf': 0.758,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0233,\n",
-       "     'bbox': [0.4713, 0.511, 0.03418, 0.04612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.758,\n",
-       "     'bbox': [0.4725, 0.511, 0.06593, 0.05535]}]},\n",
-       "  {'file': '0000/0500.jpg',\n",
-       "   'max_detection_conf': 0.901,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.901,\n",
-       "     'bbox': [0.3711, 0.4815, 0.06837, 0.0738]}]},\n",
-       "  {'file': '0000/0521.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.1623, 0.4225, 0.3504, 0.2841]}]},\n",
-       "  {'file': '0000/0541.jpg',\n",
-       "   'max_detection_conf': 0.854,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.854,\n",
-       "     'bbox': [0.4945, 0.4815, 0.1282, 0.1808]}]},\n",
-       "  {'file': '0000/0565.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.91,\n",
-       "     'bbox': [0.5152, 0.3671, 0.1159, 0.2564]},\n",
-       "    {'category': '1', 'conf': 0.963, 'bbox': [0.5763, 0.4372, 0.155, 0.3007]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3369, 0.5147, 0.271, 0.4557]}]},\n",
-       "  {'file': '0000/0601.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.1184, 0.6107, 0.238, 0.1974]}]},\n",
-       "  {'file': '0000/0632.jpg',\n",
-       "   'max_detection_conf': 0.874,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0101,\n",
-       "     'bbox': [0.04395, 0.8007, 0.06715, 0.08302]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.874,\n",
-       "     'bbox': [0.3199, 0.6863, 0.08791, 0.1088]}]},\n",
-       "  {'file': '0000/0653.jpg',\n",
-       "   'max_detection_conf': 0.981,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.981,\n",
-       "     'bbox': [0.274, 0.488, 0.4218, 0.4832]}]},\n",
-       "  {'file': '0000/0147.jpg',\n",
-       "   'max_detection_conf': 0.926,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.926,\n",
-       "     'bbox': [0.4993, 0.6162, 0.09523, 0.1273]}]},\n",
-       "  {'file': '0000/0148.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.556,\n",
-       "     'bbox': [0.4334, 0.3966, 0.04761, 0.09225]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.4285, 0.3671, 0.326, 0.2859]}]},\n",
-       "  {'file': '0000/0149.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.3516, 0.3634, 0.3308, 0.2749]}]},\n",
-       "  {'file': '0000/0150.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.3943, 0.3357, 0.1306, 0.2453]}]},\n",
-       "  {'file': '0000/0151.jpg',\n",
-       "   'max_detection_conf': 0.89,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.045,\n",
-       "     'bbox': [0.6507, 0.5996, 0.1843, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.89,\n",
-       "     'bbox': [0.4078, 0.6715, 0.06349, 0.1346]}]},\n",
-       "  {'file': '0000/0152.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.5091, 0.4981, 0.09279, 0.2324]}]},\n",
-       "  {'file': '0000/0153.jpg',\n",
-       "   'max_detection_conf': 0.876,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.876,\n",
-       "     'bbox': [0.4981, 0.5608, 0.05982, 0.1199]}]},\n",
-       "  {'file': '0000/0154.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.2417, 0.2693, 0.3223, 0.428]}]},\n",
-       "  {'file': '0000/0155.jpg',\n",
-       "   'max_detection_conf': 0.931,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.015,\n",
-       "     'bbox': [0.6874, 0.3062, 0.1807, 0.1697]},\n",
-       "    {'category': '1', 'conf': 0.101, 'bbox': [0.7643, 0.3154, 0.1245, 0.1199]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.176,\n",
-       "     'bbox': [0.7655, 0.3173, 0.06715, 0.07749]},\n",
-       "    {'category': '1', 'conf': 0.546, 'bbox': [0.2344, 0.214, 0.1794, 0.09594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.2612, 0.273, 0.3821, 0.4649]}]},\n",
-       "  {'file': '0000/0156.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.841,\n",
-       "     'bbox': [0.4908, 0.2638, 0.07203, 0.1107]},\n",
-       "    {'category': '1', 'conf': 0.931, 'bbox': [0.7032, 0.3357, 0.1746, 0.2822]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.2014, 0.2527, 0.4102, 0.3634]}]},\n",
-       "  {'file': '0000/0157.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.5873, 0.3062, 0.2551, 0.3136]}]},\n",
-       "  {'file': '0000/0158.jpg',\n",
-       "   'max_detection_conf': 0.902,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.902,\n",
-       "     'bbox': [0.3711, 0.5184, 0.1465, 0.1605]}]},\n",
-       "  {'file': '0000/0159.jpg',\n",
-       "   'max_detection_conf': 0.889,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.889,\n",
-       "     'bbox': [0.2735, 0.4538, 0.2551, 0.225]}]},\n",
-       "  {'file': '0000/0160.jpg',\n",
-       "   'max_detection_conf': 0.77,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.77,\n",
-       "     'bbox': [0.5592, 0.5369, 0.1599, 0.1125]}]},\n",
-       "  {'file': '0000/0161.jpg',\n",
-       "   'max_detection_conf': 0.906,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.906,\n",
-       "     'bbox': [0.4517, 0.5405, 0.1404, 0.1383]}]},\n",
-       "  {'file': '0000/0162.jpg',\n",
-       "   'max_detection_conf': 0.596,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0248,\n",
-       "     'bbox': [0.5518, 0.2675, 0.06593, 0.0369]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.401,\n",
-       "     'bbox': [0.3907, 0.6014, 0.09035, 0.1014]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.596,\n",
-       "     'bbox': [0.3089, 0.6014, 0.1733, 0.118]}]},\n",
-       "  {'file': '0000/0163.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0103,\n",
-       "     'bbox': [0.5775, 0.2693, 0.06593, 0.04428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3711, 0.4649, 0.199, 0.2546]}]},\n",
-       "  {'file': '0000/0164.jpg',\n",
-       "   'max_detection_conf': 0.956,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.956,\n",
-       "     'bbox': [0.3015, 0.6217, 0.1733, 0.3505]}]},\n",
-       "  {'file': '0000/0165.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.62,\n",
-       "     'bbox': [0.8192, 0.6771, 0.1599, 0.2785]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.848,\n",
-       "     'bbox': [0.008547, 0.7583, 0.1062, 0.214]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.2869, 0.6125, 0.2039, 0.3597]}]},\n",
-       "  {'file': '0000/0166.jpg',\n",
-       "   'max_detection_conf': 0.865,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0139,\n",
-       "     'bbox': [0.6068, 0.5774, 0.1123, 0.1826]},\n",
-       "    {'category': '1', 'conf': 0.699, 'bbox': [0.8266, 0.5885, 0.1636, 0.3837]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.865,\n",
-       "     'bbox': [0.3516, 0.5332, 0.08669, 0.1678]}]},\n",
-       "  {'file': '0000/0167.jpg',\n",
-       "   'max_detection_conf': 0.833,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0292,\n",
-       "     'bbox': [0.4786, 0.5922, 0.2112, 0.1642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0398,\n",
-       "     'bbox': [0.8302, 0.5258, 0.1452, 0.2121]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0923,\n",
-       "     'bbox': [0.8913, 0.5332, 0.08302, 0.0904]},\n",
-       "    {'category': '1', 'conf': 0.687, 'bbox': [0.387, 0.6974, 0.2405, 0.2749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.699,\n",
-       "     'bbox': [0.8144, 0.4944, 0.09279, 0.2343]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.833,\n",
-       "     'bbox': [0.3797, 0.4612, 0.06471, 0.225]}]},\n",
-       "  {'file': '0000/0168.jpg',\n",
-       "   'max_detection_conf': 0.919,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0334,\n",
-       "     'bbox': [0.7338, 0.4907, 0.06471, 0.08856]},\n",
-       "    {'category': '1', 'conf': 0.073, 'bbox': [0.7326, 0.4907, 0.1123, 0.1217]},\n",
-       "    {'category': '1', 'conf': 0.325, 'bbox': [0.7655, 0.5166, 0.0989, 0.166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.919,\n",
-       "     'bbox': [0.4212, 0.4797, 0.1245, 0.1715]}]},\n",
-       "  {'file': '0000/0169.jpg',\n",
-       "   'max_detection_conf': 0.812,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0107,\n",
-       "     'bbox': [0.8913, 0.4298, 0.08547, 0.1955]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0128,\n",
-       "     'bbox': [0.6898, 0.4372, 0.05494, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.237,\n",
-       "     'bbox': [0.5958, 0.4446, 0.06349, 0.1678]},\n",
-       "    {'category': '1', 'conf': 0.743, 'bbox': [0.6459, 0.4372, 0.0989, 0.1623]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.812,\n",
-       "     'bbox': [0.3882, 0.4464, 0.0586, 0.1678]}]},\n",
-       "  {'file': '0000/0170.jpg',\n",
-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.2747, 0.6309, 0.177, 0.1808]}]},\n",
-       "  {'file': '0000/0602.jpg',\n",
-       "   'max_detection_conf': 0.941,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0192,\n",
-       "     'bbox': [0.1733, 0.4704, 0.09279, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.941,\n",
-       "     'bbox': [0.4139, 0.5184, 0.08547, 0.118]}]},\n",
-       "  {'file': '0000/0603.jpg',\n",
-       "   'max_detection_conf': 0.862,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.122,\n",
-       "     'bbox': [0.1794, 0.4686, 0.09768, 0.1365]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.862,\n",
-       "     'bbox': [0.4969, 0.5332, 0.0586, 0.1715]}]},\n",
-       "  {'file': '0000/0604.jpg',\n",
-       "   'max_detection_conf': 0.917,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0154,\n",
-       "     'bbox': [0.8241, 0.5645, 0.1758, 0.4077]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0209,\n",
-       "     'bbox': [0.2747, 0.5885, 0.05616, 0.0738]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.917,\n",
-       "     'bbox': [0.2735, 0.559, 0.09279, 0.1033]}]},\n",
-       "  {'file': '0000/0605.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.3565, 0.5147, 0.3943, 0.4225]}]},\n",
-       "  {'file': '0000/0606.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.004884, 0.5405, 0.5934, 0.4298]}]},\n",
-       "  {'file': '0000/0607.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0113,\n",
-       "     'bbox': [0.6141, 0.6771, 0.09768, 0.1199]},\n",
-       "    {'category': '1', 'conf': 0.565, 'bbox': [0.6166, 0.6678, 0.2051, 0.1328]},\n",
-       "    {'category': '1', 'conf': 0.88, 'bbox': [0.5787, 0.6918, 0.3858, 0.2804]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.2832, 0.5848, 0.2271, 0.3265]}]},\n",
-       "  {'file': '0000/0608.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0419,\n",
-       "     'bbox': [0, 0.7712, 0.2136, 0.2011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.02197, 0.6088, 0.6153, 0.3634]}]},\n",
-       "  {'file': '0000/0609.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.2344, 0.3228, 0.6068, 0.5848]}]},\n",
-       "  {'file': '0000/0610.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.3601, 0.345, 0.1257, 0.4409]}]},\n",
-       "  {'file': '0000/0611.jpg',\n",
-       "   'max_detection_conf': 0.768,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.768,\n",
-       "     'bbox': [0.4407, 0.4575, 0.08547, 0.05166]}]},\n",
-       "  {'file': '0000/0612.jpg',\n",
-       "   'max_detection_conf': 0.903,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0163,\n",
-       "     'bbox': [0.1636, 0.4391, 0.03052, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0331,\n",
-       "     'bbox': [0.01465, 0.4575, 0.06349, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0656,\n",
-       "     'bbox': [0.5579, 0.5701, 0.04395, 0.1291]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.077,\n",
-       "     'bbox': [0.7533, 0.5904, 0.1111, 0.07933]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.776,\n",
-       "     'bbox': [0.1184, 0.4335, 0.07448, 0.1125]},\n",
-       "    {'category': '1', 'conf': 0.814, 'bbox': [0.365, 0.3948, 0.03785, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.903,\n",
-       "     'bbox': [0.3736, 0.4464, 0.1025, 0.1881]}]},\n",
-       "  {'file': '0000/0613.jpg',\n",
-       "   'max_detection_conf': 0.73,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0947,\n",
-       "     'bbox': [0.3931, 0.4335, 0.1013, 0.0904]},\n",
-       "    {'category': '1', 'conf': 0.26, 'bbox': [0.5177, 0.5682, 0.04639, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.73,\n",
-       "     'bbox': [0.4371, 0.4557, 0.05738, 0.06826]}]},\n",
-       "  {'file': '0000/0614.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.3369, 0.4059, 0.2173, 0.2656]}]},\n",
-       "  {'file': '0000/0615.jpg',\n",
-       "   'max_detection_conf': 0.845,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.845,\n",
-       "     'bbox': [0.4334, 0.4483, 0.08791, 0.08856]}]},\n",
-       "  {'file': '0000/0616.jpg',\n",
-       "   'max_detection_conf': 0.95,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.95,\n",
-       "     'bbox': [0.6043, 0.583, 0.1123, 0.1236]}]},\n",
-       "  {'file': '0000/0617.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0581,\n",
-       "     'bbox': [0.5702, 0.6217, 0.04884, 0.1217]},\n",
-       "    {'category': '1', 'conf': 0.959, 'bbox': [0.3028, 0.4446, 0.238, 0.225]}]},\n",
-       "  {'file': '0000/0618.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.4456, 0.4446, 0.2026, 0.1937]}]},\n",
-       "  {'file': '0000/0619.jpg',\n",
-       "   'max_detection_conf': 0.915,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.915,\n",
-       "     'bbox': [0.5628, 0.5129, 0.1575, 0.1236]}]},\n",
-       "  {'file': '0000/0620.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0109,\n",
-       "     'bbox': [0.7045, 0.4815, 0.04884, 0.07564]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3882, 0.4372, 0.1733, 0.1752]}]},\n",
-       "  {'file': '0000/0621.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3516, 0.4446, 0.199, 0.1992]}]},\n",
-       "  {'file': '0000/0622.jpg',\n",
-       "   'max_detection_conf': 0.857,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.857,\n",
-       "     'bbox': [0.5628, 0.4944, 0.08791, 0.0738]}]},\n",
-       "  {'file': '0000/0623.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.1343, 0.5129, 0.5445, 0.4446]}]},\n",
-       "  {'file': '0000/0624.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0295,\n",
-       "     'bbox': [0.8913, 0.02952, 0.1086, 0.9428]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0307,\n",
-       "     'bbox': [0.2808, 0.7675, 0.07081, 0.107]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.882,\n",
-       "     'bbox': [0.4908, 0.5922, 0.09523, 0.09225]},\n",
-       "    {'category': '1', 'conf': 0.958, 'bbox': [0.2967, 0.4704, 0.1929, 0.5]}]},\n",
-       "  {'file': '0000/0625.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0621,\n",
-       "     'bbox': [0, 0.4557, 0.1416, 0.1826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.4114, 0.2232, 0.282, 0.7472]}]},\n",
-       "  {'file': '0000/0626.jpg',\n",
-       "   'max_detection_conf': 0.904,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.822,\n",
-       "     'bbox': [0.5286, 0.487, 0.08058, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.904,\n",
-       "     'bbox': [0.3577, 0.4889, 0.1282, 0.1143]}]},\n",
-       "  {'file': '0000/0627.jpg',\n",
-       "   'max_detection_conf': 0.709,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0412,\n",
-       "     'bbox': [0.2942, 0.69, 0.05494, 0.08856]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.709,\n",
-       "     'bbox': [0.409, 0.4981, 0.1025, 0.08487]}]},\n",
-       "  {'file': '0000/0628.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.3577, 0.4797, 0.1269, 0.225]}]},\n",
-       "  {'file': '0000/0629.jpg',\n",
-       "   'max_detection_conf': 0.792,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.792,\n",
-       "     'bbox': [0.01465, 0.02952, 0.1684, 0.9372]}]},\n",
-       "  {'file': '0000/0630.jpg',\n",
-       "   'max_detection_conf': 0.925,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0133,\n",
-       "     'bbox': [0.6544, 0.5922, 0.1294, 0.07011]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0164,\n",
-       "     'bbox': [0.6556, 0.6014, 0.06593, 0.05904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.925,\n",
-       "     'bbox': [0.4481, 0.4778, 0.08791, 0.1346]}]},\n",
-       "  {'file': '0000/0631.jpg',\n",
-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.049,\n",
-       "     'bbox': [0.8815, 0.01476, 0.1184, 0.9612]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0966,\n",
-       "     'bbox': [0.2918, 0.6808, 0.05616, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.503, 0.5276, 0.1831, 0.1697]}]},\n",
-       "  {'file': '0000/0216.jpg',\n",
-       "   'max_detection_conf': 0.871,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0234,\n",
-       "     'bbox': [0.4639, 0.3357, 0.1208, 0.2029]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.871,\n",
-       "     'bbox': [0.4932, 0.4243, 0.09157, 0.1162]}]},\n",
-       "  {'file': '0000/0217.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0215,\n",
-       "     'bbox': [0.354, 0.4243, 0.07692, 0.09778]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.4297, 0.4464, 0.1306, 0.142]}]},\n",
-       "  {'file': '0000/0218.jpg',\n",
-       "   'max_detection_conf': 0.887,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0175,\n",
-       "     'bbox': [0.8058, 0.5664, 0.1416, 0.1955]},\n",
-       "    {'category': '1', 'conf': 0.237, 'bbox': [0.6056, 0.2767, 0.0818, 0.1402]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.887,\n",
-       "     'bbox': [0.4468, 0.4243, 0.1306, 0.1439]}]},\n",
-       "  {'file': '0000/0219.jpg',\n",
-       "   'max_detection_conf': 0.882,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0138,\n",
-       "     'bbox': [0.807, 0.5645, 0.1172, 0.1476]},\n",
-       "    {'category': '1', 'conf': 0.812, 'bbox': [0.597, 0.2785, 0.1172, 0.2084]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.882,\n",
-       "     'bbox': [0.4102, 0.5018, 0.1208, 0.1512]}]},\n",
-       "  {'file': '0000/0220.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3345, 0.3929, 0.188, 0.2435]}]},\n",
-       "  {'file': '0000/0221.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.2637, 0.4594, 0.4481, 0.4926]}]},\n",
-       "  {'file': '0000/0222.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.05128, 0.1752, 0.4212, 0.7952]}]},\n",
-       "  {'file': '0000/0223.jpg',\n",
-       "   'max_detection_conf': 0.848,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.848,\n",
-       "     'bbox': [0.001221, 0.5184, 0.3418, 0.3874]}]},\n",
-       "  {'file': '0000/0224.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.2625, 0.4501, 0.4004, 0.5221]}]},\n",
-       "  {'file': '0000/0225.jpg',\n",
-       "   'max_detection_conf': 0.928,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.928,\n",
-       "     'bbox': [0.4493, 0.4704, 0.1172, 0.4151]}]},\n",
-       "  {'file': '0000/0226.jpg',\n",
-       "   'max_detection_conf': 0.643,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.643,\n",
-       "     'bbox': [0.05616, 0.03321, 0.9328, 0.9354]}]},\n",
-       "  {'file': '0000/0227.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.09401, 0.4594, 0.5836, 0.5129]}]},\n",
-       "  {'file': '0000/0228.jpg',\n",
-       "   'max_detection_conf': 0.717,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.717,\n",
-       "     'bbox': [0.01465, 0.3431, 0.5726, 0.6217]}]},\n",
-       "  {'file': '0000/0229.jpg',\n",
-       "   'max_detection_conf': 0.929,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.662,\n",
-       "     'bbox': [0.4786, 0.5701, 0.1318, 0.2509]},\n",
-       "    {'category': '1', 'conf': 0.876, 'bbox': [0.7728, 0.5516, 0.2014, 0.2693]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.929,\n",
-       "     'bbox': [0.01709, 0.4649, 0.4932, 0.5073]}]},\n",
-       "  {'file': '0000/0230.jpg',\n",
-       "   'max_detection_conf': 0.937,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0473,\n",
-       "     'bbox': [0.5873, 0.6162, 0.1147, 0.142]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.937,\n",
-       "     'bbox': [0.547, 0.4797, 0.2393, 0.3265]}]},\n",
-       "  {'file': '0000/0231.jpg',\n",
-       "   'max_detection_conf': 0.863,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.646,\n",
-       "     'bbox': [0.4236, 0.535, 0.06593, 0.09594]},\n",
-       "    {'category': '1', 'conf': 0.832, 'bbox': [0.3186, 0.3505, 0.1282, 0.2324]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.863,\n",
-       "     'bbox': [0.4871, 0.5369, 0.07326, 0.08671]}]},\n",
-       "  {'file': '0000/0232.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.3125, 0.3948, 0.2893, 0.2749]}]},\n",
-       "  {'file': '0000/0233.jpg',\n",
-       "   'max_detection_conf': 0.876,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.876,\n",
-       "     'bbox': [0.282, 0.4981, 0.2478, 0.3837]}]},\n",
-       "  {'file': '0000/0234.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.814,\n",
-       "     'bbox': [0.4126, 0.5018, 0.1391, 0.1254]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.4493, 0.5885, 0.2417, 0.3819]}]},\n",
-       "  {'file': '0000/0235.jpg',\n",
-       "   'max_detection_conf': 0.935,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.935,\n",
-       "     'bbox': [0.4285, 0.5498, 0.1233, 0.3726]}]},\n",
-       "  {'file': '0000/0236.jpg',\n",
-       "   'max_detection_conf': 0.914,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0128,\n",
-       "     'bbox': [0.4896, 0.5129, 0.07081, 0.09963]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.359,\n",
-       "     'bbox': [0.4908, 0.5258, 0.04761, 0.06457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.406,\n",
-       "     'bbox': [0.3943, 0.5553, 0.06105, 0.1014]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.914,\n",
-       "     'bbox': [0.4859, 0.5867, 0.07326, 0.1678]}]},\n",
-       "  {'file': '0000/0237.jpg',\n",
-       "   'max_detection_conf': 0.711,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0719,\n",
-       "     'bbox': [0.7203, 0.4797, 0.04884, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.711,\n",
-       "     'bbox': [0.4175, 0.5036, 0.06837, 0.1014]}]},\n",
-       "  {'file': '0000/1355.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0107,\n",
-       "     'bbox': [0.5128, 0.4538, 0.05738, 0.06457]},\n",
-       "    {'category': '1', 'conf': 0.858, 'bbox': [0.3321, 0.4612, 0.1184, 0.1494]},\n",
-       "    {'category': '1', 'conf': 0.875, 'bbox': [0.5372, 0.5, 0.05738, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.3846, 0.4575, 0.2832, 0.3284]}]},\n",
-       "  {'file': '0000/1356.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.836,\n",
-       "     'bbox': [0.7936, 0.5701, 0.1831, 0.1863]},\n",
-       "    {'category': '1', 'conf': 0.898, 'bbox': [0.1111, 0.4907, 0.1111, 0.3099]},\n",
-       "    {'category': '1', 'conf': 0.965, 'bbox': [0.42, 0.463, 0.1514, 0.4372]}]},\n",
-       "  {'file': '0000/1357.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.08058, 0.4464, 0.3907, 0.428]}]},\n",
-       "  {'file': '0000/1358.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0163,\n",
-       "     'bbox': [0.8217, 0.3339, 0.01709, 0.01845]},\n",
-       "    {'category': '1', 'conf': 0.943, 'bbox': [0, 0.3726, 0.3821, 0.297]}]},\n",
-       "  {'file': '0000/1359.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.3003, 0.2047, 0.6752, 0.594]}]},\n",
-       "  {'file': '0000/1360.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.4517, 0.5701, 0.4285, 0.3892]}]},\n",
-       "  {'file': '0000/1361.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.1929, 0.1273, 0.807, 0.8431]}]},\n",
-       "  {'file': '0000/1362.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.03238, 0.7183, 0.5805, 0.2541]}]},\n",
-       "  {'file': '0000/1363.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.4151, 0.4059, 0.4261, 0.3597]}]},\n",
-       "  {'file': '0000/1364.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.009768, 0.3597, 0.42, 0.3819]}]},\n",
-       "  {'file': '0000/1365.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.001221, 0.4243, 0.2332, 0.3118]}]},\n",
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-       "   'max_detection_conf': 0.934,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.934,\n",
-       "     'bbox': [0.03174, 0.5092, 0.934, 0.463]}]},\n",
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-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.01587, 0.09594, 0.6202, 0.8745]}]},\n",
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-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.1172, 0.2158, 0.5384, 0.6881]}]},\n",
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-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.4383, 0.4612, 0.3113, 0.5092]}]},\n",
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-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.955,\n",
-       "     'bbox': [0.3675, 0.5553, 0.5873, 0.3616]}]},\n",
-       "  {'file': '0000/1371.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3858, 0.3616, 0.3186, 0.3173]}]},\n",
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-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.1282, 0.535, 0.7692, 0.4354]}]},\n",
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-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.01465, 0.535, 0.956, 0.4372]}]},\n",
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-       "   'max_detection_conf': 0.819,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.819,\n",
-       "     'bbox': [0.008547, 0.03321, 0.967, 0.9372]}]},\n",
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-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.9072, 0.02952, 0.06593, 0.2656]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.01098, 0.03321, 0.9609, 0.9372]}]},\n",
-       "  {'file': '0000/0808.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.2857, 0.5184, 0.2087, 0.2121]}]},\n",
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-       "   'max_detection_conf': 0.915,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.2832, 0.4372, 0.07326, 0.09409]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.915,\n",
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-       "  {'file': '0000/0810.jpg',\n",
-       "   'max_detection_conf': 0.871,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.871,\n",
-       "     'bbox': [0.3663, 0.4575, 0.1611, 0.1494]}]},\n",
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-       "   'max_detection_conf': 0.542,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.768, 0.1051, 0.0293, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.542,\n",
-       "     'bbox': [0.3382, 0.4151, 0.0818, 0.1107]}]},\n",
-       "  {'file': '0000/0812.jpg',\n",
-       "   'max_detection_conf': 0.931,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.931,\n",
-       "     'bbox': [0.304, 0.5608, 0.2148, 0.2084]}]},\n",
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-       "   'max_detection_conf': 0.862,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.862,\n",
-       "     'bbox': [0.2075, 0.5535, 0.2551, 0.1974]}]},\n",
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-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.1648, 0.6254, 0.2893, 0.2509]}]},\n",
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-       "   'max_detection_conf': 0.85,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.85,\n",
-       "     'bbox': [0.5274, 0.6568, 0.1807, 0.2066]}]},\n",
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-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.01098, 0.5996, 0.2393, 0.2712]}]},\n",
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-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.07326, 0.4335, 0.9059, 0.535]}]},\n",
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-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.271, 0.4298, 0.2954, 0.4077]}]},\n",
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-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.3186, 0.4575, 0.2283, 0.3099]}]},\n",
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-       "   'max_detection_conf': 0.921,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.921,\n",
-       "     'bbox': [0.04273, 0.5202, 0.2136, 0.2306]}]},\n",
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-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.904,\n",
-       "     'bbox': [0.4517, 0.4538, 0.08791, 0.08302]}]},\n",
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-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.3601, 0.5442, 0.1416, 0.2361]}]},\n",
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-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
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-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.4285, 0.4428, 0.1013, 0.1217]}]},\n",
-       "  {'file': '0000/0825.jpg',\n",
-       "   'max_detection_conf': 0.942,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.942,\n",
-       "     'bbox': [0.3125, 0.5295, 0.1196, 0.1476]}]},\n",
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-       "   'max_detection_conf': 0.81,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.015,\n",
-       "     'bbox': [0.4835, 0.559, 0.04884, 0.03874]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.81,\n",
-       "     'bbox': [0.3785, 0.5202, 0.05372, 0.04981]}]},\n",
-       "  {'file': '0000/0827.jpg',\n",
-       "   'max_detection_conf': 0.905,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0151,\n",
-       "     'bbox': [0.3626, 0.4833, 0.04517, 0.1088]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0289,\n",
-       "     'bbox': [0.4981, 0.4649, 0.04029, 0.09594]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.2393, 0.4667, 0.05738, 0.1273]},\n",
-       "    {'category': '1', 'conf': 0.779, 'bbox': [0.4188, 0.4538, 0.1184, 0.1199]},\n",
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-       "    {'category': '1', 'conf': 0.858, 'bbox': [0.365, 0.4833, 0.09523, 0.1162]},\n",
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-       "     'conf': 0.905,\n",
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-       "  {'file': '0000/0828.jpg',\n",
-       "   'max_detection_conf': 0.909,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.4102, 0.4538, 0.09768, 0.1531]}]},\n",
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-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
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-       "  {'file': '0000/0830.jpg',\n",
-       "   'max_detection_conf': 0.904,\n",
-       "   'detections': [{'category': '1',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.3748, 0.356, 0.1196, 0.1826]},\n",
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-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.884,\n",
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-       "   'detections': [{'category': '1',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.376, 0.4981, 0.1672, 0.1346]},\n",
-       "    {'category': '1',\n",
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-       "  {'file': '0000/0835.jpg',\n",
-       "   'max_detection_conf': 0.651,\n",
-       "   'detections': [{'category': '1',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.5115, 0.5073, 0.2246, 0.2029]},\n",
-       "    {'category': '1', 'conf': 0.94, 'bbox': [0.4065, 0.452, 0.1404, 0.1254]}]},\n",
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-       "   'max_detection_conf': 0.909,\n",
-       "   'detections': [{'category': '1',\n",
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-       "    {'category': '1',\n",
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-       "  {'file': '0000/1543.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
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-       "  {'file': '0000/1544.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.3565, 0.5258, 0.1562, 0.1217]}]},\n",
-       "  {'file': '0000/1545.jpg',\n",
-       "   'max_detection_conf': 0.832,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.832,\n",
-       "     'bbox': [0.4774, 0.535, 0.1599, 0.09963]}]},\n",
-       "  {'file': '0000/1546.jpg',\n",
-       "   'max_detection_conf': 0.866,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.866,\n",
-       "     'bbox': [0.3223, 0.4483, 0.1611, 0.1125]}]},\n",
-       "  {'file': '0000/1547.jpg',\n",
-       "   'max_detection_conf': 0.881,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0149,\n",
-       "     'bbox': [0.5262, 0.642, 0.04884, 0.03136]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.5274, 0.642, 0.02319, 0.02767]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.4249, 0.5848, 0.09035, 0.06826]}]},\n",
-       "  {'file': '0000/1548.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
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-       "   'max_detection_conf': 0.924,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.924,\n",
-       "     'bbox': [0.376, 0.4797, 0.3614, 0.2859]}]},\n",
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-       "   'detections': [{'category': '1',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "  {'file': '0000/1553.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.2197, 0.6217, 0.3638, 0.2601]}]},\n",
-       "  {'file': '0000/1554.jpg',\n",
-       "   'max_detection_conf': 0.862,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.862,\n",
-       "     'bbox': [0.3846, 0.4188, 0.1343, 0.1309]}]},\n",
-       "  {'file': '0000/1555.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.5689, 0.4797, 0.02319, 0.02583]},\n",
-       "    {'category': '1',\n",
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-       "  {'file': '0000/1556.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "    {'category': '1',\n",
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-       "  {'file': '0000/1557.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
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-       "  {'file': '0000/1558.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.8107, 0.1549, 0.08547, 0.1568]},\n",
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-       "  {'file': '0000/1559.jpg',\n",
-       "   'max_detection_conf': 0.914,\n",
-       "   'detections': [{'category': '1',\n",
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-       "  {'file': '0000/1560.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.5531, 0.5221, 0.3772, 0.2675]}]},\n",
-       "  {'file': '0000/1561.jpg',\n",
-       "   'max_detection_conf': 0.945,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.945,\n",
-       "     'bbox': [0.5213, 0.4464, 0.1965, 0.142]}]},\n",
-       "  {'file': '0000/1562.jpg',\n",
-       "   'max_detection_conf': 0.958,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.958,\n",
-       "     'bbox': [0.01831, 0.4741, 0.354, 0.4261]}]},\n",
-       "  {'file': '0000/1563.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3455, 0.3837, 0.2808, 0.1992]}]},\n",
-       "  {'file': '0000/1564.jpg',\n",
-       "   'max_detection_conf': 0.94,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.1489, 0.4003, 0.249, 0.2527]}]},\n",
-       "  {'file': '0000/1565.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.2796, 0.5147, 0.2722, 0.2103]}]},\n",
-       "  {'file': '0000/1566.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.2527, 0.1033, 0.387, 0.7306]}]},\n",
-       "  {'file': '0000/1567.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.4505, 0.3265, 0.2735, 0.356]}]},\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.3601, 0.6217, 0.0586, 0.0535]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.94,\n",
-       "     'bbox': [0.3931, 0.6162, 0.1892, 0.1715]}]},\n",
-       "  {'file': '0000/1569.jpg',\n",
-       "   'max_detection_conf': 0.928,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.928,\n",
-       "     'bbox': [0.3894, 0.5202, 0.221, 0.19]}]},\n",
-       "  {'file': '0000/1570.jpg',\n",
-       "   'max_detection_conf': 0.907,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.4688, 0.4963, 0.1123, 0.1512]}]},\n",
-       "  {'file': '0000/1571.jpg',\n",
-       "   'max_detection_conf': 0.982,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.982,\n",
-       "     'bbox': [0.3589, 0.03321, 0.6398, 0.9372]}]},\n",
-       "  {'file': '0000/1572.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0255,\n",
-       "     'bbox': [0, 0.6715, 0.1575, 0.2324]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.4163, 0.5424, 0.249, 0.3265]}]},\n",
-       "  {'file': '0000/1573.jpg',\n",
-       "   'max_detection_conf': 0.907,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.3943, 0.511, 0.1526, 0.4464]}]},\n",
-       "  {'file': '0000/1574.jpg',\n",
-       "   'max_detection_conf': 0.943,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.943,\n",
-       "     'bbox': [0.2039, 0.3892, 0.3589, 0.3985]}]},\n",
-       "  {'file': '0000/1575.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.02075, 0.5719, 0.4175, 0.3948]}]},\n",
-       "  {'file': '0000/1576.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.3601, 0.4095, 0.1978, 0.2029]}]},\n",
-       "  {'file': '0000/1927.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0, 0.3099, 0.4761, 0.5147]}]},\n",
-       "  {'file': '0000/1928.jpg',\n",
-       "   'max_detection_conf': 0.935,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.929,\n",
-       "     'bbox': [0.3333, 0.273, 0.5921, 0.5129]},\n",
-       "    {'category': '1', 'conf': 0.935, 'bbox': [0, 0.3929, 0.1965, 0.2675]}]},\n",
-       "  {'file': '0000/1929.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.7277, 0.356, 0.2442, 0.3431]}]},\n",
-       "  {'file': '0000/1930.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.3137, 0.07195, 0.6678, 0.8929]}]},\n",
-       "  {'file': '0000/1931.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.133, 0.6199, 0.304, 0.1789]}]},\n",
-       "  {'file': '0000/1932.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.954,\n",
-       "     'bbox': [0.3101, 0.5811, 0.2857, 0.3007]}]},\n",
-       "  {'file': '0000/1933.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.3516, 0.4778, 0.1904, 0.1383]}]},\n",
-       "  {'file': '0000/1934.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.06715, 0.2066, 0.4908, 0.4741]}]},\n",
-       "  {'file': '0000/1935.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.6068, 0.559, 0.2967, 0.2121]}]},\n",
-       "  {'file': '0000/1936.jpg',\n",
-       "   'max_detection_conf': 0.947,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.947,\n",
-       "     'bbox': [0.492, 0.5295, 0.08791, 0.1143]}]},\n",
-       "  {'file': '0000/1937.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
-       "     'bbox': [0.3675, 0.4797, 0.2087, 0.1512]}]},\n",
-       "  {'file': '0000/1938.jpg',\n",
-       "   'max_detection_conf': 0.978,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0103,\n",
-       "     'bbox': [0.9157, 0.3837, 0.05006, 0.05166]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.978,\n",
-       "     'bbox': [0.02319, 0.02952, 0.4957, 0.9428]}]},\n",
-       "  {'file': '0000/1939.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.2136, 0.5036, 0.1623, 0.1254]}]},\n",
-       "  {'file': '0000/1940.jpg',\n",
-       "   'max_detection_conf': 0.962,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.11,\n",
-       "     'bbox': [0.6446, 0.4151, 0.0293, 0.06826]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.962,\n",
-       "     'bbox': [0.2832, 0.5202, 0.315, 0.2785]}]},\n",
-       "  {'file': '0000/1941.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0431,\n",
-       "     'bbox': [0.2832, 0.9003, 0.105, 0.07195]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.5152, 0.5442, 0.1416, 0.2084]}]},\n",
-       "  {'file': '0000/1942.jpg',\n",
-       "   'max_detection_conf': 0.863,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.863,\n",
-       "     'bbox': [0.4688, 0.5092, 0.1623, 0.1642]}]},\n",
-       "  {'file': '0000/1943.jpg',\n",
-       "   'max_detection_conf': 0.97,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.97,\n",
-       "     'bbox': [0.481, 0.5701, 0.2234, 0.2564]}]},\n",
-       "  {'file': '0000/1944.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3076, 0.3154, 0.2503, 0.3062]}]},\n",
-       "  {'file': '0000/1945.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2148, 0.3062, 0.3894, 0.3154]}]},\n",
-       "  {'file': '0000/1946.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.3565, 0.3966, 0.2148, 0.1678]}]},\n",
-       "  {'file': '0000/1947.jpg',\n",
-       "   'max_detection_conf': 0.803,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.803,\n",
-       "     'bbox': [0.5006, 0.3597, 0.04517, 0.1439]}]},\n",
-       "  {'file': '0000/1948.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.5164, 0.3671, 0.1807, 0.3007]}]},\n",
-       "  {'file': '0000/1949.jpg',\n",
-       "   'max_detection_conf': 0.949,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.1855, 0.4372, 0.03418, 0.1033]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.5115, 0.4298, 0.2979, 0.2841]}]},\n",
-       "  {'file': '0000/1950.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
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-       "    {'category': '1',\n",
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-       "  {'file': '0000/1951.jpg',\n",
-       "   'max_detection_conf': 0.964,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.964,\n",
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-       "  {'file': '0000/1952.jpg',\n",
-       "   'max_detection_conf': 0.922,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.21, 0.4132, 0.133, 0.2822]},\n",
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-       "     'conf': 0.922,\n",
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-       "  {'file': '0000/1953.jpg',\n",
-       "   'max_detection_conf': 0.982,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.982,\n",
-       "     'bbox': [0.2661, 0.5498, 0.5396, 0.4225]}]},\n",
-       "  {'file': '0000/1954.jpg',\n",
-       "   'max_detection_conf': 0.888,\n",
-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.7716, 0.452, 0.1953, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.888,\n",
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-       "  {'file': '0000/1955.jpg',\n",
-       "   'max_detection_conf': 0.897,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0377,\n",
-       "     'bbox': [0.7631, 0.4501, 0.2222, 0.1642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.897,\n",
-       "     'bbox': [0.3308, 0.5701, 0.1196, 0.1199]}]},\n",
-       "  {'file': '0000/1956.jpg',\n",
-       "   'max_detection_conf': 0.89,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0452,\n",
-       "     'bbox': [0.7716, 0.4464, 0.199, 0.1734]},\n",
-       "    {'category': '1', 'conf': 0.89, 'bbox': [0.5225, 0.5387, 0.1391, 0.107]}]},\n",
-       "  {'file': '0000/1957.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.5763, 0.463, 0.1782, 0.1476]}]},\n",
-       "  {'file': '0000/1958.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.3382, 0.3431, 0.3553, 0.4059]}]},\n",
-       "  {'file': '0000/1959.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.4786, 0.4926, 0.1611, 0.2214]}]},\n",
-       "  {'file': '0000/1960.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.2527, 0.3542, 0.3577, 0.5405]}]},\n",
-       "  {'file': '0000/1961.jpg',\n",
-       "   'max_detection_conf': 0.423,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.423,\n",
-       "     'bbox': [0.4297, 0.5, 0.03296, 0.06273]}]},\n",
-       "  {'file': '0000/1011.jpg',\n",
-       "   'max_detection_conf': 0.915,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.915,\n",
-       "     'bbox': [0, 0.4889, 0.2515, 0.2103]}]},\n",
-       "  {'file': '0000/1012.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.4493, 0.4981, 0.2136, 0.2029]}]},\n",
-       "  {'file': '0000/1013.jpg',\n",
-       "   'max_detection_conf': 0.979,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.979,\n",
-       "     'bbox': [0.3601, 0.2859, 0.21, 0.6051]}]},\n",
-       "  {'file': '0000/1014.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.62,\n",
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-       "  {'file': '0000/1015.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.4334, 0.6033, 0.09768, 0.19]}]},\n",
-       "  {'file': '0000/1016.jpg',\n",
-       "   'max_detection_conf': 0.901,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.901,\n",
-       "     'bbox': [0.3846, 0.4372, 0.1013, 0.1014]}]},\n",
-       "  {'file': '0000/1017.jpg',\n",
-       "   'max_detection_conf': 0.935,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.935,\n",
-       "     'bbox': [0.2429, 0.356, 0.249, 0.2527]}]},\n",
-       "  {'file': '0000/1018.jpg',\n",
-       "   'max_detection_conf': 0.684,\n",
-       "   'detections': [{'category': '1',\n",
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-       "    {'category': '1',\n",
-       "     'conf': 0.684,\n",
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-       "  {'file': '0000/1019.jpg',\n",
-       "   'max_detection_conf': 0.953,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.953,\n",
-       "     'bbox': [0.1953, 0.3173, 0.2783, 0.2619]}]},\n",
-       "  {'file': '0000/1020.jpg',\n",
-       "   'max_detection_conf': 0.955,\n",
-       "   'detections': [{'category': '1',\n",
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-       "  {'file': '0000/1021.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.954,\n",
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-       "  {'file': '0000/1022.jpg',\n",
-       "   'max_detection_conf': 0.954,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.954,\n",
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-       "  {'file': '0000/1023.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.2661, 0.5239, 0.3492, 0.1107]},\n",
-       "    {'category': '1',\n",
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-       "  {'file': '0000/1024.jpg',\n",
-       "   'max_detection_conf': 0.891,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.891,\n",
-       "     'bbox': [0.4114, 0.4538, 0.09768, 0.1162]}]},\n",
-       "  {'file': '0000/1025.jpg',\n",
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-       "   'detections': [{'category': '1',\n",
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-       "     'bbox': [0.2271, 0.3837, 0.1343, 0.4852]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.737,\n",
-       "     'bbox': [0.2307, 0.3874, 0.1233, 0.05719]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.972,\n",
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-       "  {'file': '0000/1026.jpg',\n",
-       "   'max_detection_conf': 0.946,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0111,\n",
-       "     'bbox': [0.01098, 0.5147, 0.06593, 0.1346]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0113,\n",
-       "     'bbox': [0.8644, 0.3487, 0.09523, 0.1715]},\n",
-       "    {'category': '1',\n",
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-       "     'bbox': [0.02075, 0.5092, 0.2026, 0.3597]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0184,\n",
-       "     'bbox': [0.481, 0.2988, 0.08547, 0.08118]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0256,\n",
-       "     'bbox': [0.5384, 0.3007, 0.02808, 0.07749]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0437,\n",
-       "     'bbox': [0.01343, 0.5498, 0.3528, 0.4261]},\n",
-       "    {'category': '1', 'conf': 0.131, 'bbox': [0.746, 0.356, 0.06959, 0.1734]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.178,\n",
-       "     'bbox': [0.8986, 0.3542, 0.04517, 0.09225]},\n",
-       "    {'category': '1', 'conf': 0.194, 'bbox': [0.746, 0.3597, 0.04884, 0.1051]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.473,\n",
-       "     'bbox': [0.8327, 0.3339, 0.07203, 0.1273]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.543,\n",
-       "     'bbox': [0.9169, 0.3505, 0.05982, 0.1715]},\n",
-       "    {'category': '1', 'conf': 0.859, 'bbox': [0.7509, 0.3321, 0.1526, 0.2324]},\n",
-       "    {'category': '1', 'conf': 0.906, 'bbox': [0.5518, 0.3671, 0.2515, 0.2859]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.08424, 0.4797, 0.3272, 0.3653]},\n",
-       "    {'category': '1', 'conf': 0.945, 'bbox': [0.4652, 0.5092, 0.1318, 0.4169]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.946,\n",
-       "     'bbox': [0.5995, 0.5184, 0.2759, 0.4372]}]},\n",
-       "  {'file': '0000/1027.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.2063, 0.4003, 0.3199, 0.5738]}]},\n",
-       "  {'file': '0000/1028.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0469,\n",
-       "     'bbox': [0.1461, 0.3054, 0.0229, 0.08114]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.908,\n",
-       "     'bbox': [0.4873, 0.4832, 0.08925, 0.3448]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3222, 0.4701, 0.1635, 0.3723]}]},\n",
-       "  {'file': '0000/1029.jpg',\n",
-       "   'max_detection_conf': 0.924,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.924,\n",
-       "     'bbox': [0.5039, 0.4999, 0.1011, 0.1336]}]},\n",
-       "  {'file': '0000/1030.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0169,\n",
-       "     'bbox': [0.109, 0.3019, 0.0229, 0.07995]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0278,\n",
-       "     'bbox': [0.4415, 0.4892, 0.03317, 0.05608]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.4375, 0.5429, 0.2432, 0.4272]}]},\n",
-       "  {'file': '0000/1031.jpg',\n",
-       "   'max_detection_conf': 0.918,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.465,\n",
-       "     'bbox': [0.2614, 0.5047, 0.03949, 0.04176]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.877,\n",
-       "     'bbox': [0.4581, 0.5083, 0.09478, 0.1062]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.918,\n",
-       "     'bbox': [0.2575, 0.5429, 0.1706, 0.1217]}]},\n",
-       "  {'file': '0000/1032.jpg',\n",
-       "   'max_detection_conf': 0.873,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.873,\n",
-       "     'bbox': [0.5015, 0.3914, 0.2077, 0.1837]}]},\n",
-       "  {'file': '0000/1033.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.417, 0.4164, 0.2282, 0.1909]}]},\n",
-       "  {'file': '0000/1034.jpg',\n",
-       "   'max_detection_conf': 0.775,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0455,\n",
-       "     'bbox': [0.436, 0.519, 0.1516, 0.1002]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.775,\n",
-       "     'bbox': [0.4328, 0.5202, 0.07345, 0.1002]}]},\n",
-       "  {'file': '0000/1035.jpg',\n",
-       "   'max_detection_conf': 0.938,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.938,\n",
-       "     'bbox': [0.4344, 0.5178, 0.1406, 0.1455]}]},\n",
-       "  {'file': '0000/1036.jpg',\n",
-       "   'max_detection_conf': 0.907,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.907,\n",
-       "     'bbox': [0.4684, 0.5417, 0.06161, 0.09665]}]},\n",
-       "  {'file': '0000/1037.jpg',\n",
-       "   'max_detection_conf': 0.893,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.893,\n",
-       "     'bbox': [0.5276, 0.4988, 0.1058, 0.07279]}]},\n",
-       "  {'file': '0000/1038.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.387, 0.4164, 0.323, 0.2147]}]},\n",
-       "  {'file': '0000/1039.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.615,\n",
-       "     'bbox': [0.2006, 0.4988, 0.04423, 0.09904]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.936,\n",
-       "     'bbox': [0.2796, 0.5536, 0.09004, 0.1801]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.3736, 0.5107, 0.327, 0.4606]}]},\n",
-       "  {'file': '0000/1040.jpg',\n",
-       "   'max_detection_conf': 0.939,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0199,\n",
-       "     'bbox': [0.1113, 0.309, 0.02369, 0.07279]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.939,\n",
-       "     'bbox': [0.3783, 0.4379, 0.08293, 0.1539]}]},\n",
-       "  {'file': '0000/1041.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0229,\n",
-       "     'bbox': [0.1121, 0.309, 0.02448, 0.08472]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.3933, 0.4331, 0.2827, 0.3257]}]},\n",
-       "  {'file': '0000/1042.jpg',\n",
-       "   'max_detection_conf': 0.944,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.944,\n",
-       "     'bbox': [0.3052, 0.02952, 0.6593, 0.6992]}]},\n",
-       "  {'file': '0000/1043.jpg',\n",
-       "   'max_detection_conf': 0.976,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.976,\n",
-       "     'bbox': [0.5884, 0.5489, 0.2409, 0.4164]}]},\n",
-       "  {'file': '0000/1044.jpg',\n",
-       "   'max_detection_conf': 0.918,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.918,\n",
-       "     'bbox': [0.009768, 0.607, 0.1452, 0.1383]}]},\n",
-       "  {'file': '0000/1045.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.1855, 0.404, 0.3296, 0.3173]}]},\n",
-       "  {'file': '0000/1046.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.459, 0.3173, 0.5177, 0.6549]}]},\n",
-       "  {'file': '0000/1285.jpg',\n",
-       "   'max_detection_conf': 0.98,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.98,\n",
-       "     'bbox': [0.221, 0.4391, 0.3943, 0.5313]}]},\n",
-       "  {'file': '0000/1286.jpg',\n",
-       "   'max_detection_conf': 0.746,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.746,\n",
-       "     'bbox': [0.2844, 0.5147, 0.2442, 0.1863]}]},\n",
-       "  {'file': '0000/1287.jpg',\n",
-       "   'max_detection_conf': 0.561,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0169,\n",
-       "     'bbox': [0.1868, 0.511, 0.2625, 0.1992]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.561,\n",
-       "     'bbox': [0.2625, 0.511, 0.2148, 0.1346]}]},\n",
-       "  {'file': '0000/1288.jpg',\n",
-       "   'max_detection_conf': 0.947,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.947,\n",
-       "     'bbox': [0.06593, 0.5608, 0.2197, 0.4114]}]},\n",
-       "  {'file': '0000/1289.jpg',\n",
-       "   'max_detection_conf': 0.977,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.977,\n",
-       "     'bbox': [0.221, 0.4686, 0.3956, 0.5018]}]},\n",
-       "  {'file': '0000/1290.jpg',\n",
-       "   'max_detection_conf': 0.973,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.973,\n",
-       "     'bbox': [0.282, 0.5276, 0.2686, 0.3173]}]},\n",
-       "  {'file': '0000/1291.jpg',\n",
-       "   'max_detection_conf': 0.975,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.975,\n",
-       "     'bbox': [0.1282, 0.4557, 0.4041, 0.5073]}]},\n",
-       "  {'file': '0000/1292.jpg',\n",
-       "   'max_detection_conf': 0.967,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.967,\n",
-       "     'bbox': [0.1623, 0.5036, 0.3113, 0.3505]}]},\n",
-       "  {'file': '0000/1293.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.1025, 0.3025, 0.735, 0.6678]}]},\n",
-       "  {'file': '0000/1294.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0102,\n",
-       "     'bbox': [0.1868, 0.452, 0.04273, 0.0369]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0107,\n",
-       "     'bbox': [0.007326, 0.369, 0.1111, 0.1236]},\n",
-       "    {'category': '1', 'conf': 0.012, 'bbox': [0.0757, 0.487, 0.07814, 0.1217]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0173,\n",
-       "     'bbox': [0.2124, 0.5092, 0.09157, 0.1033]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.025,\n",
-       "     'bbox': [0.2661, 0.5202, 0.04029, 0.05904]},\n",
-       "    {'category': '1', 'conf': 0.254, 'bbox': [0.1794, 0.463, 0.1208, 0.1125]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.335,\n",
-       "     'bbox': [0.2368, 0.5092, 0.06593, 0.06457]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.637,\n",
-       "     'bbox': [0.2539, 0.4557, 0.08791, 0.1033]},\n",
-       "    {'category': '1', 'conf': 0.795, 'bbox': [0.1416, 0.4723, 0.1355, 0.1642]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.315, 0.4594, 0.2295, 0.2619]}]},\n",
-       "  {'file': '0000/1295.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3162, 0.4704, 0.1916, 0.2324]}]},\n",
-       "  {'file': '0000/1296.jpg',\n",
-       "   'max_detection_conf': 0.894,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.894,\n",
-       "     'bbox': [0.5164, 0.5479, 0.05982, 0.1254]}]},\n",
-       "  {'file': '0000/1297.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.3125, 0.5276, 0.1538, 0.2047]}]},\n",
-       "  {'file': '0000/1298.jpg',\n",
-       "   'max_detection_conf': 0.969,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.969,\n",
-       "     'bbox': [0.2039, 0.487, 0.2466, 0.2103]}]},\n",
-       "  {'file': '0000/1299.jpg',\n",
-       "   'max_detection_conf': 0.96,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.96,\n",
-       "     'bbox': [0.3455, 0.5369, 0.144, 0.3782]}]},\n",
-       "  {'file': '0000/1300.jpg',\n",
-       "   'max_detection_conf': 0.968,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.968,\n",
-       "     'bbox': [0.1684, 0.4889, 0.3638, 0.3856]}]},\n",
-       "  {'file': '0000/1301.jpg',\n",
-       "   'max_detection_conf': 0.948,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.948,\n",
-       "     'bbox': [0.1929, 0.08856, 0.5006, 0.8819]}]},\n",
-       "  {'file': '0000/1302.jpg',\n",
-       "   'max_detection_conf': 0.963,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.008547, 0.4557, 0.4969, 0.4354]}]},\n",
-       "  {'file': '0000/1303.jpg',\n",
-       "   'max_detection_conf': 0.919,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.919,\n",
-       "     'bbox': [0.6593, 0.6623, 0.3199, 0.3007]}]},\n",
-       "  {'file': '0000/1304.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.2967, 0.4095, 0.4285, 0.5608]}]},\n",
-       "  {'file': '0000/1305.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.3724, 0.4889, 0.2783, 0.3154]}]},\n",
-       "  {'file': '0000/1306.jpg',\n",
-       "   'max_detection_conf': 0.838,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.838,\n",
-       "     'bbox': [0.4542, 0.5166, 0.04639, 0.1088]}]},\n",
-       "  {'file': '0000/1307.jpg',\n",
-       "   'max_detection_conf': 0.935,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.935,\n",
-       "     'bbox': [0.4371, 0.4944, 0.1746, 0.1881]}]},\n",
-       "  {'file': '0000/1308.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.07081, 0.4243, 0.3626, 0.3653]}]},\n",
-       "  {'file': '0000/1309.jpg',\n",
-       "   'max_detection_conf': 0.742,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.742,\n",
-       "     'bbox': [0.4468, 0.4077, 0.4859, 0.3929]}]},\n",
-       "  {'file': '0000/1310.jpg',\n",
-       "   'max_detection_conf': 0.965,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.965,\n",
-       "     'bbox': [0.3992, 0.6642, 0.2295, 0.2121]}]},\n",
-       "  {'file': '0000/1311.jpg',\n",
-       "   'max_detection_conf': 0.974,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.974,\n",
-       "     'bbox': [0.5079, 0.3929, 0.2222, 0.511]}]},\n",
-       "  {'file': '0000/1312.jpg',\n",
-       "   'max_detection_conf': 0.826,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.826,\n",
-       "     'bbox': [0.3553, 0.3265, 0.5958, 0.5682]}]},\n",
-       "  {'file': '0000/1313.jpg',\n",
-       "   'max_detection_conf': 0.961,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.961,\n",
-       "     'bbox': [0.3992, 0.4446, 0.3394, 0.404]}]},\n",
-       "  {'file': '0000/1314.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0123,\n",
-       "     'bbox': [0.3846, 0.6217, 0.2283, 0.1918]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0387,\n",
-       "     'bbox': [0.5616, 0.5258, 0.1831, 0.1955]},\n",
-       "    {'category': '1', 'conf': 0.67, 'bbox': [0.5775, 0.5221, 0.07936, 0.1937]},\n",
-       "    {'category': '1', 'conf': 0.73, 'bbox': [0.3614, 0.6512, 0.1526, 0.1568]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.4017, 0.5516, 0.3431, 0.2546]}]},\n",
-       "  {'file': '0000/1315.jpg',\n",
-       "   'max_detection_conf': 0.661,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.661,\n",
-       "     'bbox': [0.4969, 0.2029, 0.2576, 0.7214]}]},\n",
-       "  {'file': '0000/1316.jpg',\n",
-       "   'max_detection_conf': 0.951,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.013,\n",
-       "     'bbox': [0.9365, 0.5295, 0.06349, 0.2693]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.0148,\n",
-       "     'bbox': [0.6422, 0.4907, 0.02319, 0.09225]},\n",
-       "    {'category': '1', 'conf': 0.85, 'bbox': [0.4932, 0.4575, 0.07692, 0.1402]},\n",
-       "    {'category': '1', 'conf': 0.923, 'bbox': [0.1978, 0.5036, 0.1611, 0.1697]},\n",
-       "    {'category': '1', 'conf': 0.939, 'bbox': [0.735, 0.4483, 0.1538, 0.3228]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.951,\n",
-       "     'bbox': [0.3846, 0.4335, 0.1709, 0.3616]}]},\n",
-       "  {'file': '0000/1317.jpg',\n",
-       "   'max_detection_conf': 0.966,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.0761,\n",
-       "     'bbox': [0.547, 0.5904, 0.05128, 0.09594]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.966,\n",
-       "     'bbox': [0.4371, 0.5848, 0.1709, 0.1881]}]},\n",
-       "  {'file': '0000/1318.jpg',\n",
-       "   'max_detection_conf': 0.957,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.957,\n",
-       "     'bbox': [0.2332, 0.3357, 0.7399, 0.6346]}]},\n",
-       "  {'file': '0000/1319.jpg',\n",
-       "   'max_detection_conf': 0.952,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.952,\n",
-       "     'bbox': [0.5531, 0.5369, 0.1306, 0.118]}]},\n",
-       "  {'file': '0000/1697.jpg',\n",
-       "   'max_detection_conf': 0.971,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.012,\n",
-       "     'bbox': [0.3076, 0.3247, 0.0586, 0.2084]},\n",
-       "    {'category': '1', 'conf': 0.944, 'bbox': [0.2136, 0.5258, 0.0818, 0.1531]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.971,\n",
-       "     'bbox': [0.2612, 0.5885, 0.3394, 0.2822]}]},\n",
-       "  {'file': '0000/1698.jpg',\n",
-       "   'max_detection_conf': 0.972,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.963,\n",
-       "     'bbox': [0.6507, 0.6586, 0.3272, 0.3136]},\n",
-       "    {'category': '1',\n",
-       "     'conf': 0.972,\n",
-       "     'bbox': [0.2319, 0.511, 0.3846, 0.4557]}]},\n",
-       "  {'file': '0000/1699.jpg',\n",
-       "   'max_detection_conf': 0.959,\n",
-       "   'detections': [{'category': '1',\n",
-       "     'conf': 0.959,\n",
-       "     'bbox': [0.42, 0.6549, 0.326, 0.2822]}]},\n",
-       "  ...],\n",
-       " 'detection_categories': {'1': 'animal', '2': 'person', '3': 'vehicle'},\n",
-       " 'info': {'detection_completion_time': '2022-10-05 18:01:31',\n",
-       "  'format_version': '1.2',\n",
-       "  'detector': 'md_v5a.0.0.pt',\n",
-       "  'detector_metadata': {'megadetector_version': 'v5a.0.0',\n",
-       "   'typical_detection_threshold': 0.2,\n",
-       "   'conservative_detection_threshold': 0.05}}}"
+       "dataset_name              20\n",
+       "url_gcp             10104328\n",
+       "url_aws             10104328\n",
+       "url_azure           10104328\n",
+       "image_id            10104328\n",
+       "sequence_id          1236468\n",
+       "location_id             9789\n",
+       "frame_num               6071\n",
+       "original_label          1201\n",
+       "scientific_name          908\n",
+       "common_name              999\n",
+       "datetime             4850339\n",
+       "annotation_level           3\n",
+       "kingdom                    1\n",
+       "phylum                     2\n",
+       "subphylum                  5\n",
+       "superclass                 1\n",
+       "class                      8\n",
+       "subclass                   3\n",
+       "infraclass                 2\n",
+       "superorder                 5\n",
+       "order                     58\n",
+       "suborder                  17\n",
+       "infraorder                 9\n",
+       "superfamily               12\n",
+       "family                   187\n",
+       "subfamily                 71\n",
+       "tribe                     46\n",
+       "genus                    538\n",
+       "species                  739\n",
+       "subspecies                12\n",
+       "variety                    1\n",
+       "multi_species              2\n",
+       "num_species                4\n",
+       "lin_duplicate              2\n",
+       "full_duplicate             2\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data"
+    "df_clean.nunique()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Looks like formatting is consistent"
+    "Let's check out our top ten labels, scientific names, and common names. Then we'll save this cleaned metadata file."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wcs_mdv5a = pd.json_normalize(data[\"images\"], max_level = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>max_detection_conf</th>\n",
-       "      <th>detections</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0000/0239.jpg</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.97, 'bbox': [0.42...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0000/0240.jpg</td>\n",
-       "      <td>0.961</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0102, 'bbox': [0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0000/0241.jpg</td>\n",
-       "      <td>0.968</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0229, 'bbox': [0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0000/0242.jpg</td>\n",
-       "      <td>0.847</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.017, 'bbox': [0.5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0000/0243.jpg</td>\n",
-       "      <td>0.779</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0119, 'bbox': [0....</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "            file  max_detection_conf   \n",
-       "0  0000/0239.jpg               0.970  \\\n",
-       "1  0000/0240.jpg               0.961   \n",
-       "2  0000/0241.jpg               0.968   \n",
-       "3  0000/0242.jpg               0.847   \n",
-       "4  0000/0243.jpg               0.779   \n",
-       "\n",
-       "                                          detections  \n",
-       "0  [{'category': '1', 'conf': 0.97, 'bbox': [0.42...  \n",
-       "1  [{'category': '1', 'conf': 0.0102, 'bbox': [0....  \n",
-       "2  [{'category': '1', 'conf': 0.0229, 'bbox': [0....  \n",
-       "3  [{'category': '1', 'conf': 0.017, 'bbox': [0.5...  \n",
-       "4  [{'category': '1', 'conf': 0.0119, 'bbox': [0....  "
+       "original_label\n",
+       "bos taurus           2041202\n",
+       "mouse                1229642\n",
+       "wildebeest            534713\n",
+       "zebra                 354233\n",
+       "gazellethomsons       323932\n",
+       "deer                  263368\n",
+       "human                 257159\n",
+       "eurasian_wild_pig     234736\n",
+       "cervus elaphus        183794\n",
+       "bird                  172834\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "wcs_mdv5a.head()"
+    "df_clean[\"original_label\"].value_counts()[:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0001.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0038.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0039.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0040.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0041.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0042.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0043.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0044.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0045.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0046.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0047.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0066.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0068.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0072.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0073.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0074.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0075.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0076.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0077.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0078.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0079.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0081.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0103.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0104.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0105.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0106.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0107.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0108.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0109.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0110.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0111.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0112.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0113.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0114.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0115.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0116.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0117.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0118.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0119.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0120.jpg',\n",
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-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0923.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0924.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0925.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0926.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0927.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0928.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0929.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0930.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0931.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0932.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0933.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0934.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0935.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0936.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0937.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0938.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0939.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0940.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0941.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0942.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0943.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0944.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0945.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0946.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0947.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0948.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0949.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0950.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0951.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0952.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0953.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0954.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0955.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0956.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0957.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0958.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0959.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0960.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0961.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0962.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0963.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0964.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0965.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0966.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0967.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0969.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0971.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0972.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0973.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0974.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0975.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0976.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0977.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0978.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0979.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0980.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0981.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0982.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0983.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0984.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0985.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0986.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0987.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0988.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0989.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0990.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0991.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0992.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0993.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0994.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0995.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0996.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0997.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/0999.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1000.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1001.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1002.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1003.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1004.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1005.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1007.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1008.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1009.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1010.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1011.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1012.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1013.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1014.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1015.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1016.jpg',\n",
-       " 'https://lilablobssc.blob.core.windows.net/wcs-unzipped/animals/0000/1017.jpg',\n",
-       " ...]"
+       "scientific_name\n",
+       "bos taurus               2088019\n",
+       "mus                      1229642\n",
+       "connochaetes taurinus     534813\n",
+       "sus scrofa                389702\n",
+       "equus quagga              374570\n",
+       "aves                      332757\n",
+       "eudorcas thomsonii        324105\n",
+       "odocoileus                311993\n",
+       "homo sapiens              257159\n",
+       "cervus elaphus            186592\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "list(dedupe_species.loc[dedupe_species['dataset_name'] == \"WCS Camera Traps\", 'url'])"
+    "df_clean[\"scientific_name\"].value_counts()[:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[]"
+       "common_name\n",
+       "domestic cow         2044000\n",
+       "mouse                1229654\n",
+       "blue wildebeest       533564\n",
+       "plains zebra          374570\n",
+       "deer                  360489\n",
+       "thomson's gazelle     324105\n",
+       "bird                  264967\n",
+       "human                 257159\n",
+       "eurasian wild pig     234736\n",
+       "red deer              186592\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dedupe_url_wcs = list(dedupe_species.loc[dedupe_species['dataset_name'] == \"WCS Camera Traps\", 'url'])\n",
-    "dedupe_wcs_empties = []\n",
-    "for file in list(wcs_mdv5a.loc[wcs_mdv5a['detections'].astype(str) == '[]', 'file']):\n",
-    "    if file in dedupe_url_wcs:\n",
-    "        dedupe_wcs_empties.append(file)\n",
-    "\n",
-    "dedupe_wcs_empties"
+    "df_clean[\"common_name\"].value_counts()[:10]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "MegaDetector v5b is supposed to have less false positives than v5a, so let's check that and see if we're still in the clear."
+    "There are also 257,159 humans in here! Glad the number agrees across labels. We'll probably need to remove the humans, though I may save a copy with them still on the HF repo (it is just our dev repo). Which datasets have them? I thought humans were filtered out previously (though I could be mistaken as they seem to be in 15 of the 20 datasets)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset_name\n",
+       "WCS Camera Traps                              162501\n",
+       "Snapshot Serengeti                             47015\n",
+       "Idaho Camera Traps                             22195\n",
+       "Orinoquia Camera Traps                          7441\n",
+       "Channel Islands Camera Traps                    5071\n",
+       "Island Conservation Camera Traps                4808\n",
+       "SWG Camera Traps                                3808\n",
+       "ENA24                                            887\n",
+       "Snapshot Enonkishu                               870\n",
+       "Trail Camera Images of New Zealand Animals       817\n",
+       "Snapshot Mountain Zebra                          536\n",
+       "Snapshot Kruger                                  532\n",
+       "Snapshot Camdeboo                                324\n",
+       "Snapshot Karoo                                   219\n",
+       "Snapshot Kgalagadi                               135\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_clean.loc[df_clean[\"original_label\"] == \"human\", \"dataset_name\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "with open(\"../MegaDetector_results/wcs-camera-traps_animals_mdv5b.0.0_results.json\") as file:\n",
-    "    data = json.load(file)"
+    "What do human labels look like (as in do they have the full taxa structure)?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -20487,117 +3151,259 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>max_detection_conf</th>\n",
-       "      <th>detections</th>\n",
+       "      <th>dataset_name</th>\n",
+       "      <th>url_gcp</th>\n",
+       "      <th>url_aws</th>\n",
+       "      <th>url_azure</th>\n",
+       "      <th>image_id</th>\n",
+       "      <th>sequence_id</th>\n",
+       "      <th>location_id</th>\n",
+       "      <th>frame_num</th>\n",
+       "      <th>original_label</th>\n",
+       "      <th>scientific_name</th>\n",
+       "      <th>...</th>\n",
+       "      <th>subfamily</th>\n",
+       "      <th>tribe</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>variety</th>\n",
+       "      <th>multi_species</th>\n",
+       "      <th>num_species</th>\n",
+       "      <th>lin_duplicate</th>\n",
+       "      <th>full_duplicate</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0000/0239.jpg</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.974, 'bbox': [0.4...</td>\n",
+       "      <th>4451993</th>\n",
+       "      <td>WCS Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>WCS Camera Traps : 5768343a-92d5-11e9-8890-000...</td>\n",
+       "      <td>WCS Camera Traps : unknown</td>\n",
+       "      <td>WCS Camera Traps : 2812</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>human</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>...</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0000/0240.jpg</td>\n",
-       "      <td>0.968</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0535, 'bbox': [0....</td>\n",
+       "      <th>4995616</th>\n",
+       "      <td>WCS Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>WCS Camera Traps : bc7c7b89-92d5-11e9-b600-000...</td>\n",
+       "      <td>WCS Camera Traps : unknown</td>\n",
+       "      <td>WCS Camera Traps : 4607</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>human</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>...</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0000/0241.jpg</td>\n",
-       "      <td>0.966</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0637, 'bbox': [0....</td>\n",
+       "      <th>4916766</th>\n",
+       "      <td>WCS Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>WCS Camera Traps : adc8775e-92d5-11e9-afee-000...</td>\n",
+       "      <td>WCS Camera Traps : unknown</td>\n",
+       "      <td>WCS Camera Traps : 4446</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>human</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>...</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0000/0242.jpg</td>\n",
-       "      <td>0.745</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0244, 'bbox': [0....</td>\n",
+       "      <th>5729969</th>\n",
+       "      <td>Idaho Camera Traps</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Idaho Camera Traps : loc_0004_im_000009</td>\n",
+       "      <td>Idaho Camera Traps : loc_0004_seq_000000</td>\n",
+       "      <td>Idaho Camera Traps : 4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>human</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>...</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0000/0243.jpg</td>\n",
-       "      <td>0.797</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0174, 'bbox': [0....</td>\n",
+       "      <th>10798486</th>\n",
+       "      <td>Snapshot Serengeti</td>\n",
+       "      <td>https://storage.googleapis.com/public-datasets...</td>\n",
+       "      <td>http://us-west-2.opendata.source.coop.s3.amazo...</td>\n",
+       "      <td>https://lilawildlife.blob.core.windows.net/lil...</td>\n",
+       "      <td>Snapshot Serengeti : S7/H07/H07_R1/S7_H07_R1_I...</td>\n",
+       "      <td>Snapshot Serengeti : SER_S7#H07#1#1523</td>\n",
+       "      <td>Snapshot Serengeti : H07</td>\n",
+       "      <td>1</td>\n",
+       "      <td>human</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>...</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 36 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            file  max_detection_conf   \n",
-       "0  0000/0239.jpg               0.974  \\\n",
-       "1  0000/0240.jpg               0.968   \n",
-       "2  0000/0241.jpg               0.966   \n",
-       "3  0000/0242.jpg               0.745   \n",
-       "4  0000/0243.jpg               0.797   \n",
-       "\n",
-       "                                          detections  \n",
-       "0  [{'category': '1', 'conf': 0.974, 'bbox': [0.4...  \n",
-       "1  [{'category': '1', 'conf': 0.0535, 'bbox': [0....  \n",
-       "2  [{'category': '1', 'conf': 0.0637, 'bbox': [0....  \n",
-       "3  [{'category': '1', 'conf': 0.0244, 'bbox': [0....  \n",
-       "4  [{'category': '1', 'conf': 0.0174, 'bbox': [0....  "
+       "                dataset_name  \\\n",
+       "4451993     WCS Camera Traps   \n",
+       "4995616     WCS Camera Traps   \n",
+       "4916766     WCS Camera Traps   \n",
+       "5729969   Idaho Camera Traps   \n",
+       "10798486  Snapshot Serengeti   \n",
+       "\n",
+       "                                                    url_gcp  \\\n",
+       "4451993   https://storage.googleapis.com/public-datasets...   \n",
+       "4995616   https://storage.googleapis.com/public-datasets...   \n",
+       "4916766   https://storage.googleapis.com/public-datasets...   \n",
+       "5729969   https://storage.googleapis.com/public-datasets...   \n",
+       "10798486  https://storage.googleapis.com/public-datasets...   \n",
+       "\n",
+       "                                                    url_aws  \\\n",
+       "4451993   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "4995616   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "4916766   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "5729969   http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "10798486  http://us-west-2.opendata.source.coop.s3.amazo...   \n",
+       "\n",
+       "                                                  url_azure  \\\n",
+       "4451993   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "4995616   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "4916766   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "5729969   https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "10798486  https://lilawildlife.blob.core.windows.net/lil...   \n",
+       "\n",
+       "                                                   image_id  \\\n",
+       "4451993   WCS Camera Traps : 5768343a-92d5-11e9-8890-000...   \n",
+       "4995616   WCS Camera Traps : bc7c7b89-92d5-11e9-b600-000...   \n",
+       "4916766   WCS Camera Traps : adc8775e-92d5-11e9-afee-000...   \n",
+       "5729969             Idaho Camera Traps : loc_0004_im_000009   \n",
+       "10798486  Snapshot Serengeti : S7/H07/H07_R1/S7_H07_R1_I...   \n",
+       "\n",
+       "                                       sequence_id               location_id  \\\n",
+       "4451993                 WCS Camera Traps : unknown   WCS Camera Traps : 2812   \n",
+       "4995616                 WCS Camera Traps : unknown   WCS Camera Traps : 4607   \n",
+       "4916766                 WCS Camera Traps : unknown   WCS Camera Traps : 4446   \n",
+       "5729969   Idaho Camera Traps : loc_0004_seq_000000    Idaho Camera Traps : 4   \n",
+       "10798486    Snapshot Serengeti : SER_S7#H07#1#1523  Snapshot Serengeti : H07   \n",
+       "\n",
+       "          frame_num original_label scientific_name  ...  subfamily tribe  \\\n",
+       "4451993          -1          human    homo sapiens  ...  homininae   NaN   \n",
+       "4995616          -1          human    homo sapiens  ...  homininae   NaN   \n",
+       "4916766          -1          human    homo sapiens  ...  homininae   NaN   \n",
+       "5729969           9          human    homo sapiens  ...  homininae   NaN   \n",
+       "10798486          1          human    homo sapiens  ...  homininae   NaN   \n",
+       "\n",
+       "         genus       species subspecies variety multi_species num_species  \\\n",
+       "4451993   homo  homo sapiens        NaN     NaN         False         1.0   \n",
+       "4995616   homo  homo sapiens        NaN     NaN         False         1.0   \n",
+       "4916766   homo  homo sapiens        NaN     NaN         False         1.0   \n",
+       "5729969   homo  homo sapiens        NaN     NaN         False         1.0   \n",
+       "10798486  homo  homo sapiens        NaN     NaN         False         1.0   \n",
+       "\n",
+       "         lin_duplicate full_duplicate  \n",
+       "4451993           True           True  \n",
+       "4995616           True           True  \n",
+       "4916766           True           True  \n",
+       "5729969           True           True  \n",
+       "10798486          True           True  \n",
+       "\n",
+       "[5 rows x 36 columns]"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "wcs_mdv5b = pd.json_normalize(data[\"images\"], max_level = 1)\n",
-    "wcs_mdv5b.head()"
+    "df_clean.loc[df_clean[\"original_label\"] == \"human\"].sample(5)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 105,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "dedupe_wcs_empties = []\n",
-    "for file in list(wcs_mdv5b.loc[wcs_mdv5b['detections'].astype(str) == '[]', 'file']):\n",
-    "    if file in dedupe_url_wcs:\n",
-    "        dedupe_wcs_empties.append(file)\n",
-    "\n",
-    "dedupe_wcs_empties"
+    "It does seem to have full taxa...interesting."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 58,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "#### NACTI"
+    "df_clean.to_csv(\"../data/lila_image_urls_and_labels_wHumans.csv\", index = False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"../MegaDetector_results/nacti_mdv5b.0.0_results.json\") as file:\n",
-    "    data = json.load(file)"
+    "df_clean.loc[df_clean[\"original_label\"] != \"human\"].to_csv(\"../data/lila_image_urls_and_labels.csv\", index = False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -20621,630 +3427,469 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>file</th>\n",
-       "      <th>max_detection_conf</th>\n",
-       "      <th>detections</th>\n",
+       "      <th>original_label</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>subphylum</th>\n",
+       "      <th>superclass</th>\n",
+       "      <th>class</th>\n",
+       "      <th>subclass</th>\n",
+       "      <th>infraclass</th>\n",
+       "      <th>superorder</th>\n",
+       "      <th>order</th>\n",
+       "      <th>suborder</th>\n",
+       "      <th>infraorder</th>\n",
+       "      <th>superfamily</th>\n",
+       "      <th>family</th>\n",
+       "      <th>subfamily</th>\n",
+       "      <th>tribe</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>variety</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>part0/sub020/2015_Unit058_Ivan042_img0265.jpg</td>\n",
-       "      <td>0.964</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.964, 'bbox': [0.5...</td>\n",
+       "      <th>4913880</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>part0/sub020/2015_Unit058_Ivan042_img0266.jpg</td>\n",
-       "      <td>0.977</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.977, 'bbox': [0.5...</td>\n",
+       "      <th>4799380</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>part0/sub020/2015_Unit058_Ivan042_img0267.jpg</td>\n",
-       "      <td>0.966</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.0102, 'bbox': [0....</td>\n",
+       "      <th>4994742</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>part0/sub020/2015_Unit058_Ivan042_img0268.jpg</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.974, 'bbox': [0.6...</td>\n",
+       "      <th>4879871</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>part0/sub020/2015_Unit058_Ivan042_img0269.jpg</td>\n",
-       "      <td>0.941</td>\n",
-       "      <td>[{'category': '1', 'conf': 0.941, 'bbox': [0.8...</td>\n",
+       "      <th>7201111</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4419687</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3869956</th>\n",
+       "      <td>human</td>\n",
+       "      <td>animalia</td>\n",
+       "      <td>chordata</td>\n",
+       "      <td>vertebrata</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>mammalia</td>\n",
+       "      <td>theria</td>\n",
+       "      <td>placentalia</td>\n",
+       "      <td>euarchontoglires</td>\n",
+       "      <td>primates</td>\n",
+       "      <td>haplorhini</td>\n",
+       "      <td>simiiformes</td>\n",
+       "      <td>hominoidea</td>\n",
+       "      <td>hominidae</td>\n",
+       "      <td>homininae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>homo</td>\n",
+       "      <td>homo sapiens</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                            file  max_detection_conf   \n",
-       "0  part0/sub020/2015_Unit058_Ivan042_img0265.jpg               0.964  \\\n",
-       "1  part0/sub020/2015_Unit058_Ivan042_img0266.jpg               0.977   \n",
-       "2  part0/sub020/2015_Unit058_Ivan042_img0267.jpg               0.966   \n",
-       "3  part0/sub020/2015_Unit058_Ivan042_img0268.jpg               0.974   \n",
-       "4  part0/sub020/2015_Unit058_Ivan042_img0269.jpg               0.941   \n",
-       "\n",
-       "                                          detections  \n",
-       "0  [{'category': '1', 'conf': 0.964, 'bbox': [0.5...  \n",
-       "1  [{'category': '1', 'conf': 0.977, 'bbox': [0.5...  \n",
-       "2  [{'category': '1', 'conf': 0.0102, 'bbox': [0....  \n",
-       "3  [{'category': '1', 'conf': 0.974, 'bbox': [0.6...  \n",
-       "4  [{'category': '1', 'conf': 0.941, 'bbox': [0.8...  "
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nacti_mdv5b = pd.json_normalize(data[\"images\"], max_level = 1)\n",
-    "nacti_mdv5b.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
+       "        original_label   kingdom    phylum   subphylum superclass     class  \\\n",
+       "4913880          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "4799380          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "4994742          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "4879871          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "7201111          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "4419687          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "3869956          human  animalia  chordata  vertebrata        NaN  mammalia   \n",
+       "\n",
+       "        subclass   infraclass        superorder     order    suborder  \\\n",
+       "4913880   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "4799380   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "4994742   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "4879871   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "7201111   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "4419687   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "3869956   theria  placentalia  euarchontoglires  primates  haplorhini   \n",
+       "\n",
+       "          infraorder superfamily     family  subfamily tribe genus  \\\n",
+       "4913880  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "4799380  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "4994742  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "4879871  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "7201111  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "4419687  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "3869956  simiiformes  hominoidea  hominidae  homininae   NaN  homo   \n",
+       "\n",
+       "              species subspecies variety  \n",
+       "4913880  homo sapiens        NaN     NaN  \n",
+       "4799380  homo sapiens        NaN     NaN  \n",
+       "4994742  homo sapiens        NaN     NaN  \n",
+       "4879871  homo sapiens        NaN     NaN  \n",
+       "7201111  homo sapiens        NaN     NaN  \n",
+       "4419687  homo sapiens        NaN     NaN  \n",
+       "3869956  homo sapiens        NaN     NaN  "
       ]
      },
-     "execution_count": 108,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dedupe_url_nacti = list(dedupe_species.loc[dedupe_species['dataset_name'] == \"NACTI\", 'url'])\n",
-    "dedupe_nacti_empties = []\n",
-    "for file in list(nacti_mdv5b.loc[nacti_mdv5b['detections'].astype(str) == '[]', 'file']):\n",
-    "    if file in dedupe_url_nacti:\n",
-    "        dedupe_nacti_empties.append(file)\n",
-    "\n",
-    "dedupe_nacti_empties"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Check Remaining Datasets\n",
+    "taxa = [col for col in list(df_clean.columns) if col in all_taxa or col ==\"original_label\"]\n",
     "\n",
-    "Let's automate the process to speed this up with a function to take the dataset and filename and return any marked empty by the model."
+    "df_taxa = df_clean[taxa].copy()\n",
+    "df_taxa.loc[df_taxa[\"original_label\"] == \"human\"].sample(7)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def check_md_results(dataset_name, filename):\n",
-    "    with open(\"../MegaDetector_results/\" + filename) as file:\n",
-    "        data = json.load(file)\n",
-    "    df_mdv5b = pd.json_normalize(data[\"images\"], max_level = 1)\n",
-    "    print(df_mdv5b.head())\n",
-    "    dedupe_url = list(dedupe_species.loc[dedupe_species[\"dataset_name\"] == dataset_name, 'url'])\n",
-    "    dedupe_url_empties = []\n",
-    "    for file in list(df_mdv5b.loc[df_mdv5b['detections'].astype(str) == '[]', 'file']):\n",
-    "        if file in dedupe_url:\n",
-    "            dedupe_url_empties.append(file)\n",
-    "    print(dataset_name, \": \", dedupe_url_empties)\n",
-    "    return dedupe_url_empties"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mdv5b_files = {\"Channel Islands Camera Traps\": \"channel-islands-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"ENA24\": \"ena24_mdv5b.0.0_results.json\",\n",
-    "               \"Idaho Camera Traps\": \"idaho-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"Island Conservation Camera Traps\": \"island-conservation-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"Missouri Camera Traps\": \"missouri-camera-traps_mdv5b.0.0_results.json\",\n",
-    "               \"Orinoquia Camera Traps\": \"orinoquia-camera-traps_public_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Camdeboo\": \"snapshot-safari_CDB_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Enonkishu\": \"snapshot-safari_ENO_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Karoo\": \"snapshot-safari_KAR_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Kgalagadi\": \"snapshot-safari_KGA_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Kruger\": \"snapshot-safari_KRU_mdv5b.0.0_results.json\",\n",
-    "               \"Snapshot Mountain Zebra\": \"snapshot-safari_MTZ_mdv5b.0.0_results.json\",\n",
-    "               \"SWG Camera Traps\": \"swg-camera-traps_public_mdv5b.0.0_results.json\",\n",
-    "               \"Wellington Camera Traps\": \"wellington-camera-traps_images_mdv5b.0.0_results.json\"}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                    file  max_detection_conf   \n",
-      "0         loc-h500hh06211894-003-577.jpg               0.981  \\\n",
-      "1  images/loc-h500fe12144668/000/350.jpg               0.935   \n",
-      "2  images/loc-h500fe12144668/000/351.jpg               0.957   \n",
-      "3  images/loc-h500fe12144668/000/352.jpg               0.933   \n",
-      "4  images/loc-h500fe12144668/000/353.jpg               0.953   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.981, 'bbox': [0.0...  \n",
-      "1  [{'category': '1', 'conf': 0.935, 'bbox': [0.2...  \n",
-      "2  [{'category': '1', 'conf': 0.957, 'bbox': [0.6...  \n",
-      "3  [{'category': '1', 'conf': 0.933, 'bbox': [0.6...  \n",
-      "4  [{'category': '1', 'conf': 0.953, 'bbox': [0.5...  \n",
-      "Channel Islands Camera Traps :  []\n",
-      "              file  max_detection_conf   \n",
-      "0  images/3738.jpg               0.898  \\\n",
-      "1  images/3739.jpg               0.879   \n",
-      "2   images/374.jpg               0.914   \n",
-      "3  images/3740.jpg               0.873   \n",
-      "4  images/3741.jpg               0.896   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.0104, 'bbox': [0....  \n",
-      "1  [{'category': '1', 'conf': 0.879, 'bbox': [0.5...  \n",
-      "2  [{'category': '1', 'conf': 0.115, 'bbox': [0.0...  \n",
-      "3  [{'category': '1', 'conf': 0.0125, 'bbox': [0....  \n",
-      "4  [{'category': '1', 'conf': 0.896, 'bbox': [0.5...  \n",
-      "ENA24 :  []\n",
-      "                                     file  max_detection_conf   \n",
-      "0  public/loc_0000/loc_0000_im_000533.jpg             -1.0000  \\\n",
-      "1  public/loc_0000/loc_0000_im_000534.jpg              0.0000   \n",
-      "2  public/loc_0000/loc_0000_im_000535.jpg              0.0143   \n",
-      "3  public/loc_0000/loc_0000_im_000536.jpg              0.0000   \n",
-      "4  public/loc_0000/loc_0000_im_000537.jpg              0.0000   \n",
-      "\n",
-      "                                          detections  \n",
-      "0                                                 []  \n",
-      "1                                                 []  \n",
-      "2  [{'category': '1', 'conf': 0.0143, 'bbox': [0....  \n",
-      "3                                                 []  \n",
-      "4                                                 []  \n",
-      "Idaho Camera Traps :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  public/chile/filipianalamatris/filipianalamatr...              0.9600  \\\n",
-      "1  public/chile/filipianalamatris/filipianalamatr...              0.9160   \n",
-      "2  public/chile/filipianalamatris/filipianalamatr...              0.9370   \n",
-      "3  public/chile/filipianalamatris/filipianalamatr...              0.9520   \n",
-      "4  public/chile/filipianalamatris/filipianalamatr...              0.0478   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.96, 'bbox': [0, 0...  \n",
-      "1  [{'category': '1', 'conf': 0.916, 'bbox': [0.1...  \n",
-      "2  [{'category': '1', 'conf': 0.937, 'bbox': [0.1...  \n",
-      "3  [{'category': '1', 'conf': 0.952, 'bbox': [0, ...  \n",
-      "4  [{'category': '1', 'conf': 0.0478, 'bbox': [0....  \n",
-      "Island Conservation Camera Traps :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  images/Set1/1.02-Agouti/SEQ81847/SEQ81847_IMG_...               0.946  \\\n",
-      "1  images/Set1/1.02-Agouti/SEQ81847/SEQ81847_IMG_...               0.948   \n",
-      "2  images/Set1/1.02-Agouti/SEQ81847/SEQ81847_IMG_...               0.946   \n",
-      "3  images/Set1/1.02-Agouti/SEQ81847/SEQ81847_IMG_...               0.894   \n",
-      "4  images/Set1/1.02-Agouti/SEQ81847/SEQ81847_IMG_...               0.819   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.946, 'bbox': [0.3...  \n",
-      "1  [{'category': '1', 'conf': 0.948, 'bbox': [0.4...  \n",
-      "2  [{'category': '1', 'conf': 0.946, 'bbox': [0.4...  \n",
-      "3  [{'category': '1', 'conf': 0.894, 'bbox': [0.4...  \n",
-      "4  [{'category': '1', 'conf': 0.819, 'bbox': [0.6...  \n",
-      "Missouri Camera Traps :  []\n",
-      "                        file  max_detection_conf   \n",
-      "0  N07/100EK113/01300334.JPG               0.907  \\\n",
-      "1  N07/100EK113/02170640.JPG               0.974   \n",
-      "2  N07/100EK113/01120029.JPG               0.911   \n",
-      "3  N07/100EK113/01120030.JPG               0.962   \n",
-      "4  N07/100EK113/01120031.JPG               0.963   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.907, 'bbox': [0.3...  \n",
-      "1  [{'category': '1', 'conf': 0.974, 'bbox': [0, ...  \n",
-      "2  [{'category': '1', 'conf': 0.911, 'bbox': [0.8...  \n",
-      "3  [{'category': '1', 'conf': 0.962, 'bbox': [0.7...  \n",
-      "4  [{'category': '1', 'conf': 0.963, 'bbox': [0.8...  \n",
-      "Orinoquia Camera Traps :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  CDB_public/CDB_S1/A05/A05_R1/CDB_S1_A05_R1_IMA...               0.974  \\\n",
-      "1  CDB_public/CDB_S1/A05/A05_R1/CDB_S1_A05_R1_IMA...               0.932   \n",
-      "2  CDB_public/CDB_S1/A05/A05_R1/CDB_S1_A05_R1_IMA...               0.942   \n",
-      "3  CDB_public/CDB_S1/A05/A05_R1/CDB_S1_A05_R1_IMA...               0.943   \n",
-      "4  CDB_public/CDB_S1/A05/A05_R1/CDB_S1_A05_R1_IMA...               0.955   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.0139, 'bbox': [0....  \n",
-      "1  [{'category': '1', 'conf': 0.044, 'bbox': [0.9...  \n",
-      "2  [{'category': '1', 'conf': 0.0387, 'bbox': [0....  \n",
-      "3  [{'category': '1', 'conf': 0.0478, 'bbox': [0....  \n",
-      "4  [{'category': '1', 'conf': 0.955, 'bbox': [0.2...  \n",
-      "Snapshot Camdeboo :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  ENO_public/ENO_S1/B02/B02_R1/ENO_S1_B02_R1_IMA...               0.970  \\\n",
-      "1  ENO_public/ENO_S1/B02/B02_R1/ENO_S1_B02_R1_IMA...               0.960   \n",
-      "2  ENO_public/ENO_S1/B02/B02_R1/ENO_S1_B02_R1_IMA...               0.970   \n",
-      "3  ENO_public/ENO_S1/B02/B02_R1/ENO_S1_B02_R1_IMA...               0.977   \n",
-      "4  ENO_public/ENO_S1/B02/B02_R1/ENO_S1_B02_R1_IMA...               0.402   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.97, 'bbox': [0.46...  \n",
-      "1  [{'category': '1', 'conf': 0.918, 'bbox': [0.8...  \n",
-      "2  [{'category': '1', 'conf': 0.97, 'bbox': [0.66...  \n",
-      "3  [{'category': '1', 'conf': 0.0102, 'bbox': [0,...  \n",
-      "4  [{'category': '1', 'conf': 0.17, 'bbox': [0.43...  \n",
-      "Snapshot Enonkishu :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  KAR_public/KAR_S1/A01/A01_R1/KAR_S1_A01_R1_IMA...              0.7980  \\\n",
-      "1  KAR_public/KAR_S1/A01/A01_R1/KAR_S1_A01_R1_IMA...              0.0649   \n",
-      "2  KAR_public/KAR_S1/A01/A01_R1/KAR_S1_A01_R1_IMA...              0.0465   \n",
-      "3  KAR_public/KAR_S1/A01/A01_R1/KAR_S1_A01_R1_IMA...              0.9670   \n",
-      "4  KAR_public/KAR_S1/A01/A01_R1/KAR_S1_A01_R1_IMA...              0.0439   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.0157, 'bbox': [0,...  \n",
-      "1  [{'category': '1', 'conf': 0.0291, 'bbox': [0....  \n",
-      "2  [{'category': '1', 'conf': 0.0277, 'bbox': [0....  \n",
-      "3  [{'category': '1', 'conf': 0.967, 'bbox': [0.2...  \n",
-      "4  [{'category': '1', 'conf': 0.0439, 'bbox': [0....  \n",
-      "Snapshot Karoo :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  KGA_public/KGA_S1/A01/A01_R1/KGA_S1_A01_R1_IMA...              0.5080  \\\n",
-      "1  KGA_public/KGA_S1/A01/A01_R1/KGA_S1_A01_R1_IMA...              0.6820   \n",
-      "2  KGA_public/KGA_S1/A01/A01_R1/KGA_S1_A01_R1_IMA...              0.6400   \n",
-      "3  KGA_public/KGA_S1/A01/A01_R1/KGA_S1_A01_R1_IMA...              0.0196   \n",
-      "4  KGA_public/KGA_S1/A01/A01_R1/KGA_S1_A01_R1_IMA...              0.0223   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.0235, 'bbox': [0....  \n",
-      "1  [{'category': '1', 'conf': 0.0214, 'bbox': [0....  \n",
-      "2  [{'category': '1', 'conf': 0.0139, 'bbox': [0....  \n",
-      "3  [{'category': '1', 'conf': 0.011, 'bbox': [0.8...  \n",
-      "4  [{'category': '1', 'conf': 0.0174, 'bbox': [0....  \n",
-      "Snapshot Kgalagadi :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  KRU_public/KRU_S1/1/1_R1/KRU_S1_1_R1_IMAG0166.JPG               0.532  \\\n",
-      "1  KRU_public/KRU_S1/1/1_R1/KRU_S1_1_R1_IMAG0167.JPG               0.830   \n",
-      "2  KRU_public/KRU_S1/1/1_R1/KRU_S1_1_R1_IMAG0168.JPG               0.000   \n",
-      "3  KRU_public/KRU_S1/1/1_R1/KRU_S1_1_R1_IMAG0169.JPG               0.000   \n",
-      "4  KRU_public/KRU_S1/1/1_R1/KRU_S1_1_R1_IMAG0170.JPG               0.000   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.532, 'bbox': [0.3...  \n",
-      "1  [{'category': '1', 'conf': 0.83, 'bbox': [0.00...  \n",
-      "2                                                 []  \n",
-      "3                                                 []  \n",
-      "4                                                 []  \n",
-      "Snapshot Kruger :  []\n",
-      "                                                file  max_detection_conf   \n",
-      "0  MTZ_public/MTZ_S1/B04/B04_R1/MTZ_S1_B04_R1_IMA...               0.000  \\\n",
-      "1  MTZ_public/MTZ_S1/B04/B04_R1/MTZ_S1_B04_R1_IMA...               0.952   \n",
-      "2  MTZ_public/MTZ_S1/B04/B04_R1/MTZ_S1_B04_R1_IMA...               0.926   \n",
-      "3  MTZ_public/MTZ_S1/B04/B04_R1/MTZ_S1_B04_R1_IMA...               0.929   \n",
-      "4  MTZ_public/MTZ_S1/B04/B04_R1/MTZ_S1_B04_R1_IMA...               0.932   \n",
-      "\n",
-      "                                          detections  \n",
-      "0                                                 []  \n",
-      "1  [{'category': '1', 'conf': 0.952, 'bbox': [0.1...  \n",
-      "2  [{'category': '1', 'conf': 0.687, 'bbox': [0.2...  \n",
-      "3  [{'category': '1', 'conf': 0.0173, 'bbox': [0....  \n",
-      "4  [{'category': '1', 'conf': 0.856, 'bbox': [0.1...  \n",
-      "Snapshot Mountain Zebra :  []\n",
-      "                                   file  max_detection_conf   \n",
-      "0  lao/loc_0388/2020/03/image_00046.jpg             -1.0000  \\\n",
-      "1  lao/loc_0388/2020/03/image_00047.jpg             -1.0000   \n",
-      "2  lao/loc_0388/2020/03/image_00048.jpg              0.0134   \n",
-      "3  lao/loc_0388/2020/03/image_00049.jpg              0.1070   \n",
-      "4  lao/loc_0388/2020/03/image_00050.jpg              0.0295   \n",
-      "\n",
-      "                                          detections  \n",
-      "0                                                 []  \n",
-      "1                                                 []  \n",
-      "2  [{'category': '1', 'conf': 0.0134, 'bbox': [0....  \n",
-      "3  [{'category': '1', 'conf': 0.0475, 'bbox': [0....  \n",
-      "4  [{'category': '1', 'conf': 0.0122, 'bbox': [0....  \n",
-      "SWG Camera Traps :  []\n",
-      "                       file  max_detection_conf   \n",
-      "0  050116153240043c1606.JPG               0.939  \\\n",
-      "1  140616110256010as072.JPG               0.808   \n",
-      "2  011116175838031a1603.JPG               0.771   \n",
-      "3  011116175856050a7511.JPG               0.881   \n",
-      "4  011116175858050a7512.JPG               0.903   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.0166, 'bbox': [0....  \n",
-      "1  [{'category': '1', 'conf': 0.808, 'bbox': [0.8...  \n",
-      "2  [{'category': '1', 'conf': 0.771, 'bbox': [0.5...  \n",
-      "3  [{'category': '1', 'conf': 0.881, 'bbox': [0.0...  \n",
-      "4  [{'category': '1', 'conf': 0.903, 'bbox': [0.0...  \n",
-      "Wellington Camera Traps :  []\n"
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 9935544 entries, 1 to 19351155\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column            Non-Null Count    Dtype  \n",
+      "---  ------            --------------    -----  \n",
+      " 0   dataset_name      9935544 non-null  object \n",
+      " 1   url_gcp           9935544 non-null  object \n",
+      " 2   url_aws           9935544 non-null  object \n",
+      " 3   url_azure         9935544 non-null  object \n",
+      " 4   image_id          9935544 non-null  object \n",
+      " 5   sequence_id       9935544 non-null  object \n",
+      " 6   location_id       9935544 non-null  object \n",
+      " 7   frame_num         9935544 non-null  int64  \n",
+      " 8   original_label    9935544 non-null  object \n",
+      " 9   scientific_name   9935544 non-null  object \n",
+      " 10  common_name       9935544 non-null  object \n",
+      " 11  datetime          6633995 non-null  object \n",
+      " 12  annotation_level  9935544 non-null  object \n",
+      " 13  kingdom           9935544 non-null  object \n",
+      " 14  phylum            9919960 non-null  object \n",
+      " 15  subphylum         9884001 non-null  object \n",
+      " 16  superclass        79 non-null       object \n",
+      " 17  class             9917621 non-null  object \n",
+      " 18  subclass          8765223 non-null  object \n",
+      " 19  infraclass        8764312 non-null  object \n",
+      " 20  superorder        8555342 non-null  object \n",
+      " 21  order             9539604 non-null  object \n",
+      " 22  suborder          7032141 non-null  object \n",
+      " 23  infraorder        253192 non-null   object \n",
+      " 24  superfamily       1548287 non-null  object \n",
+      " 25  family            9435907 non-null  object \n",
+      " 26  subfamily         7405240 non-null  object \n",
+      " 27  tribe             6614011 non-null  object \n",
+      " 28  genus             9188249 non-null  object \n",
+      " 29  species           7264553 non-null  object \n",
+      " 30  subspecies        74052 non-null    object \n",
+      " 31  variety           2050 non-null     object \n",
+      " 32  multi_species     9935544 non-null  bool   \n",
+      " 33  num_species       9935544 non-null  float64\n",
+      " 34  lin_duplicate     9935544 non-null  bool   \n",
+      " 35  full_duplicate    9935544 non-null  bool   \n",
+      "dtypes: bool(3), float64(1), int64(1), object(31)\n",
+      "memory usage: 2.5+ GB\n"
      ]
     }
    ],
    "source": [
-    "empties = {}\n",
-    "for key in list(mdv5b_files.keys()):\n",
-    "    empties[key] = check_md_results(key, mdv5b_files[key])"
+    "df_clean.loc[df_clean[\"original_label\"] != \"human\"].info(show_counts = True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'Channel Islands Camera Traps': [],\n",
-       " 'ENA24': [],\n",
-       " 'Idaho Camera Traps': [],\n",
-       " 'Island Conservation Camera Traps': [],\n",
-       " 'Missouri Camera Traps': [],\n",
-       " 'Orinoquia Camera Traps': [],\n",
-       " 'Snapshot Camdeboo': [],\n",
-       " 'Snapshot Enonkishu': [],\n",
-       " 'Snapshot Karoo': [],\n",
-       " 'Snapshot Kgalagadi': [],\n",
-       " 'Snapshot Kruger': [],\n",
-       " 'Snapshot Mountain Zebra': [],\n",
-       " 'SWG Camera Traps': [],\n",
-       " 'Wellington Camera Traps': []}"
+       "dataset_name             20\n",
+       "url_gcp             9849119\n",
+       "url_aws             9849119\n",
+       "url_azure           9849119\n",
+       "image_id            9849119\n",
+       "sequence_id         1198696\n",
+       "location_id            9524\n",
+       "frame_num              1023\n",
+       "original_label         1200\n",
+       "scientific_name         907\n",
+       "common_name             998\n",
+       "datetime            4688143\n",
+       "annotation_level          3\n",
+       "kingdom                   1\n",
+       "phylum                    2\n",
+       "subphylum                 5\n",
+       "superclass                1\n",
+       "class                     8\n",
+       "subclass                  3\n",
+       "infraclass                2\n",
+       "superorder                5\n",
+       "order                    58\n",
+       "suborder                 17\n",
+       "infraorder                9\n",
+       "superfamily              12\n",
+       "family                  187\n",
+       "subfamily                71\n",
+       "tribe                    46\n",
+       "genus                   537\n",
+       "species                 738\n",
+       "subspecies               12\n",
+       "variety                   1\n",
+       "multi_species             2\n",
+       "num_species               4\n",
+       "lin_duplicate             2\n",
+       "full_duplicate            2\n",
+       "dtype: int64"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "empties"
+    "df_clean.loc[df_clean[\"original_label\"] != \"human\"].nunique()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Snapshot Serengeti\n",
-    "\n",
-    "Snapshot Serengeti was evaluated with MegaDetector v4 due to questions raised by [this issue](https://github.com/ultralytics/yolov5/issues/9294) as noted on [LILA BC's site](https://lila.science/megadetector-results-for-camera-trap-datasets/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Snapshot Serengeti was evaluated with MegaDetector v4\n",
-    "mdv4_files = [\"snapshot-serengeti-mdv4.1.0_results.json/snapshot-serengeti_S\" + str(i) + \"_mdv4.1.0_results.json\" for i in range(1,11)]\n",
-    "mdv4_files.append(\"snapshot-serengeti-mdv4.1.0_results.json/snapshot-serengeti_SER_S11_mdv4.1.0_results.json\")"
+    "We have 1,198,696 distinct sequence IDs for the 9,849,119 unique image IDs, suggesting an average of 8 images per sequence?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                file  max_detection_conf   \n",
-      "0  B04/B04_R1/S1_B04_R1_PICT0003.JPG              0.9820  \\\n",
-      "1  B04/B04_R1/S1_B04_R1_PICT0004.JPG             -1.0000   \n",
-      "2  B04/B04_R1/S1_B04_R1_PICT0005.JPG              0.0684   \n",
-      "3  B04/B04_R1/S1_B04_R1_PICT0006.JPG              0.9930   \n",
-      "4  B04/B04_R1/S1_B04_R1_PICT0007.JPG              0.9970   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.982, 'bbox': [0.5...  \n",
-      "1                                                 []  \n",
-      "2  [{'category': '1', 'conf': 0.0684, 'bbox': [0....  \n",
-      "3  [{'category': '1', 'conf': 0.993, 'bbox': [0.6...  \n",
-      "4  [{'category': '1', 'conf': 0.997, 'bbox': [0.0...  \n",
-      "Snapshot Serengeti :  []\n",
-      "                                file  max_detection_conf   \n",
-      "0  B04/B04_R1/S2_B04_R1_IMAG0195.JPG               0.994  \\\n",
-      "1  B04/B04_R1/S2_B04_R1_IMAG0196.JPG               0.994   \n",
-      "2  B04/B04_R1/S2_B04_R1_IMAG0197.JPG               0.993   \n",
-      "3  B04/B04_R1/S2_B04_R1_IMAG0198.JPG               0.996   \n",
-      "4  B04/B04_R1/S2_B04_R1_IMAG0199.JPG               0.997   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.994, 'bbox': [0.2...  \n",
-      "1  [{'category': '1', 'conf': 0.994, 'bbox': [0.2...  \n",
-      "2  [{'category': '1', 'conf': 0.993, 'bbox': [0.2...  \n",
-      "3  [{'category': '1', 'conf': 0.996, 'bbox': [0.0...  \n",
-      "4  [{'category': '1', 'conf': 0.997, 'bbox': [0.0...  \n",
-      "Snapshot Serengeti :  []\n",
-      "                                  file  max_detection_conf   \n",
-      "0  D11/D11_R11/S3_D11_R11_IMAG0526.JPG               0.109  \\\n",
-      "1  D11/D11_R11/S3_D11_R11_IMAG0527.JPG               0.856   \n",
-      "2  D11/D11_R11/S3_D11_R11_IMAG0528.JPG               0.000   \n",
-      "3  D11/D11_R11/S3_D11_R11_IMAG0529.JPG              -1.000   \n",
-      "4  D11/D11_R11/S3_D11_R11_IMAG0530.JPG              -1.000   \n",
-      "\n",
-      "                                          detections  \n",
-      "0  [{'category': '1', 'conf': 0.109, 'bbox': [0.7...  \n",
-      "1  [{'category': '1', 'conf': 0.856, 'bbox': [0.4...  \n",
-      "2                                                 []  \n",
-      "3                                                 []  \n",
-      "4                                                 []  \n",
-      "Snapshot Serengeti :  []\n",
-      "                                file  max_detection_conf   \n",
-      "0  B03/B03_R1/S4_B03_R1_IMAG1053.JPG              -1.000  \\\n",
-      "1  B03/B03_R1/S4_B03_R1_IMAG1054.JPG               0.112   \n",
-      "2  B03/B03_R1/S4_B03_R1_IMAG1055.JPG               0.498   \n",
-      "3  B03/B03_R1/S4_B03_R1_IMAG1056.JPG               0.176   \n",
-      "4  B03/B03_R1/S4_B03_R1_IMAG1057.JPG               0.101   \n",
-      "\n",
-      "                                          detections  \n",
-      "0                                                 []  \n",
-      "1  [{'category': '1', 'conf': 0.112, 'bbox': [0.0...  \n",
-      "2  [{'category': '1', 'conf': 0.498, 'bbox': [0.7...  \n",
-      "3  [{'category': '1', 'conf': 0.176, 'bbox': [0.5...  \n",
-      "4  [{'category': '1', 'conf': 0.101, 'bbox': [0.7...  \n",
-      "Snapshot Serengeti :  []\n",
-      "                                file  max_detection_conf   \n",
-      "0  B03/B03_R2/S5_B03_R2_IMAG0535.JPG              0.0000  \\\n",
-      "1  B03/B03_R2/S5_B03_R2_IMAG0536.JPG             -1.0000   \n",
-      "2  B03/B03_R2/S5_B03_R2_IMAG0537.JPG              0.0765   \n",
-      "3  B03/B03_R2/S5_B03_R2_IMAG0538.JPG             -1.0000   \n",
-      "4  B03/B03_R2/S5_B03_R2_IMAG0539.JPG             -1.0000   \n",
-      "\n",
-      "                                          detections  \n",
-      "0                                                 []  \n",
-      "1                                                 []  \n",
-      "2  [{'category': '1', 'conf': 0.0765, 'bbox': [0....  \n",
-      "3                                                 []  \n",
-      "4                                                 []  \n",
-      "Unexpected exception formatting exception. Falling back to standard exception\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3505, in run_code\n",
-      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
-      "  File \"/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/1689048971.py\", line 3, in <module>\n",
-      "    mdv4_empties[file] = check_md_results(\"Snapshot Serengeti\", file)\n",
-      "                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_1497/599526326.py\", line -1, in check_md_results\n",
-      "KeyboardInterrupt\n",
-      "\n",
-      "During handling of the above exception, another exception occurred:\n",
-      "\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n",
-      "    stb = self.InteractiveTB.structured_traceback(\n",
-      "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1310, in structured_traceback\n",
-      "    return FormattedTB.structured_traceback(\n",
-      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1199, in structured_traceback\n",
-      "    return VerboseTB.structured_traceback(\n",
-      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 1052, in structured_traceback\n",
-      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
-      "                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 978, in format_exception_as_a_whole\n",
-      "    frames.append(self.format_record(record))\n",
-      "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 878, in format_record\n",
-      "    frame_info.lines, Colors, self.has_colors, lvals\n",
-      "    ^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/IPython/core/ultratb.py\", line 712, in lines\n",
-      "    return self._sd.lines\n",
-      "           ^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n",
-      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
-      "                                               ^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/stack_data/core.py\", line 734, in lines\n",
-      "    pieces = self.included_pieces\n",
-      "             ^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n",
-      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
-      "                                               ^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/stack_data/core.py\", line 681, in included_pieces\n",
-      "    pos = scope_pieces.index(self.executing_piece)\n",
-      "                             ^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n",
-      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
-      "                                               ^^^^^^^^^^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/stack_data/core.py\", line 660, in executing_piece\n",
-      "    return only(\n",
-      "           ^^^^^\n",
-      "  File \"/opt/homebrew/Caskroom/miniforge/base/envs/std/lib/python3.11/site-packages/executing/executing.py\", line 190, in only\n",
-      "    raise NotOneValueFound('Expected one value, found 0')\n",
-      "executing.executing.NotOneValueFound: Expected one value, found 0\n"
-     ]
+     "data": {
+      "text/plain": [
+       "annotation_level\n",
+       "sequence    4156306\n",
+       "image       2892394\n",
+       "unknown     2886844\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "mdv4_empties = {}\n",
-    "for file in mdv4_files:\n",
-    "    mdv4_empties[file] = check_md_results(\"Snapshot Serengeti\", file)"
+    "df_clean.loc[df_clean[\"original_label\"] != \"human\", \"annotation_level\"].value_counts()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#only ran first 4\n",
-    "for file in mdv4_files[4:]:\n",
-    "    mdv4_empties[file] = check_md_results(\"Snapshot Serengeti\", file)"
+    "#### Check Number of Images per Scientific Name?"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "with open(\"../MegaDetector_results/snapshot-serengeti-mdv4.1.0_results.json/snapshot-serengeti_S1_mdv4.1.0_results.json\") as file:\n",
-    "    data = json.load(file)\n",
-    "\n",
-    "serengeti_df_mdv4 = pd.json_normalize(data[\"images\"], max_level = 1)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "91803"
+       "<Axes: xlabel='Count', ylabel='class'>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "len(serengeti_df_mdv4.loc[serengeti_df_mdv4.max_detection_conf >= .8])"
+    "sns.histplot(df_clean.loc[df_clean[\"original_label\"] != \"human\"], y = 'class')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "406612"
+       "<Axes: xlabel='Count', ylabel='order'>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6ulKpUiVWrVqFnp4e3bt3Z+vWrYwcOZKAgAAKFSrEsmXLsLOz48iRI0ycOJGdO3cC2fmNVapUkXpQ3759m4SEBFq1aoW3tzfPnz9n8ODBGBsbM3r0aBwcHAgJCWHVqlXUr1+fffv2MWnSJE6fPs3du3dxdHSUnvfAgQM4OTkxc+ZM9PX12bhxI35+fu85xj+Ht40w+aHtUUFGW5OptRWht2YRemsWobfm0Fat833QeOrUKYyMjBg8eDAymYxvv/2WTp06cfv2bbXzDh48SPXq1aUgsXnz5jRr1kw6vnnzZnr06CG10GvWrBmOjo4EBgbyyy+/vHffpk2b0rp1awBcXV0JDAzk4MGDUs5j//79pTzF5s2bs2bNmg8+w4ABAzAyMuKbb76hePHidOjQgZIlSwJQu3ZtoqKiANi9ezdubm5Uq1YNgNatW3P48GF27dpFzZo1AXB2dsbU1FS6tkwmY/r06ZiYmGBiYsKgQYNYuXIlo0ePBqBBgwb88MMPALRp0wZPT08iIiKk++fg7e2NsbExenp6REdHY2xsTGpqKklJSVLXms/lbSPM12x79F8mvyZTF1SE3ppF6K1ZhN6aI79qXWCMMLGxse85k21sbN4LGmNjY9/rx2xjY0NiYiIAUVFRhIeHs23bNum4Uqnk+++/z/W+ZcuWVXtvZWVFfHy89N7MzEx6raen99Gl3bfP1dHRUQv65HK5ZEx5/vw5ZcqUURtrbW3NnTt3pPfvuq2LFCmitgVvZWVFXFxcrvfW18/Oq8ttrhEREcybN4/Hjx9TtmxZKQXgS9zTbxth8kPbo4KMtiZTaytCb80i9NYsQm/Noa1a5/ug0dLSkqioKLKyspD/f3PF232a3z7v1KlTap+9XW/Q0tISV1dX3NzcpOPR0dEYGhoSGRlJeHg4lStXlo69XUsRIDIyUm07Ny98rkO6dOnSREREqH0WERGhFii+e63k5GTevHmDkZGRNM93g2dA2jbPjYyMDIYMGcKYMWPo2bMnMpmMGzducODAgc+a97u8bYTJUKq4H5esdtyskD7FTAy+6NoCgUAgEAj+N+T7BsGOjo6oVCr8/f1JT0/nxo0bUo7f27i4uHDv3j127NhBZmYmf/zxB0ePHpWOd+3alY0bN3Lt2jUgO5+gY8eOBAcH53rfo0ePcu7cOTIzM9m1axf37t2jbdu2X+ch/z9dunRh1apV3Lx5E6VSyaFDhzhx4gQdOnT44BilUsncuXNJS0vj4cOHrF27ls6dO+fpvhkZGaSmpmJoaIhMJiM6Opr58+dLx/LKgsN3mX3wNrMP3sYr6CZT991Q+3n5Oj3P1xQIBAKBQPC/Jd+vNJqamrJ27VpmzJjB+vXrsbW1xdnZmUePHqmdV6ZMGVasWIGPjw+//vor1apV48cff5SOt2zZktevXzN58mSio6MxMzOjX79+9O7dW8ophGyndmhoKCqVigEDBiCTybC1tWXVqlWUKVOGc+fOAeDm5kaVKlX49ttvOX78uDQ+Z7Wzbt26720l9+7dm1evXrFixQpmzpyJjY0NlpaWPHjwgLp161KkSBHq16/Pzz//THx8PMWLF8fGxobhw4djamrKq1evJBe2v7+/dK89e/Zw8OBBkpOTqVevHrt372b58uUYGRlRu3bt9zQdN24cycnJqFQqUlNTKVSoEDNnzmT27NlMmTIFAENDQwwNDbl79y7lypXL25f2iTaC/3ZLvf8i+dWBV1ARemsWobdmEXprjvyqdYFxTwNUrlyZwMDAXI917NhRel2/fn3279//wet07NhR7fx36d69Oz179sTW1paGDRsyffp0Jk+eTFxcHA4ODkRGRjJ06FBmzpxJp06duHr1KkOHDqVKlSps2rSJPXv2sGPHDn777Tfs7e0JDw9n5MiR3L59G2trawAMDAxYtmwZNjY2DBgwgHPnzuHn50eTJk3w9fXlyJEjHD9+nIcPH9K+fXvGjRtHjx49ePbsGaNGjeLevXvSfG/evAnAn3/+KfWtfvz4MatWraJcuXLMnj2bc+fOqa0W9urVi4kTJ/Ly5Uu6dOlCcnL21vGLFy+wsrJi3bp1lChRgitXrvDTTz9RpEiRz/iG1PlUG0HhqP730FYHnrYi9NYsQm/NIvTWHNqqtVYEjZpCT0+P7du3s3z5crKysoiKisLMzEz6coOCgqhSpYpUhqZevXp07dpVOr579266desm9ZKuX78+3bp1IzAwUFr1dHZ2pmLFitL4V69eSfUfGzduzPr166V72dnZ0bdvXwBsbW0ZO3Ys7u7uTJ48Gcg2xcTGxqq5mwcOHEiVKlUAmDRpEvXq1ePy5cvS8fbt22NgYEDJkiX57rvvePr0KZC9fd+hQweKFi1KXFwcqampGBsbv5fb+Tl8qo2gcFT/c/KrA6+gIvTWLEJvzSL01hz5VesC457WJHK5nD///JOjR4+SmZlJXFycWu/nZ8+eUbp0abUxZcqUkYT+kPs5p/g4vO+kfnsl720ndUJCAmXKlCEyMhInJyeOHz+OtbU1qampJCQkSNd6N6jLcT0DGBkZYWZmJm11Q/ZK440bNwCk/wK8efOGmTNncuHCBSwtLalatSoqleofu6dzPywc1f8W2urA01aE3ppF6K1ZhN6aQ1u1FkHjWyQkJDBr1iwCAwOpXr06AOvWrZPyJ0uXLs3JkyfVxkRHR0uvra2tpZW7HCIiIqSADfLmpD5y5IjaZ0+fPkVfX18KNIsUKcLdu3fVznk7iExJSSExMRErKyspF/Ltreo6depIr6dOnUqRIkX4448/MDAwICsrS6ppmVfedk/nRm6OakHeUKlUyMysePg85R/1Ly/oCKe+QCAQ/HuIoPEt7ty5g1wux9DQEICrV6+yceNGKeBq3749y5YtY9++fbRr144bN26wY8cOvvnmGyC7vd+UKVNo3Lgx9vb2XLhwgW3btvHmzRt8fHy4dOkSb968wdLSkt9++40nT56gq6vLgQMH1HpoT5w4kaNHj5KSkoKXlxeQHZz6+vry448/MnnyZI4fP05GRgbTpk1jwoQJmJiYkJaWxsyZM3n06BEhISG8fPkSQ0NDKlWqxOnTp6Xr16lTh3Xr1kk9qAFevXpFTEwM7du3JzY2lqysLFJTU0lPz7vTecHhu2SIdtNfFZUKFAoFJiYmiJjxw3i7VhdBo0AgEPxLiKDxLX744Qd69uxJr169yMrKwtramt69e7Nw4UKeP3+OpaUlS5YsYd68eXh5eVGlShUaNmwoFRBv1aoVCoUCb29voqOjKVmyJEOGDMHPz4+UlBTq1KmDoaEhPj4+LF26lPDwcE6cOIGXlxdGRkZS0e8nT55w9OhRrl27xvjx4wHw8PDAxcWFS5cuUb58eX766ScuXLhAXFwc06ZNw9fXF8jOS9i7dy8ZGRnUqVOHBw8eEBgYSK1ataTnvHLlynvPXrNmTXbs2IFcLsfExIQaNWpw8uRJzpw5Q/fu3fMm5Cfc04J/gf+fxoBKheqjyQD/bf4tp35+dTwWVITemkXorTnyq9afOx+ZKieJTvBJnj17RmJiolrfax8fH+Lj41m4cGGuY3JyErdu3cq3335Lr169qFWrFhMmTJDOmTdvHnfv3mX58uV8++23rFixggYNGgDZq5/t27fn+PHjvHjxgh49ehAeHi6ZXx49ekTLli0JCwujbdu2PH/+nD/++EPaEh87diz6+vrMmTNHKvCds6Xt6ekpPUNSUhIZGRkULVqU+Ph4Hjx4gIeHB5MnT8bV1fWz9FEqlVy9epVfQ1981D0tEGiKWa7VUL189r+ehkAgEGgFtWvX/miupVhpzAOJiYn07NmTzZs3U716de7cucOBAwckN/PHyKnZ+DGzTGJiIunp6VhZWUnH3j43MjISpVJJkyZN1Mbr6+urdZJ5O4dST0+PD/27IGd7E/5dI8yn3NOCfwGVCkVKCibGxoj96Q/zbzn186vjsaAi9NYsQm/NkV+1Fu7pr0DVqlWZMmUKY8aMIT4+nmLFiuHm5vZZnWJyzAofM8uYm5tjYGBAREQE5cuXB9RbJlpaWmJoaMj58+elX7b09HQiIiLUXNOfw5YtW7hy5QqNGjUC/l0jzKfc04J/jrQlLZOJmPEj/NtOfW11PGorQm/NIvTWHNqqtQga80iXLl3o0qXLF4/PzSyzfft2pk2bhr6+Pq6urixevBg7OzuMjY2ldn6QnXdoa2uLj48Po0ePRkdHBx8fH44fP86xY8fw9fX9YH9pQOrDnZyczIsXL9SOKRQKSpQogVwuR6FQsHTpUhQKxRe1EfylbWXkOuJX62uiUqlIVigobGIi3NMfwayQ/v96CgKBQFBgEP9n/0L8/f3ZtWsXb968oUyZMgwfPlyqp+jr60tUVBR169bFwsJCbUx4eDiTJk2SzDLp6em0bdsWV1dXYmNjefnyJY8fP6ZJkybI5XKcnJyk8dWqVWPs2LEsX76cjRs3oqOjQ/Xq1SlatCjff/89JUuWlM5NT09n0aJFHD16lNTUVE6fPs2PP/5I3bp1qV+/PllZWSiVSoKDg/Hx8aFfv35MmTKFPXv2oFKpKFy4MFWrVlXrQPO5lC1qjL6++J/110SpVHI18m/Kl/14/olAIBAIBP8WImj8AsLCwti+fTt79uyhePHibN++nSlTpmBtbY2HhwezZ8+mdevWHDt2jLFjx9KhQwepjSCor1Y6Ojri4OAAZG8Rm5mZcfbsWfT19dm4cSN+fn5cvnxZMr6cOXOGkydPkp6eTps2bYiJiWH9+vVYWVkxcOBAqfbihg0bCA0N5dChQ2ptAdesWUP9+vWlAHbTpk0ALFmyhJ49e+Lu7o5KpWLBggUcPnyYX375Jc/6KJXKfOcMK2jkVwdeQUXorVmE3ppF6K058qvWBar3dH7DwMCApKQkduzYQbNmzejSpQvdunXD39+f6tWrSzUXW7ZsSVBQ0Gdf19vbG2NjY/T09IiOjsbY2JjU1FSSkpKkoPGnn36SuspUqlSJqlWrUqFCBQC+//57qfZiXtsCrly5kpIlS6JSqYiKisLU1PSLWggC3Lp164vGCfKOtvYv1VaE3ppF6K1ZhN6aQ1u1FkHjF1CnTh38/f3ZtGkTa9aswdDQkN69e5OQkECpUqXUzi1XrhzPnz//rOtGREQwb948Hj9+TNmyZSVzy9sO5o+1IXzz5o1UiDyvbug7d+4wfPhw4uPjqVChAhYWFh90XX+KqlWriu3pr0x+deAVVITemkXorVmE3pojv2ot3NNfkejoaIoWLcratWtJT0/nzz//ZOTIkbRp04aQkBCp0DZku591dbNlvnTpErdv35aOZWVl8fLlSyC7vd+QIUMYM2YMPXv2RCaTcePGDQ4cOKB27w+ZHp4/f87mzZulGpJ5cUPHxsbi4eHB0qVLcXR0BODw4cPvtTH8XLTVFaaNCK01i9Bbswi9NYvQW3Noq9YfbhAs+CDXr19n0KBB3LlzB319fYoWLQpAlSpVANi+fTuZmZmcO3eOw4cPS+O6d++OUqnk77//JjMzkzVr1vD69WsgO2hMTU3F0NAQmUxGdHS05Jz+HAdzamqq2nkKhQIDAwPJDT1v3jw1N7SBgQEKhQKVSkVKSgpKpRIjIyMA7t+/T0BAAMAXtREUCAQCgUBQ8BArjV+As7Mz58+fp0uXLqSnpyOXy2nYsCF2dnYArFixgunTpyOTyTA3N5cCtbt372JsbEy/fv148+YNhoaGFC5cGC8vL/T09PDy8mL27NlMmTIFgNKlS1OkSBHu3btHuXLlAAgJCWHixIkkJSWhp6dH1apVUSqVUq3I69evc/DgQaZOnYqHhwc1atQAoEiRIlSvXl1yQx8+fJhHjx5RpUoVSpQogYeHB+7u7igUCmQymfRz6tQpWrRooVF9BQKBQCAQ5D9E0PgFvHz5kpCQEIYMGYKbmxsxMTH07t2bx48fA9CsWTMmTpzIy5cvadGiBQ8fPgRALpdTrlw5Nm3axJ49e5g0aRI+Pj60adOGrKwsZsyYQenSpfH398fKyopt27axePFiKfADuHz5Mps2bcLMzIwJEyZw/fp1dHR0CA4OxsnJiaNHj2Jtbc3p06dJSkpi06ZN1K1blzNnzjBmzBh8fHwAKFSoEKamphw+fBgjIyNev37N8uXLCQgIwNHRkcTEREaOHMnRo0fzHDQK9/TXJ7868AoqQm/NIvTWLEJvzZFftRbu6a/IyZMnMTAwYMSIEchkMmxsbFi/fr2URDpq1CgMDAwoWbIkxYoVIyUlJdfr6Onp0b59e+RyOWlpaQQHBxMQECAZYPr27UtQUBDBwcG4ubkB4OHhIbUWnDBhAi1btiQyMvK9a2/evJkePXpIeYzNmjXD0dGRwMBAqYxO48aNpdqOhoaGhISEYGNjg0KhICYmBnNz8y9yUAv3tObQVgeetiL01ixCb80i9NYc2qq1CBq/gPj4eKysrNRMKeXLlyc+Ph4Ac3Nz6XO5XP5BF3Lx4sWRy7PTSpOSksjIyFCr5wjZbQffDgrfbheY49SOj49X6zcNEBUVRXh4ONu2bZM+UyqVfP/999L7nH7YkB3ABgcHExgYiEwm45tvvkGhUEgmnrwg3NNfn/zqwCuoCL01i9Bbswi9NUd+1Vq4p78ilpaWPHv2DJVKJQWOx44dQ6FQSOd4enoC8O2333L79m169+6Nvb09z58/p02bNgwcOBCZTMZvv/0mmU709PSIiIiQ6i4CPH36VHI0Q7bT+ZtvvgGQgslSpUq9Z5axtLTE1dVVWqGEbNe3oaGh9P7toPfQoUNs3ryZbdu2SYHprFmzvqgjTMTLVOQ6mXkeZ1ZIn2ImBnke919GWx142orQW7MIvTWL0FtzaKvWImj8Apo2bYqPjw8rVqxg4MCBxMTEMHv2bIYMGfLJscWKFZNyGgG2bt3K8OHD6d+/P15eXvj6+lKuXDkpp/H+/fssXLhQGh8QEEDlypUxMDBg7ty5NGrUiJIlS0qrnDmBa9euXfH29ub777+nZs2aXL9+ncGDBzN8+PBc+1MnJycjl8sxNDREpVIRGhrKvn37qFSpUp71mRV8h4wvKPHo7VpdBI0CgUAgEORTRND4CW7dusWsWbO4c+cONjY2tGzZkp07d7JmzRrc3d1ZsmQJKpUKfX19Dh069MnrxcfH4+joyMiRI6XVSl9fX06ePMnKlSuZMmUKbdu2JT09HX19fVq1akXp0qWl8TExMTg6OpKens4PP/zAo0ePWL9+PXv37kUul+Pq6srgwYO5c+cOSUlJ9OrVC7lcTtGiRenXrx8VKlSgc+fO3Lp1i/v372Nra4uLiwsdOnTg5MmTNGvWjKysLPT09ChXrhwPHz6U5vLZqFSoVLnXk/z4MFW+Sw7Or+TXZOqCitBbswi9NYvQW3PkV62FEeZfQKFQMGjQILp168aGDRt49OgRQ4cORSaT8fjxY1JTUzl06BBly5blwYMH9OzZE29vb7Vr+Pj4SH2eR40aRenSpVm6dCkdO3akY8eOUgDZsWNHHj58yNGjRxk3bhw9evTg2bNnjBo1innz5jF16lQAqdd0zZo1MTAwoEWLFuzcuZN169ZhYmJCp06d2LJlC2vXriUgIIAJEybw4sULNm7cyJ07d+jWrRvz58/HycmJv/76i+HDh2Nubk6jRo1QKBQMHz6cESNGkJiYSN++fRk2bFie8xNTXqeQ9gV/HpIVCq5G/p33gf9htDWZWlsRemsWobdmEXprDm3VWgSNH+HEiRPo6OgwatQo5HI5dnZ2DBo0iLVr19K4cWPq1q2LpaUlL168IDExETMzsy/u1wwQFBSEnZ0dffv2BbJNL2PHjsXd3Z3JkycD2eaV+vXrq43r1KkTlpaWANSsWROFQkGdOnUAaNiwIcuXLwcgMDAQJycnqYRO3bp16dq1K1u2bKFRo0YYGBgQGhpKhQoVqF+/Pvv375eMOnnBuJAxel+w0ljYxITyZWvnedx/kfyaTF1QEXprFqG3ZhF6a478qrUwwvwLxMTEUKpUKbXAKafcjUqlYtGiRZw8eRILCwuqVKlCRkbGF/drBkhISJCun4O1tTWpqakkJCQA6r2nc3i3H7WJiQkxMTFYWlqqubejoqIICwujXr160vlKpRIbGxsA/Pz88Pf3Z9GiRYwZM4a6desyY8aMvOc1ymTkPWTMNubkpz9E2oC2JlNrK0JvzSL01ixCb82hrVqLoDEXoqOjadOmDWPGjCE6OlrNJR0dHQ3AggULiI6O5sSJE5iYmADQrl27f3TfQ4cOUbhwYbXPnj59ir6+PkWKFGHkyJGEh4e/N+7dftRnzpyhfv36dOzYUe1zS0tLOnTowMyZMwE4f/48ffr0YdWqVWRlZXHr1i1GjRrF5MmTefbsGXPmzMHT05Pdu3fn6TnGOdsh+4IVygylivtxyXke9zkIZ7ZAIBAIBP8METTmQqlSpbhy5QqvX79m5cqVkks6MjKStWvXAv/X21lHR4e0tDS2bNnCvXv3aNas2Rff18jIiLi4ODZs2ECPHj2IiYnB19eXdu3a5Smv8EP9ojt37kz//v1p0aIFP/zwAzExMQCsW7eOSZMm4e3tjb29PRMmTMDCwgIDAwO1mpOfy4LDd7/IPf01Ec5sgUAgEAj+GQUmaIyMjMTJyYmpU6eyYsUKUlNTcXR0ZPr06ZiYmBASEsKKFSuIjo7G1taWMWPG0LBhQwB69+5N6dKlOX/+PCqVipUrV+Li4sLx48dZtmwZHh4eLF68GJlMRuHChdHV1WX06NFMmjSJevXqkZWVhVwux8zMjHPnzjFmzBgAnj9/Tq9evbh27RqQvf2bU5A75z7Pnj3Dy8uLv//+G5lMRv/+/Tl8+DCLFy8mMzMTPT09nj59yvnz56X6jAARERH8+uuvREdH4+3tzbVr15g8eTJnz57l9evXTJ8+nRs3bhAeHk5iYiIAtWrVwtfXFw8PD9LS0ihUqBAAY8eOBaBHjx4sXLiQLVu2oFKpKFKkiFRDMk98oXv6a1LQnNn51YFXUBF6axaht2YRemuO/Kr1f9Y9feTIEYKCglAqlYwYMQIvLy/atm3L9OnTWb58udSHedSoUezYsUPK1zt37hw7d+7EyMiIV69eAdldWiIjI3n+/DkHDx6kfPnyzJgxg8DAQAwMDBg5ciTjxo1jz549FC9enO3bt+Pn50dGRgZubm60b9+ecePGsX79eskJbW9vz4kTJ9i1axcbNmxg165dVKpUiaVLlxITE0O5cuUYO3YsgwcPxszMjJkzZ6Kvr8/GjRvx8/Pj3LlzZGZmMnDgQBwcHLh06RKpqakMHDgQf39/QkND1RzZBw8eZPr06VLZnOrVq5OWlsahQ4eIjo6mT58+6OvrExMTw5w5c1i8eLFa7+kdO3ZIrQg/ly91T39NCqozW1sdeNqK0FuzCL01i9Bbc2ir1gUuaJw0aRIWFhYAuLu7M2zYMBITE/PUhzknaMzKymL8+PFAtvO4bt26hIWFMXr0aEqUKEFUVBRJSUns2LGDZs2a0aVLF7p164ZMJvukE3r//v107dqVatWqAdk9pXfs2CE9h7e3N8bGxujp6REdHY2xsTGpqalSIBsVFcXkyZMxMjLC2NiYpUuXkpWV9Z4ezZs3x8vLixMnTtCyZUuCgoKoU6cOZcqUkfIzASwsLP613tNf6p7+mhQ0Z3Z+deAVVITemkXorVmE3pojv2r9n3VPv92b2crKivT0dB4+fMiFCxc+uw9zDubm5ixevBgfHx82b97Mxo0b0dXVJT4+nrS0NOrUqYO/vz+bNm1izZo1GBoa0rt3b4YNG/ZRJ7Snpye3bt2iTZs2qFQqxo0bx/Hjx8nMzJSczhEREcybN4/Hjx9TtmxZ6bmysrKIj4/H3NwcIyMjtWvnhr6+Pm3btmX//v20bNmSvXv3MmDAgPfO+zd7T3+pe/prUlCd2drqwNNWhN6aReitWYTemkNbtS5wQWNsbCzly5cHsvMcjYyMKF26NN27d//sPsxvU6dOHXx8fKTcxcuXL+Pu7k758uVp1qwZRYsWZe3ataSnp/Pnn38ycuRIqlWrRunSpTly5IjatXKc0N7e3sTGxhIREUFcXBzBwcHs3r1bKhyekZHBkCFDGDNmDD179kQmk3Hjxg0OHDgAZLugExMTefPmjRQ4Xrx4kRs3btCvX7/3nqFTp0507dqVK1euEBkZibOz83vn/Ju9p7/UPf1PMDHQJesj5Y7MCuWtQLlAIBAIBAJ1ClzQ6O3tzdmzZ/Hw8CAgIACZTEZWVha//fYb3377LceOHePw4cNERUVRqFAhOnbsKK3uXbhwgTlz5vD48WMgu89zt27dGDBgAIMGDeLw4cNER0eTlpbGhQsXKFasGNOmTWPx4sXs2rWL0NBQ0tPT2blzJx4eHixfvpyZM2dy7NgxqlatyunTp6latSrTpk0jMzOTbdu2sWnTJgC6detGZmYmANu3b0ehUDB37lwCAwPp3LkzJ06cAGD06NFSUPzDDz+wYcMGunTpQunSpUlKSsLPzw8dHR1u3brFb7/9RkREBDVr1qRs2bLMnDmTVq1asXPnTrZs2UJcXBwAN27ckHpPh4WFMXz4cKKiokhNTZWKhueF/4V72tu1OhVLFP70iQKBQCAQCL6IAhc05riTc1r1DRs2jJ9//pkyZcowatQoXrx4QcmSJfn555+xt7end+/eVKhQAYAJEybg7u7Od999h5OTE6dPn6Zp06ZMmjQJLy8v9PX1MTMzw8XFhQMHDjBgwAD69+/PoEGDyMrKomTJkri5uXHhwgVWrFjBmjVr8PLyIjY2luTkZHr37s2QIUOYP38+ZcqUoWXLlqxcuZL4+HicnJx4/Pgxly5d4vfff8fNzY3g4GCePHnCnDlzaNeuHcWKFePNmzeEhYWxefNmAgICpK1mfX19Tp06xbNnz3BxcWHz5s04OTnx22+/0b17d2xtbTlz5gwODg6sX7+e5cuXk5CQID3D/v37CQ8PZ9q0aRgbG1OlShUqVqzIjh07uHz5MnXr1v38L+F/4J4uaO7oT5FfHXgFFaG3ZhF6axaht+bIr1p/7nxkqn/SwiQfkVNyZ8uWLfTq1Yu9e/dStWpVAP744w+GDRvGyZMnAShatCjx8fE8ePAADw8PJk+ejKurKy1btsTGxkYyzRQqVAi5XE5qaiqNGzemSZMmdOzYkbp166Knp4dcLufatWv06NGD8PBwjI2NAXj06BEtW7YkLCyMe/fu0adPH86cOSMZbTw9PYHsvtQ58z5+/DjW1tb06tWLWrVqMWHCBOnZ5s2bx927d1m7di29e/fG1tZW6nGdM37Xrl3UqFEDgCZNmtC/f39pq3rChAnExcURGxuLTCajT58+dO/eXbp+9+7dadGiBT179vzgc34OSqWSq1ev8mvoC427p2e5VkP18plmbyoQCAQCQQGidu3aH821LHArjTnkZojJyMhgxowZXLx4kVKlSlG1alVUKpXkOt6wYQP+/v54eXkRHx9Po0aNmDFjBpaWlmzbto1ly5YxduxYFAoFzs7OTJ06lcjISJRKJU2aNFG7v76+PhEREdL73Iw2ufH8+fNcDTQ529Mfuta7rQRNTU0BSExM5NWrV9y+fZsRI0awcOFC5s6dy4IFC6TzMzMzqV69OoaGhh98ziJFinzW/OF/454uaO7oT5FfHXgFFaG3ZhF6axaht+bIr1r/Z93TOeRmiPnll1+4du0aEydOpHz58gwePFhaRUtLS+P+/fvMmDEDXV1dHj16xNSpU5k9ezazZ88mLi6OhQsXAnD79m3GjBnDihUr+PHHHzE0NOT8+fPSL0B6ejoRERHY2tpy6dIlALU2hAcOHKB58+YAUuHvtm3bMmHCBKytrXn69Knas0RERFC8eHHpfW6mnQ8ZeW7cuMHp06cpUaIE3bt3Z8uWLbi7u9OmTRvpnKdPn2JmZoZCofjgc06cOPHzxf8fuKcLqjv6U2irA09bEXprFqG3ZhF6aw5t1brABI3W1tbcvXuXyMhIAGlFLSUlhSVLltC+fXvu3r1LRkYGMpmMypUr061bN9avXy99NmbMGAYOHMiAAQMoXrw4urq6mJubk5KSwuDBg5kzZw5t27alRIkSyOVyzM3NqVmzJra2tvj4+DB69Gh0dHTw8fHh+PHjHDt27L15lipVChcXF+n90aNHAQgODsba2hpzc3OmTJlC48aNsbe358KFC2zfvp1p06Z9kS6NGjWiffv2QPbqZ9euXVm+fDmVK1emQoUKhIaGMnz4cPz8/KhevfoHnzMv/NK2MnIdzf5qCXe0QCAQCARflwKT05hDTo7fgAEDOHToEG/evKFdu3aMHz+e3r1789dffwHZUb5SqaRRo0YULVqUffv24ebmJgWRurq6NG3aFB8fH0xMTPD09OTgwYOkp6cjk8mwtbVl586dmJqa4uHhwe3bt4mOjiYjIwNDQ0OmTZvGrVu32LNnD69fv8bb25suXbpI82vRogUymYxjx46hVCoxNDTkwoULxMfH4+7uzu3bt1EqlRgbGzNs2DAGDx7M+fPnGTRoELa2tsTExODm5sb169c5cuQIDg4OXL9+nWLFivH69WsqVarEzZs30dfXp0yZMpQtWxYfHx8ePXrEiBEjePToESqVClNTU37++Wd69OiBQqHAzc2Nq1evolQq0dXVpVWrVsyePfuzel/n5DTWqFEjT72yBXknR+tP5Z8I/h2E3ppF6K1ZhN6aI79q/bnzKjArje/Sq1ev97ZUd+zYIbXYK126NH369GHNmjVERkayb98+Hj58yLlz50hPT6d///4ULVqUwoULc/DgQc6ePUtQUBBly5blwYMH9OzZk8OHD9OlSxeMjIyIjY3lt99+o27duowfP55ffvmFSZMmceHCBbZu3crMmTOlFT+AiRMnYm1trWaKycjIYMCAAVSvXp3ffvsNlUqFl5cXhw4don///kD21nerVq1wc3MjLS0Nb29vDA0NcXd3l+596NAhnJycWLNmDVu3bmX+/Pls3LiR169fM3DgQNq0acPIkSN58eIF7u7uUmeYdevWYWxszOXLl5HL5UybNo2kpKQ8B4BKpTLfOcMKGvnVgVdQEXprFqG3ZhF6a478qvV/tvf0P2Hw4MGSgcTR0ZErV64A2S0G69ati6WlJS9evCAxMREzMzO1Fnvff/899erVk16fPn2a3r17A9ltC3/99VeeP3/+0ftfvHiRiIgIdu/ejYmJCQBeXl7Y29tz48YN6bzOnTujp6eHnp5enu599epV0tPTGTNmDDKZDCsrKzw8PHB3d2fs2LEYGhpy584d9u3bR4MGDZg9e/ZnO6ff5tatW3keI/gytLV/qbYi9NYsQm/NIvTWHNqqtQga36JYsWLSa11dXanot0qlYtGiRZw8eRILCwuqVKlCRkYGb+/sv+1eTkxMlIJP+D+TSm69od8mISEBc3NzKWAEMDExwczMjKioKGl+77qnP+ScfvfeUVFRvHjxQurBnfNsGRkZJCQkMHjwYPT19dm1axczZ86kTJkyjB07lhYtWnx03u9StWpVsT39lcmvDryCitBbswi9NYvQW3PkV63/s+7pHEPMP+XkyZNSx5QFCxYQHR3NiRMnpICuXbt20rnHjh3Dzs4OgC1btrB3794PXjfHLZ1DZGQkf/31F0ePHmXYsGEkJiaiUCik+yQnJ5OYmEjx4sWlIPVdp/SHnNPvYmlpiY2NDb///rv0mUKhICEhAQsLC+7evYujoyP9+vUjOTmZrVu38vPPPxMWFkbhwp/fbUVbXWHaiNBaswi9NYvQW7MIvTWHtmqt2QbB+QB9fX2Sk5M/eV6zZs0oV64ckB1YGRgYoKOjQ1paGuvWrePevXtkZGQA0Lx5c6m24osXL/iYt6hmzZpq7x8/foy1tTWXLl2iX79+VKxYkenTp5OcnExycjIzZszAxsYmbx1ZPvJMKSkprFmzhvT0dF69esXEiRP5+eefkclk7Ny5kwkTJpCQkICJiQkmJiYUKlQoz6uGiSkZ/3iuAoFAIBAI8hcFZqXxxIkTrFq1iidPnvD69Wtq1KiBt7c3ZcuWJSgoiJUrVxIVFYWhoSHz58/HwsICyC7o/dtvvwEwdOhQpkyZQv369Tl27JiUg2hubs4ff/zBt99+i1KpxMjIiFq1anHv3j0ADh8+TOXKldm7dy8rV66UekhDdm5knTp1ABgyZAhDhw6VjnXu3Jn4+HieP39O8+bNOXbsGBMnTmTChAnSFrK1tTXLli1DV1eX0NBQAAYMGMC1a9eYPn06oaGhFCtWjB49enD79m2KFCmCUqlk7NixnDx58r2t7qlTp/LLL7+wYMECZDIZlSpVYtWqVQD07t2b3r1706BBAwAKFy7Mr7/+ioGBQZ6+i6TUdEpinKcxAoFAIBAI8jcFImiMiYnBw8ODxYsX4+joSGJiIiNHjiQgIIDOnTszefJkli5dSqNGjfjjjz8YPnw469at49q1a8ydO5cVK1ZQq1Ytdu/ezbBhwzh16hRVqlSRrl+kSBFevnyJp6cnP/30E+fPn2fo0KGsXr0ayA4qu3TpQocOHYiMjCQ8PJxNmzZJ4x88eMCFCxeA7ILZkB0M7tq1i969e2Nvb8+oUaNITExkzJgxdOzYEQ8PD5KTkxk3bhyLFi1i+fLlUrHydu3asWLFCrKystixYwcPHz5kw4YN2NjYMGDAAK5evUqrVq3w8fHB19eXI0eOYG1tTWxsLFOmTOHnn3+ma9euPHnyhOHDh3Pq1Cm6d+/O8uXLadKkCdOnTyctLQ13d3fCw8PznNOoUuU/Z1hBI7868AoqQm/NIvTWLEJvzZFftf5PuactLCwICQnBxsYGhUJBTEwM5ubmxMbGsm/fPlq0aCG1+WvcuDFbt26lZMmS7N27l27dukkrgV26dKFChQoYGhq+dw87Ozup7E3Dhg1xdnZm//791K9f/5Pzc3Z2VjOnfIjjx4+jp6fHuHHj0NHRwdDQkF9++YU2bdoQHx8PgJ6eHu3bt1dzNTs7O1OxYkUA6tWrx6tXr6SOM40bN2b9+vUAHDhwgAoVKtCrVy8AKlasyMCBA9m8eTPdu3fHwMCACxcuEBISQv369VmzZs0XuacVr1O4evVhnscJ8o62OvC0FaG3ZhF6axaht+bQVq0LRNCop6dHcHAwgYGByGQyvvnmGxQKBbq6usTFxVG1alW183PyCuPj4ylVqpTasQ/lDpYtW1btvZWVlbRq+Ck+t+90QkICpUqVUkuOtba2BiAqKgqA4sWLvxfIveuefrtPtFwul3Iso6KiuHnzplSeR6VSoVQqpdI9U6dOZeXKlaxduxZPT08qV67M1KlTpfM/F5NCxlQsVztPYwR5I7868AoqQm/NIvTWLEJvzZFftf5PuacPHTrE5s2b2bZtG7a2tgDMmjWLe/fuYWVlJRWvzmHdunXUrl0bKysrnj17pnZs0aJFam3+cni7JiNku56trKw+a36PHz+mTp06Ut3HD1G6dGmio6NRKpXSL1NOH+rixYvz8OHDT/ad3rBhg1rpoLextLTEwcGBtWvXAuDu7o61tTU9e/YEsusrduvWjVGjRvHixQsCAgIYOXIkYWFhn/WcOWRmQeKbTIqZ5C0XUpB3tNWBp60IvTWL0FuzCL01h7ZqXSDc08nJycjlcgwNDVGpVJw5c4Z9+/aRkZFBhw4dOHr0KH/88QdZWVmEhobi7+9P4cKF6dixI9u3b+fatWtkZWWxe/dutmzZkmuv5atXr7J//36USiWnT5/m+PHjdOrU6b3zDAwMUCgUag7qcuXKfTJgBKQt9AULFpCamkp8fDy//vor33//PaVLl/4HCmXTrl07rl69yoEDB8jMzCQ2Npbg4GB8fHwAWLFiBbNmzUKhUGBqaoqRkVGe+04D+By6w8vX6f94vgKBQCAQCPIPBWKlsUOHDly6dIk2bdqgo6ND+fLl6du3L1u2bKFGjRrMnTuXuXPnEhUVRenSpfH19aVSpUpUqlSJV69eMX78eOLj46lYsSKrV6+WnNVvU6VKFY4fP463tzfFihVj/vz5Ui7k25QpU4b79+9TuXJlihUrRlpaGg8ePMDOzo67d+9KuYk7d+5k+fLlxMTE8OzZM7p06YKlpSXr169n7Nix/Pbbb2RlZVGyZEm+//57PD09sbe3JzExEU9PTynQA1i6dCn29vY4ODiozSU2NpalS5cCUKtWLYoVK0avXr3Yvn07kydPJiMjA5lMRmpqKpCdGzlnzhzq1auHSqWicOHCLFiwIO9fiEolbX0Lvg75NZm6oCL01ixCb80i9NYc+VXr/5QRRl9fn3nz5r33ubu7OwCtWrWiVatWuY7t1auXZAx5m7eDMsguP7NkyZJcr3HixAkAXr58yYwZMxgyZAhubm7ExMTQu3dvqYYjQO3atQE4deoU+/btk/pcL1u2jJkzZ6JQKHj48CEBAQE0btyYkydPMnr0aNq1a0fHjh0JDw9Xu/emTZukwuIApqamuLm5Adk5ipaWlly9ehV9fX02btyIn58f586dw9jYWM25HRMTw7Rp095zoIeEhNC0adNcn/tDpLxOIVmh4Grk33kaJ8g72ppMra0IvTWL0FuzCL01h7ZqXSCCxvzCyZMnMTAwYMSIEchkMmxsbFi/fn2uvxwf6nO9e/duWrRogaOjIwA//vij5ITOK97e3hgbG6Onp0d0dDTGxsakpqaSlJSEsbF6HcWPOdDzinEhYwqbmFC+bO0vmrfg0+TXZOqCitBbswi9NYvQW3PkV63/U0aY/EJ8fDxWVlZqxpTy5ctLW9Jv86E+18+ePXvP7V2mTBmp0HheiIiIYN68eTx+/JiyZctKJqHcemB/zIGeZ2QyZDJZvvoDUVDR1mRqbUXorVmE3ppF6K05tFVrETR+BqNGjfqs8ywtLXn27BkqlUoKHI8dO4ZCoXjv3GvXrmFtbc39+/fZvHkzr169YuHChZKD+m2io6OlVn5yuZy0tDTp2IsXL3KdS0ZGBkOGDGHMmDH07NkTmUzGjRs3OHDgQK7nf8yBnlfGOduRoVTxXJEmHNQCgUAgEBQQCoR7Or/QtGlTMjMzWbFiBenp6Tx9+pTZs2erBXk55NSKPHr0KLq6unz77beMHTuWLl26cPToUUJDQyWn9pEjR6RxFSpU4OLFi8TGxpKamkpAQECuZXgyMjJITU3F0NAQmUxGdHQ08+fPl46Beh/ujznQ88qCw3fxCropHNQCgUAgEBQgxErjZ3Lr1i1mzZrFnTt3sLGxoWXLluzcuZORI0eyefNmzMzMuHbtGoMGDWLNmjUsX74cc3NzevXqJeUPRkZGSte7evUqmzdvZvPmzWRmZvLixQsePXqEtbU1tWrVws3NjaysLCwsLKhduzZ6enpERkYyb948ypYtS5MmTTA0NKROnToYGhqycuVKhg0bxps3b7h+/TrPnj3D0NCQKVOmMH36dCwtLenatSv37t1j6tSpPH78mPT0dMLCwvjrr7/YtGkTFy5cwMnJiczMTORyOSVKlODvv/8mPT1dWun8LFQqVCqZcFB/RfKrA6+gIvTWLEJvzSL01hz5Vev/lHv6a6NQKBg0aBDdunVjw4YNPHr0iKFDh0orfDdv3sTHx0fqB3327FnJlQzqwWJOh5fixYvj6emJsbGxWq/qzp07I5fLOXPmDIULF2bx4sVs3bqVGjVqSNewt7cnKCiI1NRUNmzYwJ9//knXrl1Zu3YtCxcuZN26dfTt25ewsDBOnz7NiBEj2LBhA1ZWVhw7dozSpUuzcuVKMjIymDRpEoUKFUJfX5/69evz999/s2nTJoyNjVmyZAknT57MW8BItns6TYlwUGsAbXXgaStCb80i9NYsQm/Noa1ai6DxMzhx4gQ6OjqMGjUKuVyOnZ0dgwYNkjqr5NYP+kuIiIjg+vXrmJub8+bNG4oXL07jxo1Zt26dmtvZ1dUVfX19KZirWLEiLVu2BKBBgwasXr2aoUOHoqenJ7mwo6OjSUhI4ObNm6xfv1663sSJE2nZsiW//PILhoaGREZGsmvXLho3boyHhwc///xznp/DuJAxeiqZcFB/RfKrA6+gIvTWLEJvzSL01hz5VWvhnv4E/v7+ait8HyMmJoZSpUqpBYVv1158ux90ZGQk4eHhVK5cOddrvWtEuXr1KpcuXeK7775jypQpQHbtyD59+pCUlETp0qUpU6aM2mrfu72s3+49nTOPnP7TOe+zsrKIjIxEqVRKnWdy0NfXJyIigjZt2pCRkcHOnTvx9fWlaNGiDB06lB49enxSIzVkMmQgHNQaQFsdeNqK0FuzCL01i9Bbc2ir1v/ZoDEvlCpViujoaDVX9NsO59yMKJmZmdLrxMRE6bWLiwvjx4+X3l+/fp1SpUpx7Ngx4uLiAGjZsqW0ta1UKmncuDHFixf/4P1yu39uWFpaYmhoyPnz56Vf1vT0dCIiIrC1teXRo0dUq1YNV1dXUlNT+f3335k4cSL16tWjUqVKn3UPgBkuVVHJ5JgVytu2tkAgEAgEgvxLgQgac3IKb9y4gbGxMV26dMHd3Z1Lly6xaNEi7t69i6mpKS4uLgwfPvy9HL09e/awdOlSqbMLoNYt5dSpU7x69YpWrVoRExODmZkZSqWSlJQUvLy8SE9PZ+fOnXTp0kUaf/ToUU6fPs2bN28wMjJSu1cODRo0ICkpiVevXtGnTx82btxIrVq16NGjByqVClNTU4oXL05mZibNmjUjICAAgOHDhxMTE0NAQAAbNmzAzMwMFxcXnjx5Iq2ADh48mIsXL2JlZSXdr2bNmhQrVoxGjRqRlpZGiRIlKFmyJA8ePODYsWPs2LGDLVu2oKenh6mpKTY2Nujo6FC4cOE8fR/KrCy+sSqSpzECgUAgEAjyN1ofNL58+ZIBAwbQu3dv1q5dK7XuK1myJL/++ivjxo1j/fr1PHv2jFGjRqFQKJg6dWqe7qGrq0tWVhYyWbYjOCUlheTkZMzNzRkzZgzz589n5syZtG/fXhqTkZHBmzdvMDEx+WCu49mzZ6lXrx7FihVj48aNPHz4kNu3b1O1alUiIyNJTEzk5cuXODk5qW1BDx48GEdHRwwMsmsgxsfHc/jwYUxMTGjdujWQHVhWq1aNCRMm8ODBAwDu379PXFwc1apV48mTJ8TExPD06VOmTZuGgYEB165do0qVKkRGRvLixQueP3+Oi4sLlpaWedJLpcp/zrCCRn514BVUhN6aReitWYTemiO/av2fcU9/qHXf6tWrsbOzo2/fvgDY2toyduxY3N3dmTx5cp7ukZaWRrVq1QgMDARg586dzJo1C1tbW7p06cL3339P8+bN1bq2bNiwQcpr/OOPPxg2bBilSpUCoHTp0jg4OADqvaKDgoKws7Nj+/bt0nVOnz6Nu7s7WVlZmJqaYmtrS7t27aTjpqam9OrVSwrsHBwcUCgU1KlTB4CGDRty/fp1HBwcmDFjBs2bN8fX11ca7+vry8mTJ+nWrRuGhoZSUF2/fn1MTU2/yNyjeJ3C1asP8zxOkHe01YGnrQi9NYvQW7MIvTWHtmqt9UHjh1r36enpqZlVILvcTWpqKgkJCXm6h0ql4urVq5w+fZomTZqQlJSEUqmkWbNmwP/lFL7dni+ntA6AlZUV6enpvHz58qP3SUhI+OSc3zXBgLoRRkdHRzLBQLYRJqdAd1RUFGFhYdSrV086rlQqsbGxAcDPzw9/f38WLVrEmDFjqFu3LjNmzMhTPiOASSFjKparnacxgryRXx14BRWht2YRemsWobfmyK9a/2fc0x9q3VeyZElu3rypdu7Tp0/R19dXC6ogO7BKT1fvXvK2ecXQ0BB7e3sWLFjA6NGj0dXVxcjIiIEDB35wXrGxsZiYmADZjupChQphYWHxwfPbtGlDpUqViIiI+OicczO9fMwIEx0dTWxsLJCtVYcOHZg5c6Z0PC4uDpVKRVZWFrdu3WLUqFFMnjyZZ8+eMWfOHDw9Pdm9e/cHr58bMhn56g9DQUZbHXjaitBbswi9NYvQW3Noq9Za30bwQ637ihUrxoMHD9iwYYP0ua+vL+3atXvPCFOhQgWeP39OWFgYKpWK/fv3S3mAOZQqVYqgoCCuXLnCpEmTMDU1RVf3wzH3/PnzSUpKIiYmhsWLF9OtW7ePPkdISAjjxo377Dl/LqmpqdLrzp07ExwczB9//EFWVhaPHz/mp59+Yt26dcjlcry9vfHz8yMtLQ0LCwsMDAwwNzfP8z0zlHA/LjnPP88V77dbFAgEAoFAkD/Q+pVGU1NT1q5dy5w5c1i/fj1GRkb06tWLbt26UaFCBXx9ffH398fQ0JC2bdsyevTo965Ro0YNhg0bhqenJykpKTRv3hxnZ+d/NK/y5ctjb2+PsbExSqWSJ0+ekJCQQJ06dXj16hUDBgwgKSmJ6OhoHj16hKOjIyNHjmTNmjUMGTKEBQsWqG13X7t2jSNHjnD//n0cHR2ZNm0aTZs2BeDixYts3ryZqKgoFAoFRYsW5cWLF6SkpLB+/XoA6tSpw7p161i4cCFTpkyRVh9LlCghFQb38/Nj9OjR1K5dm6ysLAoVKkT37t3z/Ow+h+6Qocq7Zt6u1SlmYpD3gQKBQCAQCL46MpVK9QX/exd8isjISJycnLC3t2fx4sUolUpGjBiBra0tNjY2LFu2jHXr1lGzZk0MDAxo0aIFI0eOpGPHjvTu3ZuHDx+yYcMGbGxsGDBgAFevXsXPz48mTZrg6+vLkSNHOH78ONeuXZPK9dSsWZOYmBj69u1Lq1atGD16NOfPn6dPnz7cvXsXgC1btki9sStUqMD+/fuZO3cuhw4dwsTEBHt7e1avXo2DgwO3bt2iV69ebNiwgZo1a37ymZVKZfY8L6WRrvq82pFv4+1ajfLFjD99oiDf5sUUVITemkXorVmE3pojv2qdM6/atWt/dF5av9KY35k0aZKUy+ju7s6wYcPo168fZcqUoX79+h8c5+zsTMWKFQGoV68er169onnz5gA0btxYWkH85ptvCA4OxtramqSkJOLi4rCwsJBWEt9ly5YtDBkyRHJ2d+7cmV27dnHgwAF69uyJoaEhu3btIisri7p163Lp0qU8O6hzek/nFdGrOu9oqwNPWxF6axaht2YRemsObdVaBI1fGVtbW+l1jos6KSkpVxf023zKEZ2zQCyXy9m4cSNBQUEUKlQIOzs7FAoFH1pAjoqKYu7cuSxYsED6LDMzk+rVq2NoaMi2bdtYtmwZY8eORaFQ4OzszNSpU98zD32MnN7TeUX0qv588uu/VgsqQm/NIvTWLEJvzZFftf7PuKfzO7GxsZQvXx7I3rI2MjLC3Nz8k63/Pqc14KBBg7C3t+fQoUPo6+tz/PhxAIYOHfrBMZaWlri7u9OmTRvps6dPn2JmZoZCoSAuLo6FCxcCcPv2bcaMGcOKFSuYOHHiJ+fz1uTJe8goelV/CdrqwNNWhN6aReitWYTemkNbtRZB41dm4cKFzJ07l5SUFJYsWUL79u0/6rrOC2vWrMHX1xe5XI5MJiMzM5OQkBBCQ0Mlc0tO15jk5GQKFy5M165dWb58OZUrV6ZChQqEhoYyfPhw/Pz8qF69OoMHD2bOnDm0bduWEiVKIJfL8+yg/qVtZeQ6eX9G0ataIBAIBIL8iwgavzI2Nja0bduWN2/e0K5dO8aPH8+qVas+a2yOmaZRo0ZcuXKFb7/9FkdHR2mVsHfv3vj7+3P69Gnu3r1Lo0aNqFq1Kk5OThw/fpx69epRuHBhLC0tadiwIUuWLKF169bs3LmTtm3bolKpMDIy4ueff8bJyQmFQoG9vT2TJk1i3LhxyGQyatWqRb9+/fL0zGWLGn9xiSCBQCAQCAT5ExE0fmV69er13tbuqFGj3jvvxIkT0utNmzYB2UEjZLcxPHPmjOTADgkJYeTIkYSHh2NmZkbfvn1ZunQpJ06c4OHDh7Rv355x48bRo0cPqef2jz/+SJMmTZg4cSL16tUjKCiItLQ03N3diY6OBpDK/Jw7d47ChQtz5MgR3N3defbsmVpu5qdQKpX5rq9mQSO/9i8tqAi9NYvQW7MIvTVHftX6P9N7+r/AhxzYuZHTv/pDPbcNDAy4cOECISEh1K9fnzVr1kju6FGjRqGjo4OJiQkxMTHS1nZcXFyegsZbt279g6cV5AVtdeBpK0JvzSL01ixCb82hrVqLoFEL+JADOzc+1b966tSprFy5krVr1+Lp6UnlypWZOnUq9erVIyEhgV9//ZVbt25hbW1N9erVAfWe2p9D1apVxfb0Vya/OvAKKkJvzSL01ixCb82RX7UW7ukP0Lt3b+zt7XPdIv43sba2lgpqv8uKFSu4ePEia9asAWDu3LkEBgair6/P4cOH1crtwIcd2I8ePXrv2qVLl+bIkSNqn73dv/rWrVt069aNUaNG8eLFCwICAhg5ciRhYWF4eHjg6OjI2rVr0dXVJTExkR07duT52bXVFaaNCK01i9Bbswi9NYvQW3Noq9Za33taGxk6dKgUMAJs3LiRuXPncv78+fcCRsh2YCsUCmJjYz/pwG7Tps1H+1evWLGCWbNmoVAoMDU1lQJQyHZYGxoaoqOjw4sXL/D29gYgIyPj3xdBIBAIBAKBVqGVK405ruKpU6eyYsUKUlNTcXR0ZPr06ZiYmBASEsKKFSuIjo7G1taWMWPG0LBhw/euo1Kp2LRpE1u2bCEhIYFvvvmGyZMnS9uyL168YPbs2Zw+fRq5XM4PP/zAjBkzKFKkCJcvX8bPz4+HDx+SlJREpUqVmDZtGrVr1+b8+fNMmjSJLl26sHXrVtLS0nBwcGDOnDmYmJjg7+9PeHg4S5YswdHRkczMTMaNG0fr1q3x8fHh2LFjLFu2TFpJTE5Opk2bNqSmpmJhYUFCQgKHDx8mOTmZp0+fMmnSJMzMzHB2diYmJoYqVaqwZ88efHx8yMrKolixYlIO5MyZMxk2bBjfffed1F86x6gzZ84cfvnlF5YtW4ZKpcLExAQLCwvu3LmTq34fQhhhvj75NZm6oCL01ixCb80i9NYc+VXr/4QR5siRIwQFBUmuYi8vL9q2bcv06dNZvnw5devW5cyZM4waNYodO3ZQqVIltfFbt25l/fr1an2Y+/fvz6FDhyhWrBgeHh4YGxtz5MgR9PT08PDwwMvLi9mzZzNs2DDc3d3p0aMHqampTJ48mXnz5rF161Ygu/NKbGwsR48eJTY2ll69erF161bc3Nyk+5ubm3PlyhXs7Oykfs9hYWGMHj2aefPmUa1aNVq0aMHDhw8ZNGgQ/fr1w9PTk8OHD7N9+3YsLS0xNTUFoFKlSixdupT09HTatGmDkZERwcHBWFlZMXDgQNavX8+cOXO4ffs2T548YePGjZI+Y8aM4dtvv6VRo0bIZDJmz55Nhw4diIyMpEePHu/lSH4KYYTRHNqaTK2tCL01i9Bbswi9NYe2aq3VQWNuruLExER69OjBd999B0CzZs1wdHQkMDCQX375RW38x/owOzs7Ex4ezu+//y5t3/r4+PDy5Uv09PTYvn07tra2pKWlERUVhZmZ2Xu/BCNGjMDQ0BBbW1scHBxyzUF8lz179uDk5ETr1q2lkjs9evQgMDBQWi2sXbs233zzjdq4n376SdrarlSpElWrVqVChQoAfP/991y6dAmAzZs3f1QfAwMDDh06hJmZGd999520ypoXhBHm65Nfk6kLKkJvzSL01ixCb82RX7X+TxhhcnMVP3z4kAsXLrBt2zbpmFKp5Pvvv39v/Mf6MMfHxwPZxpIcihcvTvHixQE4f/48gwcP5vXr11SsWBFdXd33+j3nnAugp6eHSqVi2rRp3LhxA2NjY1QqFePGjQNgzJgx/PHHHyQkJFClShW161haWhIVFSW9z61v9ef2qo6KiiI8PPyD+mzYsAF/f3+8vLyIj4+nUaNGzJgxA0tLy/fu+SG0NcFXGxFaaxaht2YRemsWobfm0FattTpozM1VXLp0abp37662DRwdHY2hoeF74z/Wh/nNmzfS2LJlywJw//59goODadasGbNmzSIwMFDKf1y3bt1nrSTOnDlTymmMi4sjODgYAF9fX2QyGaVLl+bp06fA/zmwN2/erBaA5taX+nN6Vec8s6ura676pKWlcf/+fWbMmIGuri6PHj1i6tSpzJ49myVLlnzW9QUCgUAgEBRMtC5ofPr0qbTN3KFDB3r27EmvXr3w8vJCJpPRq1cvvL29+f7775k/fz62trYcO3aMMmXKYGVlxbVr17h27Rq6urokJSWp9WGeOnUqu3btIiAggOHDh2Nra4uLiws6OjrUrVsXpVJJ0aJF8fDwICsrSwpEr169ysKFC6X3YWFhANSrV49atWpRqlQpLl26xLfffounpye3b99GR0cHZ2dn6bn27NmDg4MDZmZmbN++nZo1a2JjY0ObNm0IDAyka9eu9O7dm5iYGOLj4wkLC2PlypW4uLgA8PPPP/P69WtatWpFcnIy27ZtY926ddSsWZNq1aoB2cafkiVL4ufnx/Lly6lSpQpdu3bFx8eH4cOHU7RoUcaNG4euri7W1tY4Ozujq6ub597TAoFAIBAICh5aFTS+fv2afv360bhxY8LCwnBxcZHcz99++y0qlYqWLVvy+vVrJk+ezIMHD7h9+zaDBg3i0aNHHDlyhIoVK1K/fn06deqEv78/rVu3Zvjw4cTFxZGRkYGrqytOTk5A9vJxo0aNuHz5MmFhYRQpUoQjR46wbds2Vq9eTa9evcjKyqJo0aLIZDJev37NqVOnpN7SYWFhnDx5ktGjR1OqVCm1ZzE2NiY4OFi6V8eOHdmzZw9btmzBw8ODo0eP8uDBAxYvXkzbtm0ZOnQoYWFhxMXF0bRpU2bNmsWrV6+k6/n4+EiriAYGBnTt2pUhQ4bQvXt3qTj31q1bCQsLY8SIERw8eJDr169z+fJlBg8eTJcuXXBwcGDKlCkEBQVx9+5dli9fTqNGjaQt9M9FuKe/PvnVgVdQEXprFqG3ZhF6a478qnWBdE+fOnWK9PR0Bg4cyLZt2xgyZAjOzs64u7vTrl07li5dCmQHYB07dpQKeQ8dOhRPT09q167N+vXrpes1atQIgMOHD3Pz5k1++uknNbPMmDFj+PHHHwF49OgRLVu25OXLl7i6uuLn50dgYCA1atTA29ublJQU5syZw9SpU3F2dsbX1xeAH3/8kebNm2NkZISPjw+enp5UqVIFHx8fyehy/PhxrK2t6dWrF926dWP48OEMHz4cgHnz5nH37l0p96Fdu3ZS/cScoHHXrl3UqFEDyM6j7N+/v5ppRkdHhzlz5tC6dWspkBwxYgQA3bt3x9zcHJlMhqGhIX/99RejR4+mbt266Onp5dkEA8I9rUm01YGnrQi9NYvQW7MIvTWHtmqtVUFjVFQUL168oH379gC4uLggk8nIyMggISHhk+PfNZB07NiR6dOnM2nSJPbu3UvLli0xNjaWjr9ttMlZKYyPj6dOnTo0atSI/fv3U7lyZYKDg/H39wfg2bNnVK1aVe0+ZcqU4fnz55+c3/Pnz3NtAXjixIkPPgO8b4LJKcMD75tgPmT8MTQ0ZNu2bSxbtoyxY8eiUChwdnZm6tSpaqaaz0G4p78++dWBV1ARemsWobdmEXprjvyqdYF0T1taWmJjY8OaNWtwcnLiwIEDmJmZkZCQwJUrV0hPT1c7PzExUe39u2aRnILgZ8+e5dChQyxevFjteGxsrFTaJmdVMCd47NSpE15eXjRo0IDChQtLJWxKly5NdHS02nU2b94sOaLPnTtH0aJFASSzTevWralRowbW1taSCSaHiIiIf9UE8yHjj0KhIC4ujoULFwJw+/ZtxowZw4oVK6Ti35+LtrrCtBGhtWYRemsWobdmEXprDm3VWqvaCDZr1oyUlBR+//13rl+/jqmpKRMnTuTnn3+mQoUKPH/+nLCwMFQqFfv37+fBgwcfvZ6enh4uLi4sXrwYExMT6tWrp3Y8ICCA+Ph4Xr16xdy5c2nUqBElS5YEoGnTpiiVSpYsWULHjh2lMV26dOHo0aOEhoaiVCo5ffq0FBwC/PDDD9jZ2QFI8wsMDGTLli107tyZ7du38+eff6JUKgkLC2P79u106tTpX9Gva9euLF++XLpvaGgobdq04cKFC6SkpDB48GCCgoJQqVSUKFECuVwuTDACgUAgEAgALVtpNDEx4bfffsPHx4c1a9aQlZWFg4MDy5cvp2TJkgwbNgxPT09SUlJo3ry5mjv5bW7evImPjw83btzAwMCAxMREOnfujJ2dHXfv3pXOS0pKwtnZGblcTtOmTalYsSI//vgjz58/p1y5cnz33XccPXoUGxsbPD098fHxoUaNGnh5eTFo0CAMDQ1xcHDAwMBA+hdFaGgoxYoV49ixY4wfPx7ILt49depUOnXqREhICIMGDSIzMxN9fX26deuGq6srkO3STklJITAwkOLFizNw4EAAtm/fzp49e0hNTQWycwpXrlxJXFwcZmZmUjDcs2dPQkNDcXFxITMzEyMjI0aMGCGZcTp16sSkSZMYN24cOjo61KtXT8qNFAgEAoFA8N9Gq4JGgAoVKrB69epcj3l4eODh4ZHrMR8fHwBevnzJgAED6N27N2vXruXevXt06tRJLX8xh1KlSlGrVi18fHzYs2cPc+fOZcWKFdSqVYvdu3fj5eWFvb09RkZG0phHjx5Rs2ZNAFatWoWDgwO1atWSciXLly+Pvb09zZs3Z82aNfTp04e//voLAH9/f65fv86OHTuws7PjyJEjTJw4ERcXF2rWrEnx4sXJysri0KFDQPYWMkB6ejqnTp3i3LlzuLm58fDhQ3bs2MGrV6/o1KkTDRo0AGDJkiUkJSXx+++/U6JECVavXs3OnTvp378/cXFx7NmzhwMHDlC+fHlCQ0MZMWIEL1++zDWP8mMI9/TXJ7868AoqQm/NIvTWLEJvzZFftS6Q7ul/g5MnT2JgYMDgwYN58uQJ27dv54cffpC2nT/E3r176datG3Xq1CE+Ph47OzvMzMzo3r07p0+fls67f/8+Xl5e0vvz58+TlpYmtSr8GLt378bNzU2qq9i6dWsOHz7Mrl27pEDU2dlZzegCMGTIEPT09GjYsCGQvXJZpEgRihQpQqVKlYiMjESlUhEYGMiSJUsks82IESPYsWMHp06dokaNGtI5zs7O1K9fn6tXrwr3dD5HWx142orQW7MIvTWL0FtzaKvW/7mgMT4+HisrKzIyMujevTtWVlasWLFCrU3fh8blmGBOnTqFt7c37du3p1WrVmpB448//sj9+/fx8/Nj8ODB2NjYYGZmlutK5rt8yD19584d6X1uq345eYc5W+C5uadfvHjB69ev8fDwUAsEMzIyiIqKwtnZmU2bNrFmzRqGDh2KUqmkY8eOjB8/HgMDg0/O/W2Ee/rrk18deAUVobdmEXprFqG35sivWhdI9/Q/5eLFi/j7+2Nubo6xsTGXLl0C4NixY1IuY3p6Ovr6+ty9e5chQ4ZIY62srHj27BmQbXbp0qULixYt4sGDB8jlcg4ePIi9vT0dO3akW7du+Pn5sXr1ahwcHHB0dFSbx8qVK6lfv770vmXLlsTGxlKoUCEiIiLUzo2IiFALFL/UPW1ubo6BgQHr1q2jdu3a0ucPHz6kZMmSJCQkoFQqCQgIICsri8uXL+Pu7k65cuXo1avXJ6//NtrqCtNGhNaaReitWYTemkXorTm0VWutck//U+rVq8fZs2fJzMxkxYoVpKen8/TpU2bPno1KpUJXV5eQkBAguzROTjtAyK7puH37dq5du0ZWVha7d+9my5YtmJubU6FCBdLS0nj16hWpqakEBAR8NJAbMmSImlPb0NCQixcv4ubmxqpVq7h58yZKpZJDhw5x4sQJOnTo8I+fXS6X07lzZxYuXEhMTAxZWVns3buXtm3b8uTJE6KjoxkwYAB//vkncrlc2q4X7mmBQCAQCASQj1Ya33Y0Gxsb06VLF9zd3dm9ezdbt24lKiqK9PR07O3tmTNnDhYWFvj7+0t9nENDQ7GwsGDIkCF069YNyK7D2LBhQ44fP07x4sWZOHEi/fr1Y9++fcyYMQM/Pz+KFClCVlYWa9asoVq1avj6+jJ58mRkMhnFihUjLS0NgLNnz2JjY8P48eOJj4+nYsWKJCcn8/fff9OtWzf8/f3x8/Nj7dq19OzZk0KFCjFs2DAKFy5MSkoK8+bNo3Tp0gAsXboUe3t7tm7dCsDdu3epV68eu3btIiUlhT59+qBQKNDR0aFy5crSSmNmZiaTJk3i3r177N69W6oN6eHhgaGhIUePHgUgPDycixcvsmXLFl6+fCkFsBMnTmTu3Lm0bNmS1NRUdHR0qF+/PjY2NpiYmDBlyhRGjRpFcnIyMpkMS0vLT+Z6CgQCgUAg+G+QL4LGdx3NMTEx9O7dGyMjI5YtW8bGjRupWbMmMTEx9O3bl40bNzJ69GgguwWfp6cnvr6+nD9/nqFDh2JjYyNt/167du09t3GVKlVYuHAhTk5ONGjQgNmzZxMREYGrqyuVKlVi165d6Onp0b17dypUqCDNs3z58pILG5DqLZqYmGBhYcHIkSPp2LEj06ZNo2zZsgQEBGBqaoqXlxf79+8HYNOmTdK4xYsX4+/vT3h4OJs2bQKyt9CrV6/OggULKFy4MIsXL6Zfv34EBwezdetWnJycSElJ4ezZs6SmprJhwwYCAgLw8/PDx8eHhQsXsnz5cvr27cvp06c5ffo0I0aMICoqCisrK27cuEGLFi2YNm0aGRkZTJo0iWnTpuHr64uuri6lS5dm06ZNGBsbs2TJEry8vDhw4ECevk/hnv765FcHXkFF6K1ZhN6aReitOfKr1lrlns5xNI8YMQKZTIaNjQ3r16/HyMiI1q1bY21tTVJSEnFxcVhYWBAbGyuNtbOzo3///gA0bNgQZ2dn9u/fLwWNubmN32bAgAEYGRnxzTffULx4cTp06CCtrtWuXfuTBpl3ycjI4MCBA/j7+2NlZQXAtGnTCA4O/uTYiIgIwsPDCQkJkbrAjBs3jqCgIE6fPk2tWrUAcHV1RV9fXzKbVKxYkZYtWwLQoEEDVq9ezdChQ9HT05PyKaOjo0lISODmzZusX79eKgE0ceJEWrZsyS+//IKhoSGRkZHs2rWLxo0b4+Hhwc8//5yn5wfhntYk2urA01aE3ppF6K1ZhN6aQ1u1zhdBY46j+e08wPLly5Oens6CBQsICgqiUKFC2NnZoVAopF7KAGXLllW7lpWVlbSiCLm7jd/mc/s2fy4vX77kzZs30lY0ZK9Efk5uYE5/6rcd1Do6OlhZWREVFSUFje8+09vPkOOMzukXnfM+KyuLyMhIlEolTZo0URuvr69PREQEbdq0ISMjg507d+Lr60vRokUZOnQoPXr0+NzHB4R7WhPkVwdeQUXorVmE3ppF6K058qvWWuWetrS05NmzZ6hUKilwPHbsGHfu3OHs2bMEBQVRrFgxAIYOHao29u1VR8juEZ2zwgefdhZ/bt9muVwu5TcCvHjxItfzihYtiqGhIdHR0ZQvXx6A169fS32w393qvXr1KpcuXeK7776Tel8/ffqUSpUqAdlfZHR09Ef7T+el97ShoSHnz5+XflnT09OJiIjA1taWR48eUa1aNVxdXUlNTeX3339n4sSJ1KtXT5rP56CtrjBtRGitWYTemkXorVmE3ppDW7XOF+7ppk2b5upoDgwMRFdXFz09PTIzM9m/fz+hoaFkZGRIY69evcr+/fulPs/Hjx//13o1v02FChW4ePEisbGxH3VI57iU/f39iY2N5c2bN8yZM0fKF3BxcVE7//r165QqVYoLFy7www8/0KRJE7y9vYmPjyc1NZUFCxagVCpp1qzZP36GmjVrYmtri4+PDykpKaSmpjJ79mz69euHUqnk5MmTjBw5ksjISAwNDTEzM0NXV5fChQv/43sLBAKBQCDQbvLFSqOpqSlr165lzpw5Ui5jr1696Nq1K56enjRr1gwDAwOqVq1Kz5491UrhVKlShePHj+Pt7U2xYsWYP38+derUyfU+V65cAaB+/fqkpKQA2SuT4eHh7Ny5k5cvX+Lt7c38+fNxdHQkKysLuVzO33//zdGjR3n+/DlNmjTBwMCAIUOGUKpUKTIzM1m8eDExMTHMnDmTQ4cOMXbsWF6/fk3r1q1RKBTStrKfnx9dunSR5tOgQQOSkpJ49eoVffr0YePGjVIf6kaNGgFQunRp1qxZg5mZGQEBAQAMHz6cmJgYAgIC2LBhA2ZmZri4uPDkyRNpa3vw4MFcvHhRbdVVV1eXwYMHM3PmTDZt2oRMJqNChQqsX78eAwMDfvjhB3777TeaN28OZG+rz5kzB0tLyzx9n8II8/XJr8nUBRWht2YRemsWobfmyK9aa5URBrKDv40bN773+YoVKz46rnDhwixZsiTXYydOnJBe5wRZy5cvx9HRkcTEREaOHMnu3bupX78+ly9fZvTo0QwaNIjbt2/Tt29fvL29adOmDT/99BMNGzZk27ZtJCYm0rdvX4yMjDhx4gRz584lLCyMw4cPS/2cBw0axN69e5kzZw52dnaYmJgAMGXKFO7du0fp0qVxcHDg7NmzODo6Sq7rhw8f4u7uzrhx4+jRowfPnj1j1KhR7Nu3j+rVq2NqaopcLmfSpEnUrFkTAwMDTE1N0dfXZ9WqVZiYmNCpUyeMjY0ZPnw41apVY8KECRQrVgwHBwfu3LnDlClTmD9/Pk5OTvz1118MHz6cZ8+eUbFiRby9venatSsjRoyQnjMhISHP36UwwmgObU2m1laE3ppF6K1ZhN6aQ1u1zjdB49fGwsKCkJAQbGxsUCgUxMTEYG5uLuVEGhoaMnToUGQyGTVr1sTOzo5Hjx4BYGBgQGhoKBUqVKB+/frs379fMsnk1s951apV/Pzzz6xZswbI3rIuV64c1atX5969ex+cY1BQEHZ2dvTt2xcAW1tbxo4di7u7O5MnTwayTTJvd5MB6NSpk7QaWLNmTRQKhbTa2rBhQ5YvXw5AYGAgTk5OtGjRAoC6devStWtXtmzZQqNGjT74nHlFGGG+Pvk1mbqgIvTWLEJvzSL01hz5VWutMsJoAj09PYKDgwkMDEQmk/HNN9+gUCjQ1dXl+fPnFC1aVC1HUU9PT3JO+/n54e/vz6JFixgzZgx169ZlxowZWFhY5NrPGSAxMVFyKcfHx7Nq1apPzjEhISHX3tOpqanSil9ubvB3HeA5zmlQd4BHRUURFham1o1GqVRiY2Pz0efMiwkmZw756Q9DQUZorVmE3ppF6K1ZhN6aQ1u11uqgcdSoUZ997qFDh9i8eTPbtm3D1tYWgFmzZnHlyhX8/PyklTpPT0+1cVlZWdy6dYtRo0YxefJknj17xpw5c/D09GTnzp0f7edsbGyMnZ0df//9txSY7d27V3JSq1QqXrx4wbRp01i6dCldu3aVurrk8PTpU/T19aVA8Et7T0O2e7pDhw7MnDlT+iwuLg6VSvXR59y9e/dnXV8gEAgEAkHBJV+4pzVBcnIycrkcQ0NDVCoVZ86cYd++faSlpX00AVQul+Pt7Y2fnx9paWlYWFhgYGCAubn5J/s55/D29Tt06CDVbIyLi+PNmzcMGTKEEydO0LZtWx48eMCGDRskF7mvry/t2rX7V7Z7O3fuTHBwMH/88QdZWVk8fvyYn376iXXr1n30OQUCgUAgEAi0eqXxXT7Wv3rbtm28fPmSxo0bo6urS5UqVXB1dWXz5s1AdseUHHd1QkICf//9N9euXWP79u107tyZq1ev0rBhQzIzMzEyMkJHR4eaNWtSunRpKlasSM+ePXnx4gVv3rzB0dGRvn37Sq5pgDp16rBu3Tp27txJYmIit27domfPngCsXLkSlUqFu7s7vXr1wtfXl9mzZ6Ojo0ODBg2YNm0aAHv27CE9PZ1mzZqhUqlYuXIlUVFRXL58GX9/fxITEylevDi2tra4uLgQERGBlZWVFLTWrFmTtm3bMmzYMNLT09HV1aVVq1aMHTsWyA4qFy1axObNm5HJZJQtW5b169fn+XsQ7umvT3514BVUhN6aReitWYTemiO/aq117ul/Sl77Vzds2JDRo0fTokUL+vTpw507dwDYvn07YWFhrFy5kvr167Nv3z4mTZrE6dOnKVmyJK1bt8bJyQl3d3dUKhULFizg8OHDnD59msjISJycnChWrJjUG/r27dv06dNHCkhtbW2xtbWlatWqBAcH4+TkxO+//461tTV79uwhMDCQFStWYG9vT3h4OCNHjiQ0NJQff/wRa2trnjx5QmBgIEZGRrx69QqAlJQUDh48KPXPLlSoEKtXr5b6Z+eU+dm6dSuhoaHs3LmTChUqsH//fubOncurV68wMTFh4cKFrF69GgcHB27dukWvXr2kbj15QbinNYe2OvC0FaG3ZhF6axaht+bQVq0LTND4T/pXv0uDBg344YcfAGjTpg2enp5ERERQsmRJVq5cScmSJVGpVERFRWFqavretd7tDf257N69m27duknu6Pr169OtWzcCAwP58ccfAWjcuLHUGzsnaPzc/tlbtmxhyJAhVK5cGcheWdy1axcHDhygZ8+eGBoasmvXLrKysqhbty6XLl0S7ul8Sn514BVUhN6aReitWYTemiO/av2fck+fP38eT09Pateu/UX9q9/lbTdyTvCTs3R7584dhg8fTnx8PBUqVMDCwgKVSkXv3r2lYCw3h3OdOnVYvXo1kN0i8cCBA9StWxfILtb99OlT5HI5bdu2VRtnbW2tVm/yc9zTH+qfHRUVxdy5c1mwYIF0PDMzk+rVq2NoaMi2bdtYtmwZY8eORaFQ4OzszNSpU9Xc2J+DtrrCtBGhtWYRemsWobdmEXprDm3VukAEjTl8af/qzyU2NhYPDw+WLl2Ko6MjAIcPH+bIkSNq5+XmZs7Znt61axfNmzfHxcWFyMhI6brh4eEMGzaMp0+fqo2LiIj4aN/pD32WG5aWlri7u9OmTRvps6dPn2JmZoZCoSAuLo6FCxcCcPv2bcaMGcOKFSuYOHHiZ11fIBAIBAJBwUXrgsbczC4ODg5AtkPawcGB9PR06tWrx/3798nMzMTCwoLly5dz7tw5IiMjSU9Pp2zZsqhUKgwMDAAYO3Ysly9fJjExEScnJ06cOMGqVaskF/Svv/7K+PHjUSqVGBkZsXPnTvz9/YmLiwPg9evX0hznzp2LTCbj77//Jj4+HgA7Ozup483hw4ext7eXgsakpCQcHBzo27cvGzZs4NmzZ1y5coVXr16RmprK8OHDpWsvXbqUpKQkgoKCpJVNDw8P7O3t2b17N8nJyZw7d47MzEyWL19OXFwc1tbWAHTt2pWAgABOnDjBxYsXSUlJISUlhdmzZ/PDDz8wePBgmjZtyvXr10lJSSE9Pf2LOsIIBAKBQCAoeGhV0Pghs0tO/l7Tpk2Jj4+XVhd/+OEHFi5cSI8ePdiyZQtFihTB3t6ewoULc+jQIcLCwqhVqxYmJiYEBwczd+5czpw5Q2ZmJh4eHixevBhHR0fs7OwAOHDgABMmTMDDw4OkpCRKlSrFxIkTmTdvHjdu3JC2mwFCQ0PZvn07RYoU4eeff+bSpUv89ddfas/TsWNHAgICKFGiBGfOnAEgLCyM48ePI5fLsbS0pHLlyqxfv55OnTpJ454+fcqpU6d48uQJ7du358aNG7Rr146wsDC+//57QkJCUKlUHDx4kNGjR3P69GkuXLhAv3792LdvH8eOHUMmk1GiRAns7OzYvn07rq6ujBgxAn9/f/T19TE0NKRKlSqcOXOGjIwM9PT0Pvt7Eu7pr09+deAVVITemkXorVmE3pojv2pdIN3THzK75CRvTps2TaorOHbsWAwNDTEzMyMwMJCMjAyKFi1KfHw8Dx484Ny5c8TGxlKoUCGqVq2Kra0trq6uuLq6kp6eTkxMjNRycN++fQQEBBAbG8v8+fO5f/8+6enp0lZu//796dChA6ampty9e1fKr/zmm2+AbNeynZ0dtWrVws3NjfDwcCA7X3HOnDksXboUgLS0NG7cuEFAQIDUTQb+r77ipk2bsLOzo23bthgZGVG5cmXmzJnDrFmz6Nu3LzKZjF27dvHjjz8ycOBAjIyMWLlyJU2bNiUqKory5ctz7949Dh06RPny5QF48+YN9erV4+bNm3z//fcsW7aMQYMG0axZMypXroxcLv/s7e8chHtac2irA09bEXprFqG3ZhF6aw5t1Vqrgsac8i/vml1ytoDfLkStp6cnRc5v3rxh5syZXLhwAUtLS6pWrSp1QcnhbYPJx1oOQnYOYrVq1dTm9m77v9wMK58iKSmJjIwMaTs5B2tra2krO7drm5mZSZrkuJ3fNcNkZWVJLuquXbuqjdfR0SEyMpKWLVvi7+/Ppk2bWLNmDYaGhvTu3Zthw4blyUUt3NNfn/zqwCuoCL01i9Bbswi9NUd+1bpAuqctLS1zNbsoFIoPjjl48CATJkxApVKxaNEiWrRoQVZWFt99953aeXfu3MHR0ZETJ058sOXgvXv3pHlERESojY+JiVHr0fyh1bk2bdpI+Y9xcXEEBAQQHR3NuHHjmDdvHgYGBkRERFChQgVpzNOnTyXjTW7X/tyVwJxt/EOHDqmZa+7fv0+ZMmWIjo6maNGirF27lvT0dP78809GjhxJtWrVaNq06WfdA7TXFaaNCK01i9Bbswi9NYvQW3Noq9Za1UawadOmZGZmsmLFCqnN3uzZs0lLS/vgmJ07d2JmZkb79u1p1qwZCoWCefPmoVAoyMjIyHXMh1oO5pzfqVMnjh07xsmTJ8nMzGTv3r3v5St+iJCQEAoVKgRk5y8mJiZiZWXFggULkMvldOrUCV9fX548eUJ6ejobNmzg/v37ao7nL6VkyZI0bdqUX3/9lcTERDIyMli+fDmdO3fm1atXXL9+nUGDBnHnzh309fUpWrQogGglKBAIBAKBQLtWGk1NTVm7di1z5syRCnf36tWLsmXL5nr+qVOnSEpKQi6Xc+DAAYKCgsjKykJHRwe5XM7SpUsl5/XbdOjQgUOHDtG0aVOysrLQ19fHwcGB69evk56eTmJiIiVLlmTYsGEAVKhQQSoG7unpyZUrV0hNTaVOnTpYWFjQt29f6dqOjo6kpKRw9uxZjhw5QkZGBq9fv+bcuXPUrVsXPT09IiMjcXZ2RiaTUbFiRdauXUu5cuUkQ87w4cP57rvvaNGiBStXriQpKQl7e3vkcjm9evUCoEePHrx+/Vot2FQoFBQvXpzg4GAOHz6MXC6ncuXKrFmzhuLFi3Pv3j0yMzNxdXUFwMLCgsmTJ6u1QxQIBAKBQPDfRKuCRoAqVapIpWve5u7du2rvfXx8AOjduzf29vZ06dKFH3/8EX9/fxwdHUlMTGTkyJEEBASwadMm9uzZI7USjIqK4tKlS8yaNQtXV1fu3LlDnz59WLx4MZcvX2b06NHMmzePFi1acPfuXYYPH06XLl3o168fnp6eREVFsXLlShwcHNi1axczZ87kzJkz0vbwxIkT6dixI3v27GHp0qVS8W5PT09u3brF3r17sbKyYtu2bSxevJjSpUtLz9WkSRMWL15MRkYGx44d4/Hjx0yaNIk+ffoQGBiIt7c3bdu2xdvbm/v379OtWzc2bNjAd999h7u7OykpKRw/fhxDQ0N8fHz4+++/+fbbbwkLC2P79u3S1vX27dvx8/Ojc+fOef6OhHv665NfHXgFFaG3ZhF6axaht+bIr1oXSPf0P8HCwoKQkBDJER0TE4O5uXmu7QRDQkKoVq2aFDBVr16drVu3UqJECXx8fHBycqJ169YAVKtWDTc3NzZt2kS/fv0AcHBwoEGDBkD2Vvb06dN5+vSpFDTmRlpaGsHBwQQEBEh5lH379iUoKIjg4GDc3NwAJOe0kZERAIUKFZKc0w0bNkSpVErO6Ro1alCiRAnJOX348GEOHTokbTtPnjxZck4bGBiQlJTEjh07aNasGV26dKFbt255dk6DcE9rEm114GkrQm/NIvTWLEJvzaGtWv9ngsZPOaLfJi4ujlKlSql9llNIOyEhgSpVqqgds7a2lpzJgJrJJKe+4dtO7dwoKM5pEO5pTZBfHXgFFaG3ZhF6axaht+bIr1oXSPf0P+FTjui3sbKy4vTp02qf7d69m6JFi1K6dOlcW/2Zm5tjZ2dHixYtMDY2zvP8ihUr9kXO6bi4OA4cOICLi8t714yMjCQqKkrKwYSv75wG7XWFaSNCa80i9NYsQm/NIvTWHNqqtVa5p/8Jn3JEv02bNm24desW+/btQ6lUcuPGDXx8fNDV1aVTp05SWR6lUsmtW7dYvXo1rVq1+kfz+1LndMmSJXMNGHM7T1PO6ccJKdyPS1b7ea74sMNdIBAIBAJB/uc/s9LYoUMHLl26RJs2bdDR0aF8+fJ06NCBTZs2sX79epYuXUpKSgrjx49n+vTptGrVilmzZjFp0iQABg4cSLVq1fD19cXIyIixY8eiUqkoXrw4PXv2pHXr1vz2229ERETw9OlTzpw5g6OjI56entIcjh07RlxcHDNmzGDlypVUq1YNlUoFwN9//82dO3d49OiRFIBWq1aNtWvXUrJkSWbOnAnA0KFDqVevHjNmzACy60Pu2bOHjh07SvUfXVxcMDY2pnv37moaTJw4kaFDh/LDDz+gUqmwsLDA39+f4sWL4+zsTHBwMJ07dyYjI0Nybr9btPxzmBV8hwyV+mfertUpZmKQ52sJBAKBQCDIHxT4oHHTpk3S63nz5qkdi4yMZNOmTZw4cYKjR4+iVCoZMWIEXl5e2NjY8Pr1a9atW0fNmjUxMDCgf//+yOVyDh48SOHChVm8eDGHDh3ip59+4uXLl0B2juG6devIyspi+PDhzJ49m7t37xIWFpar67p///4AeHl50aBBA7Zu3UpiYiJ9+/aldevWfPfdd3h6evLgwQNOnTpF0aJFmT59OmPGjGH79u1SC0KANWvWUKdOHQICApDL5YwdOxYAZ2dnsrKypPaGe/bsISMjg0mTJrF3714aNWrEtWvXCA0NZevWrdSsWZOYmBj69u3Lxo0bGT16dN5EV6lQqWTvfKTKd24xbSa/OvAKKkJvzSL01ixCb82RX7UW7uk8MGnSJCwsLABwd3dn2LBh9OvXjzJlylC/fn0gO28xPDyckJAQKSdw3LhxBAUFcfr0aamWoaen53vX8vb2Zs+ePR91XRsYGBAaGkqFChWoX78++/fvRy6Xk56eTkhICMuXL8fKykqa75MnT9SeIT09nUOHDrFixQppa3nChAm0b98egBs3bnDz5k3Wr18v5VxOnDiRli1b8ssvv/DNN98QHByMtbU1SUlJxMXFYWFhkau7/FOkvE4h7Z3fv2SFgquRf+f5WoKPo60OPG1F6K1ZhN6aReitObRVa60NGpOTk8nIyJACtLyiVCp59uwZgGSMgWwTTHp6OklJSWpO5efPnwPqPaZ1dHSwsrIiKipKChrfdj/nXOvly5efdF37+fnh7+/PokWLGDNmDHXr1mXGjBmYmZmRnp6u5uY2NTWlRo0aatdKTEwkPT1dCizfnWtkZCRKpZImTZqojdPX1yciIoLKlSuzceNGgoKCKFSoEHZ2digUCmn7PC8YFzJG752VxsImJpQvWzvP1xLkTn514BVUhN6aReitWYTemiO/al3g3dM//vgjixcvzrWjy+fw888/S47ia9eu0a9fP44fP05kZCRGRkaYm5tLTuUDBw4QEBAAZLuZc3pMK5VKoqOj1dzIsbGxmJiYANmBWqFChbCwsJBc14MGDaJevXoMHTqUBw8eIJPJqFWrFra2tmzZsoXJkyfz7Nkz5syZg6enJzt37kRfX59nz55Rvnx5ILvsz+rVq9W2jc3NzSX3dc55MTEx0nFLS0sMDQ05f/689Iuanp5OREQEtra2rFu3jrNnzxIUFESxYsWA7PzJL0Im493qjjKZLF/9ASkoaKsDT1sRemsWobdmEXprDm3VWmvd04mJif/a+NWrVwPZq4lLliyhffv2avUbXVxcOHz4ME2aNMHb25v4+HhSU1NZsGABSqWSZs2aSefOnz+fpKQkYmJiWLx4Md26dQOQXNedOnVi8ODBkus6LS2N06dPo1Kp8PPzIy0tDQsLCwwMDDA3N0cul+Pq6oq/vz+xsbGkpaXh5+fH1atXMTQ0lO6rr6+Pq6srixcvJiYmhuTkZObPny8dr1mzJra2tvj4+JCSkkJqaiqzZ8+mX79+KJVKqWalnp4emZmZ7N+/n9DQ0A/25/4YM1yq4u1aXe3HrJCo2ygQCAQCgTaT71ca/f392bVrF2/evKFMmTIMHz5cMrQMHjyYUaNGUbRoUTZv3oyZmRnXrl1j+vTp7NixA3t7e0aNGgVkr/o5OTlx/Phxli9fzsWLF7l8+TKAtPXbt29f9PX1uX//PsWKFZNW3HLa/e3Zs4cFCxbQpk0bkpOTkclkFC1alN9++00qe/PmzRsaNGggFdWOj49HT0+PsWPHsnjxYsaNG8e4ceMwNTUlKSkJgGbNmjF48GDOnz/Pd999R3p6OnK5nEqVKhEaGoqnpyeLFi2iadOmUjBra2vLn3/+SUxMDGfOnGHhwoWkpqZiY2NDy5YtSUtLU1tR1NXVZenSpQwdOpTNmzeTlZWFubk58+bNw8DAgAEDBnDy5Elp5dbIyIjvvvsu1zqWnyJTmUXFkibvfZ7fEn+1mfyaTF1QEXprFqG3ZhF6a478qnWBMMLk9EPes2eP1A95ypQphIaGUr16dVavXo2DgwN79uzh5s2b+Pj4sGLFCrKystixY8cHr/vrr7/y9OlTKY/P1dWVnTt30qhRI+bMmUNWVhb9+vXDzs5ObZyZmRkDBgxg//79eHp60qNHD549e8aoUaNQKBTcvXsXf39/zp8/j4+PD+3ateOPP/5gyJAhODk54eTkRPXq1aVg9vz58/Tp04crV64A2V/a48ePWbZsGXZ2dhw5coThw4ezZcsWpk6dyokTJzA1NZV6b9++fRulUknJkiU5deoU586dw83NjQYNGrBo0SJevXpFp06duHbtGuXLl2fr1q3o6+tz5MgRSpQowerVq/Hy8uLgwYMkJyfz+PFjDh48SPny5QkNDWXEiBEcO3Ysz9+b4nUKV68+zPM4Qd7R1mRqbUXorVmE3ppF6K05tFXrPAWNa9asoWfPnhQqVOhrzUeNvPRD1tPTo3379nluefc2Q4cOpXDhwgBSGZp3CQoKws7Ojr59+wLZK35jx47F3d2dyZMnA1C2bFlcXV0BaNKkCcWLF+fx48fUrl37o/ffvXs3bm5uVKtWDYDWrVtz+PBhdu3aRc2aNYHs8jlvtwkEGDJkCHp6ejRs2BCAHj16UKRIEYoUKUKlSpWIjIxEpVIRGBjIkiVLJIPMiBEj2LFjB6dOnaJGjRrSOc7OztSvX5+rV69+kZ4mhYypWO7jzyr4Z+TXZOqCitBbswi9NYvQW3PkV62/ihFm1apVUl1BTVCnTp0P9kN+l+LFi39WgBMfH/9ef+cczMzMpNd6enq5LtcmJCS8V/Da2tqa1NRUEhISpLm8jZ6e3id7T0N2TmVu175z5470/t3e0/B/XVsiIiKA93tPq1QqXrx4wevXr/Hw8JB0ysrKQqlUEhUVhbOzs6Tz0KFDUSqVdOzYkfHjx2NgkLei3DIZ+eoPQ0FGW5OptRWht2YRemsWobfm0Fat87SM1KhRI1avXk1cXNzXmo8ab/dDDg8PZ+7cuaxYsYIzZ868d+67q49yuVzNxJFjfLl06ZL0mampKXfv3sXS0lJtrL+/P3v27Ml1Trn1nn769Cn6+voUKVIEgPDwcM6fPw9ktyTM6dTyLvHx8UB2vmXOtXMCvxwiIiLUAsXcVlllMhm3bt2ibdu2ud4H/s9dvW7dOi5evEiXLl1o0aIFe/fupVu3biQkJKBUKgkICOD8+fOsWrWKgwcPsmvXrg9e80MUMRSmF4FAIBAIChp5ChovXbqEn58fTZo0oUqVKmo/X4OP9UPW19cnOTn5g2MrVKhAaGgor169Ijk5WXJI5/Cp8R+iTZs2PHjwgA0bNpCens7Tp0/x9fWlXbt26Ou/HyyFhIR89nZ+ly5dWLVqFTdv3kSpVHLo0CFOnDhBhw4dPjk2p27lh5DL5XTu3JmFCxcSExPDixcvePLkCW3btuXJkydER0czYMD/Y++8o6K81rd9zcDQFRQRUOwFjSVojGjsEMUINlCx9y7F3is27AWxgsQWRREL9ihqNFHRxBJ7jEaaICJShYFhvj84vJ8TKznH+QHZ11pnnSlv2e89uNaTvff93IO5dOkScrlcakf0T7KnSxkrCnyOQCAQCASCwk2Blqf/HsP3uXFycuKvv/5i1KhRJCUlYW5uzvTp0/nyyy9xd3dnwoQJDBw4UKM5dz4jRoxgxowZODo6UqJECalwW716NQcPHuSPP/7gxo0b3L59W3quJUuW4OfnB0BOTg6PHz/G3t5eakMDecvFlStXJigoCD8/PwwMDGjVqhUhISGMHj36rXE4ODiQnp4OQFpaGn/++Sc3btxg79690h7EfDp27CjlP6vVakxNTVm0aBGNGzfmypUrxMXFcfbsWVatWkVWVpbULzIqKophw4ZJ1/njjz+oU6cOvr6+3Lx5k99++429e/fi7u7Ol19+iYuLi1Qwly9fni+++ILffvuN8uXLM2TIEFQqFbq6unTt2lXKwS4IKpWq0DnDihuF1YFXXBF6axeht3YRemuPwqr1Z3FPN27cGIDk5GSioqL44osvyMnJeecM2/+KESNGMGLEiLc+nzlzJjNnzpTeu7q6anxvaWlJQECAxmcODg54eHhQvnx5+vfvr7FU/ffZvOfPnzNr1izc3d2JiIhgxIgRXLt2jUaNGlGiRAkcHR012vnkL+N6enpq5EFDXlyfq6srkydPpmLFim9lQ0Pe/kJPT09q1qzJzp07pWzoc+fOSe18VCoV5ubm/Pjjj8THx9OnTx8mTJhAhQoV2LJlC/379+fBgwcAzJ07l+joaC5evEiJEiU4deoUXl5enDp1iokTJzJ16lQAfH19yczMZNSoUXh5edGrVy8yMzOZPn06jx8/fudy+Me4e/dugc8R/DOKqgOvqCL01i5Cb+0i9NYeRVXrAhWN6enpzJ49m6NHj2JgYEBoaCiDBg0iKChISiEpLtSuXZu+ffsC0KxZM5ycnDh06BCNGjUC8rTIyMj45KXn/zYbOp8xY8ZgYGBApUqVsLe358mTJ++8n6enJzo6OpiYmBAXFyeZWZ4/f/7WzKxCoSA4OJhKlSqRlZVFTEwMZmZm//iP+osvvvis/yEhKLwOvOKK0Fu7CL21i9BbexRWrT+Le3rp0qVkZGRw/PhxevToQYUKFWjTpg0LFy4kMDDwHw+2MPJ3h7W1tbXU6Do7O5udO3fSt29fjIyM2LVr1wevtXnzZh48eIBSqSQkJISTJ0/i4+Mj3cPZ2RkjIyNycnLemw2dz5vObIVC8d5s6MTERBYuXMjdu3exsbGhbt26AO90cevo6HDlyhWGDRtGRkYG1atXR1dX9x/lTudfrzD9YyjOCK21i9Bbuwi9tYvQW3sUVa0LVDSePXuWsLAwTE1NkclkKBQKpk6dSsuWLT/X+D4Lbyal5M+IJSUlaZg+/u4Qj4qKonz58gCo1WoN00mHDh3YunXrB+9paGiIvr4+Xbp0kQrDO3fuALBq1Sqpcfj7sqHfXEr/FLy9vXFwcCAwMBBdXV2SkpLe2/D85s2bzJ8/nz179kjF5datW987iykQCAQCgeDfR4GKxtzcXKnIyp+FevOzwk6+Y7pixYro6upy9OhRunbtyi+//MLly5c1TB+3bt3C39+f69ev8+uvv5KRkUH79u1JTU2VpnCdnZ2ZPXv2O2dZJ0yYQHp6Omq1GnNzc3R0dOjSpQuTJ0+mSZMm9O3bl4EDBwIwbtw4Bg0aRKVKlRgyZAjx8fHEx8dLs30XL16Urjt16lSuXLmCWq2mdu3ahIeH06RJEylneuLEifTs2ZOnT5+ybds2bt++zZw5c/D39wfg+PHjzJ49m5iYGIyNjbl9+zapqanI5XJ+/fVXpkyZQkxMDEqlssD9GQUCgUAgEBRfClQ0NmnSBB8fH2bPni0ZJFavXi0ZZAo73bp1Y9WqVfz+++9Mnz6d9evXM3/+fJo0aYKrqyuvX7+Wjv36669Zv349kJfwMnToUA4ePMjkyZP54YcfcHd3R1dXF39/f2rXrs2ff/4JwKVLlwAYPHgw/fv355tvvpH6MM6YMYOIiAjOnDlDREQEAwYMICgoiMDAQBo1aoSpqSnLly/HyMgImUyGjY0NT5484aeffpIadv/yyy/s27cPQ0NDZsyYAcD58+c5fPgwQ4cOJSwsjOvXr+Pr68u6deu4cuWKlKRjZWXF0aNH2bFjBy9evMDLy4tu3bpx4cIFevTowaJFizAyMqJKlSp8/fXXbNu2jQsXLtCiRYsC6Szc05+fwurAK64IvbWL0Fu7CL21R2HV+lPHI1MXYONaYmIio0aN4u7du6hUKgwMDKhcuTIbN26U+voVF0JCQli9ejXnz5+XlosfPXqEs7MzwcHBuLu7c+bMGWxsbPDz8yMiIoIdO3Ywbdo0lEolK1askK7VtWtXHBwc8PT01HAtR0dH4+joKF2nT58+fPnll0yePFk6d+nSpTx48IDAwED69etHpUqVWLBgAYB0fkhICPXq1QPyYgsHDRokzWJOnjwZHR0dFi9eTIcOHejfvz89e/aUrt+zZ0/atWtH7969admyJa1atcLV1ZWGDRuiUCgKFCOoUqm4cePGP9JbIBAIBALB/y12dnYf3GtZoJlGc3NzgoOD+f3334mJicHKyor69esXyc2cHyMxMZFy5cppPFu+cSU/LvBdxMfHS9nR+fw9GhDgr7/+QldXU/73xQiGh4dL798VI/hm/KGOjs47YwQBYmJiWLJkCcuXL5e+z8nJoW7duhgYGLB7927Wr1/PhAkTSEtLw8nJiZkzZ0pJN5+KcE9/fgqrA6+4IvTWLkJv7SL01h6FVev/qXs6NjZW432ZMmUoU6YMkFckAZQrV66gYyzUlC9fntjYWFQqlfTD5scHXr9+/b3nWVlZvRUFGBcXJzXiBnj27BlDhgxh27ZtGsfZ2Ni8FVEYFRWl4ZiWyWSEhoaybt06tm/fDoCLiws3b9786DNZWVnh5eWFs7Oz9FlkZCRmZmakpaXx/PlzaYb03r17jB8/no0bNzJlypSPXvtNiqorrCgitNYuQm/tIvTWLkJv7VFUtf6ktUcHBwccHR0/+L/iRr7Defny5WRmZpKQkMDChQvR09OTiri0tLS3znNzc+P06dOcPXuWnJwcDhw48FZBp1Qq39nOplu3bgQHB3Pp0iVUKhWXL18mODgYNze3D4716NGjn/RMPXr0YMOGDdL+ywsXLuDs7MzVq1dJT09n2LBhhIWFoVarKVu2LHK5/B/FCAoEAoFAICh+fNJM45kzZwA4dOgQv/76K5MmTaJixYo8e/aM5cuXY2dn9znH+H9CiRIlCAoKwtfXVyog9fT0yM7OZvny5VSoUIHu3btTrlw5nj17BsD333/PgAEDWLp0KQsWLCA2Nha5XI6enh7Hjh3D3d2dhIQEbty4gVqtlmb8xo8fT4sWLfD09CQtLY05c+bw9OlTbGxsmDx5MvXq1aNfv35cu3ZN2lf5Jo6Ojjx48IDo6GhiYmK4evUq69atIzk5GUNDQ7766isABg4cyJMnT+jSpQtKpRKFQkHnzp2lon/q1KnMmjWLiRMnIpfLqVy5Mt27d9eW5AKBQCAQCAoxn1Q05vcnDA4O5vDhw9Iet2rVqrF06VLat2//zqi/ok6NGjXeaqeTH0XYrFkznJ2d6d+/Pz169ODp06eMHj0aAwMDevbsyfnz51EoFMyZM4esrCy8vLzYvHkzgYGB0vJy/l7Ffv36Sdfv3r07TZs2xdHRkW3btmFpacl3331Hy5YtCQgIIDIykmHDhiGXy7GxsWH79u30799fY4wpKSkcPHgQpVLJoEGDpN/rjz/+ICwsjBUrVuDo6MjNmzcZPXo07du3p0WLFpw4cYKhQ4cyZswYkpKSGDBgAAcPHmTQoEEF0k24pz8/hdWBV1wRemsXobd2EXprj8Kq9WfJnk5PT38rUSQjI0Oj0fW/hcOHD1OtWjX69OkDQPXq1RkyZAg7d+6kZ8+e6Ovrc/XqVY4ePUrTpk0JCAgokBM5n+vXr/Ps2TMmT56Mvr4+NWrUYNCgQW/th3yTYcOGSWYYBwcHaQ/mnj17cHR0pF27dgA0bNiQHj16sGvXLlq0aIG+vj4XLlygWrVqNG3alEOHDv2jMYvsae1RVPNLiypCb+0i9NYuQm/tUVS1LlDR6OjoyOjRo/Hy8sLa2pqoqCjWrFmDi4vL5xpfoSUmJoY7d+5IWdSQ1+g8f2PrzJkz2bRpE4GBgUydOpVatWoxc+ZMjeM/hfj4eEqVKoWBgYH0WcWKFT94Tr5JCdCIA4yJieHSpUsaY1CpVNL1Vq9ejZ+fH6tWrWL8+PE0bNiQuXPnaph4PgXhnv78FFYHXnFF6K1dhN7aReitPQqr1p8le3r27NnMmzePESNGSIkhnTt3lnoP/puwsrLC3t5eY/k6KSmJ9PR0IG+2zd3dHU9PT16+fIm/vz8eHh5cvnz5rWvJ5XKN2dqkpCSN1wkJCaSnp2NsbAzkubH/CfnpPdeuXQNg6NCh1KpVi379+pGbm8vdu3fx9PRk+vTpPHv2jMWLFzN16lT2799foPsUVVdYUURorV2E3tpF6K1dhN7ao6hqXaC1x3PnzjF//nyuXbvGTz/9xLVr1/Dx8flXzSrlRxF27NiRGzducPjwYXJycnj+/DkjR47E19cXgI0bNzJ//nzS0tIoWbIkhoaGkhNZX1+f169fk5OTA+TtDb1w4QIpKSmkpqayZcsW6X75s3wLFizg9evXPH369KM51++jfv36ZGRkcPHiRXJzc5k5cyanTp1i69atyOVyFixYwOrVq8nKyqJ06dLo6+sL97RAIBAIBAKggDON8+bNo127digUinc2mS6O+Pn5ERISwuvXr6lQoQJ2dnasWrWK8PBwLC0tmTJlCpMnT8bQ0JD27dszffp0Nm/ezNOnT4mMjKRRo0bI5XIaNGjAmjVrmDp1Krm5uWRlZVGnTh0sLS1xd3fH3NwcR0dHjIyMpEKtb9++dOrUCYDnz5/zzTffYGpqiq6uLs+ePaNNmzaSMxrgxIkTAKxbt45z584hk8mwsrLCyMiIK1euEBAQAOTteTQwMCA7O5s6depIzbyrVKlCSEgIO3fuRCaTUblyZYKCgrSsuEAgEAgEgsJIgYrGevXqcezYMTp37vy5xlOoyO+TGBoaioWFBcHBwaxevZrQ0FA6d+7MnDlz6NKlC/fv36d///506NCBCxcusH37dnbu3EnlypX5888/6d27N126dKFmzZoAHDt2jE2bNmFvb09ISAg+Pj6cPXsWS0tL+vTpQ6lSpdi5cyepqamMGjUKgMDAQDIyMnBxceG7777Dw8ODly9f4uXlxfDhwwEoXbo0AJUqVeLChQvcu3ePAQMGsGDBAuzt7Zk3b95bru3GjRujp6fHokWLSElJ4cKFC5QoUYJTp07h5eWFUqkssG7CPf35KawOvOKK0Fu7CL21i9BbexRWrT+Le/rVq1dMmTKFWbNmUaZMGWQymfRdfi/H4oS+vj7Jycns3buXNm3a0L17d9zd3fH396dOnTp069YNgLp16/LDDz9QtmxZ9PT0aNiwIVZWVrx8+ZKkpCTMzMyk5BwAe3t7mjVrBuQ1A58zZw6RkZHk5ORw7do1Tp48iYmJCSYmJnh4eDBmzBggb3uAUqlk/PjxyGQyrK2t8fb2xsvLiwkTJgBgYGDAyJEjkclk1K9fH1tbW548efLRZ/X09ERHRwcTExPi4uLQ19cH8mY4K1WqVCDdhHtaexRVB15RReitXYTe2kXorT2KqtYFKhr79u0LQEJCAjKZDKVSWeziA9+kQYMG+Pn5sWPHDgICAjAwMKBfv37Ex8e/9dy1atUCIDU1lVWrVnH27FlKly5N7dq1yc7O1kiAeTMWUKFQAHkmlXdFMr7plI6JieHly5d8/fXX0mdqtZrs7GwpD9vc3FyjmFcoFO9Mn/k7iYmJLFy4kLt372JjY0PdunWlcRUU4Z7+/BRWB15xReitXYTe2kXorT0Kq9afxT3dtm1bJk+eLC1vAjRt2pQ1a9YUfIRFgN9++40RI0Zw5swZypYty6VLl/Dw8GD06NGcP39e49j9+/djbm7O2bNniY2NJTw8HBMTEwA6duz4SfezsrIC8vKmq1WrBmg6pa2srKhYsaK0dxFg7969BAQESEvTf+fx48ekpKTg6en5wXt7e3vj4OBAYGAgurq6JCUlsXfv3k8a998pqq6woojQWrsIvbWL0Fu7CL21R1HVukDu6ZUrV5KRkcHRo0e5efMmhw8fRq1Ws2zZss81vv9T7t+/D8Cff/6Jnp4e5ubmAHzzzTfcvXuXgwcPolKpuH37Nr6+vujq6pKWloa+vj46OjpkZWWxdetWHj58+EkN0MuVK0fz5s1ZvHgxycnJJCQksG7dOun7Nm3akJ6eTkBAAEqlkpSUFM6fP4+JiYnG7OL7+Ltr+01SU1MxMDBAR0eHly9fsmDBAoB/ZeN2gUAgEAgEb1Ogmcbw8HBpRg2gZs2aLFu2jE6dOjF//vzPMsD/S1q2bAkguYsBKleujJWVFaVLl2bVqlXMnz8fc3Nzpk6dypIlS+jYsSOhoaE0bNgQHR0d1Go1X331Fb///jtjx47l/PnzZGVlcfXqVUaNGiXti8xv2XP9+nWys7P55ptvKF26NG5ubty8eZNhw4Zx8+ZNdHV12blzJ1u2bEGtVmNjYyMtTQO8fv0aZ2dnYmJisLe31yj6vv76a/T09Khbty6GhobA/5/dXLx4MT4+PmzYsEE6XqFQEBISQvPmzQukmzDCfH4K62bq4orQW7sIvbWL0Ft7FFatP4sR5vXr15QoUULjs5IlS/6jfW9FiSZNmrB48WJyc3MZOHAg/v7+9OnTh9DQUGmZ+vbt20RHR9OnTx/KlCnDtGnTWLRoEc7OzuTm5uLp6YlCoeCXX35BT0+P7du3M3/+fL777jvu3LlDhw4dsLe358KFC2RmZjJkyBBatGjBgAED2L17N9WrV8fPz4/U1FQmTpyIkZERGzZskHKsAezs7EhNTWXatGl06NCBc+fOceHCBWrXrg3AvXv3SE9PZ8eOHTRs2JCffvqJ8ePH88cff9CiRQsqV66MnZ2d1Htz+/btrF69WqOx+KcgjDDao6hupi6qCL21i9Bbuwi9tUdR1bpAReOXX37JmjVrmDhxIjKZDLVazZo1a6hXr97nGl+hYOTIkVKx3KJFC27duoWnpyerV6+WNrQePHiQ9u3bS8WVQqGgc+fOUnbzggULMDY2RqFQEBsbi7GxMZmZmSQnJxMdHU1MTAzTp0/H0NAQY2Nj1q1bR25uLmfOnEGhUDBx4kR0dHQwMDBg1qxZODs7k5CQoDHOY8eOUbduXam347fffkubNm2k73fu3EmvXr0kI02bNm1wcHBgz549zJo164NjLEjRKIwwn5/Cupm6uCL01i5Cb+0i9NYehVXrz2KEmThxIv369ePw4cOUL1+emJgYZDJZsW8AbWZmJr1WKBSoVCosLCxo0aIFhw4dolatWhw5cgQ/Pz/pOAsLC6lgBLh69Srbt2/nr7/+onLlylIbm9zcXBISEihVqpS0ZAxgY2MDwPHjxylXrpzGH1f+dzExMRrjfJeru2LFilIsYUxMDBEREezevVv6XqVS0aRJEyDPgLN06dJ3jrEgFNUNvkURobV2EXprF6G3dhF6a4+iqnWBisaaNWty8uRJzpw5Q2JiIuXLl6dVq1aSS1jbREdH4+joyJkzZ6RCSpu4ubkxb948mjVrRokSJTRa4bxpTPnxxx+ZOHEis2bNonfv3gwbNkzS7PXr1wQFBZGQkECvXr2kgu7atWvcvn2b8uXLExsbi0qlkv7AIiMjgbzC9PHjx9J9rKysOHfunMYY3+y5aGVlRZcuXaRm4ACxsbFSOsyIESMYP348vXv3RiaTcfv2bQ4fPvw/VEwgEAgEAkFRpUDuacibdXNzc2P48OE4Ozv/nxWMhYHWrVujUqlYu3Ytrq6u7z0uMTERtVqNgYEBMpkMHx8fqdh78OABv//+O1WqVMHW1pbXr1/z4sULFi9ezMuXL2nVqhUAy5cvJzMzk4SEBBYuXEiTJk0oX768xn06derEw4cP2bt3Lzk5OVy8eJEff/xR+r5Hjx5s376dW7duAXl7KlxdXTly5AjZ2dlkZmZKY4yNjZVc8cJBLRAIBAKBoEAzjYWVgwcPcvDgQTIyMnBwcGDq1KmcOnVKIzIP/n9snqenJzk5Ofj7+xMaGkpqaiq1a9dm1qxZ1KpVC6VSyYYNGzhw4AAA06ZNY8GCBdJybUREBAsWLCAsLAwTExPu379P7969cXV15a+//sLQ0JDs7Gxyc3O5evUqCxcuRCaTMWPGDHx8fFCr1TRs2JASJUowdepUAJ49e8b169dp1aoV2dnZ6Ojo8Ndff/HLL7/g4eHBjz/+SKtWrUhOTqZy5cokJyfTpUsXmjVrRlxcHAEBAQQFBSGTyVi9ejU+Pj7I5XJyc3O5cuUKSqWS9u3bk5KSwogRI6Ql60qVKtGyZUuMjIxYtGgRCxcuZMaMGUBeo3ATExMePnxIlSpVPvn3EO7pz09hdeAVV4Te2kXorV2E3tqjsGr9WdzThZVr166xd+9ecnNzGT16NIsWLaJRo0YfPGfDhg0cOXKEwMBAqlSpwrp16xgxYgTh4eGsWrWKy5cvs2PHDsqWLcuWLVsYPHgwx44dw9PTk3Xr1hEZGcm5c+fYsWMHp06dYt68eSxdupR27drx4MEDRo8ezfbt2xk4cOA7M5+/+uorvv/+e65cuUL//v25efMmAH5+foSEhLB+/XpsbW05deoUU6ZMYdeuXdSvXx8HBwcMDAykxtv37t0jICCAhIQEzp07xy+//MLw4cNp1qwZq1atIiUlBTc3N44dO0aXLl148uQJVlZW7N27V3q2IUOGcOzYMRo2bMjr1685duwYVatW5cKFC4wZM4YGDRoU6PcQ7mntUVQdeEUVobd2EXprF6G39iiqWheLonHq1KlSIoqXlxejRo2iYcOGHzznwIEDjBgxgurVqwMwatQoWrVqRW5uLnv27GHt2rVUqFABgDFjxrB3717OnTuHk5MTAK1ateKPP/4gODiYcuXK4ejoSIcOHQCoU6cOw4cPZ8eOHQwcOLBAz7J//36GDx9OnTp1AOjQoQMnT54kJCSE+vXrA+Dk5ETJkiU1zhsxYgQKhULqqdirVy9MTU0xNTWlRo0aREdHo1arP/hs9erVk45xcnKiadOm3LhxQ8PQ8ykI9/Tnp7A68IorQm/tIvTWLkJv7VFYtf4s7unCypsmGGtra5RKJa9evfrgOQkJCRpOYz09Pezs7EhMTCQjIwNvb2+NYik7O1vDrRwdHc3SpUvp3LkzMTExUhH25pj+7m7+FF68ePHOa+Wn0wCULVv2rfNKlSoFIP0RvllUyuVy1Go1L1++/OCzOTk5STnbI0eORKVS4erqyqRJkyQzzadQVF1hRRGhtXYRemsXobd2EXprj6KqdbEoGuPj4yVDTnR0NEZGRpQpUwalUqlxXP4+PsgrLp89eya9z87OZtmyZQwZMgR9fX22bt2KnZ2d9P3jx4+xtLSU3rdu3ZopU6YAMHv2bMnRnE9UVBQWFhYFfpby5csTFRX11rXeLBTfFRn4KTGCpUqV+uCzJSYmolKp8Pf3Jzc3l99++w0vLy+qVKlCnz59CvwsAoFAIBAIig8Fdk8XRpYtW0ZycjJxcXGsWbMGd3d3qlWrxosXL7h8+TJqtZpDhw7x559/Sue4uroSGBjIkydPyMnJYdOmTZw+fZrSpUvTrVs3VqxYQVxcHLm5uRw4cAAXFxeePn36zvu7ubkRHh7O8ePHUalU3L17ly1btkifb9q0idjYWL788kv69u1LZmYm9+7dw9XVlaVLlwIQFhZGfHw8+vr6LFy4kHr16uHo6MicOXMIDw+na9eu2NrakpaWxooVK2jQoAE9e/bUKDDv3LlDv379AJg0aRLff/89arUayJt6XrZsGfr6+vTq1Qt7e3t8fHwIDQ3FxcWFR48esXLlSvr06UPDhg0ZMWKElFGdP4spEAgEAoHg30uxmGls0KAB7du3Ry6X4+Liwrhx49DX12fUqFFMnTqV9PR0vv32W2k/IsDQoUPJyclhyJAhJCcnU69ePbZs2YJCoWDKlCn4+fnRu3dvXr16RYUKFVi7di1ffPHFO++fn5Tj7+/P9OnTKVWqFL169aJjx460b98eHx8fAgMDiY6OJj09nfj4eCwsLLhz5w7Dhg3j9u3btG3bFk9PT2rWrImTkxMHDx7k2bNnBAcHs2zZMho3bgzk9XUcM2YMrq6ueHl5sWfPHiBvtnXAgAGMGzeOiIgIxo4dy8aNGzEwMADg5s2bJCQkcODAAfbs2cOBAwfYtWuX9GwnTpzg7t27TJgwgX379nHp0iV+/vlnhg8fznfffVeg30O4pz8/hdWBV1wRemsXobd2EXprj8Kq9aeOR6bOn4oS/M9RKpXExcVRsWJF0tLSiIqKwt/fn5SUFLp06cLs2bO5deuWtL8wPj4eY2NjDAwMePbsGZcuXWLWrFmcPXuWcuXKYWtry8qVK3F2dgZg7969bNq0iTNnzrBlyxZOnz5NcHCwdP/g4GB27txJWFgYycnJZGdnY25uTkJCAn/++Sfe3t5Mnz6dzp0707BhQ9auXUuLFi0AUKvVtGrVihkzZmgU2x9CpVJx48aN/62IAoFAIBAItIKdnd0H91oWi5nGwopCoeDIkSPs2bMHmUxGzZo1SUtLQ1c3T/a/Rw1+SoxfmTJlpNe6urrS8nNMTAx37tzRaDWUm5sr/fivX7/Gx8eHq1evYmVlxRdffIFarSY3N/ejBpmCItzTn5/C6sArrgi9tYvQW7sIvbVHYdX6X+WeLixs3LiRa9euERAQAOTlRu/cuZPdu3dLBeD8+fM5d+6c1IgbYMmSJezZs4eMjAwmTZrEkCFDChzjZ2Vlhb29PYGBgdJnSUlJpKenAzBz5kxMTU25ePEi+vr65ObmSrGHHzPIFJSi6gorigittYvQW7sIvbWL0Ft7FFWti4URprAwcuRIqWAESE1NRS6XY2BggFqt5qeffuLgwYNYWFgwaNAg6bjt27fj4+ODrq4upUqV+kcxfh07duTGjRscPnyYnJwcnj9/zsiRI/H19QUgLS0NfX195HI5aWlpLF26lLS0NLKzs5HL5QU2/wgEAoFAIPh3IWYaC0h0dDSOjo7MnDmTjRs3kpmZiYODA3PmzCEoKIiIiAh27NgB5PV+zMzMpGXLlsjlcipVqsSAAQMICAhg06ZNqFQqGjRoQE5ODjNmzODLL7/E39+fuXPnolarkclk6OjosGnTJhYtWgTA5s2b2bVrF3/88YfUasjW1hYfHx9MTEyYMmUK06ZNw8jICF1dXR48eICbmxsjR45kw4YNNG7cGB0dHeRyOTo6OqxevZry5ctL5h83NzdevnwJQOnSpTlw4AA1a9aUltQFAoFAIBD8OxGVwD/k1KlThIWFoVKpGDNmDPPmzaNixYrS91euXGH27NmsW7eOFi1acPHiRUaPHk2HDh2QyWQaxaWtrS1btmzB3t6ey5cvM3To0HdGEj548ICpU6dy8uRJgoODsbKyomTJktja2hIWFsahQ4dQKpU4OztjaGhIUFAQ1tbWDBkyhPDwcEJDQzl//jwTJkzA39+fhg0b8tNPP+Hp6cnevXuZOHEiR48eZdGiRXTt2pXo6Gh69epFo0aNPtkMA8I9rQ0KqwOvuCL01i5Cb+0i9NYehVXrf1X29P8F06ZNeyu68M3IwIMHD9KuXTtatWoFQMuWLfnhhx8+ukcwNDT0o5GEdnZ21KxZU+O8vn37YmZmBkCNGjX44osvqFatGgBNmjTh119/BWDnzp306tVL2s/Ypk0bHBwc2LNnD7NmzUJfX5/jx49jZmbG119/zfnz5wscIyiyp7VHUc0vLaoIvbWL0Fu7CL21R1HVWhSN/5B8Ywv8/+jC5ORk6bPnz5+/1dcxPzv6QyQmJlK7dm2Nz/4eSfiuGEEzMzOeP3+OiYkJOjo6mJqaSt/lxwhCnss6IiKC3bt3S9+rVCqaNGkCwLZt2/Dz82PevHkkJCTQokUL5s6di5WV1UfHno9wT39+CqsDr7gi9NYuQm/tIvTWHoVVa+Ge/i/J37t45swZjWzrfOLj46latap0rKGhIaVKleLJkydAXiEZGxvL7NmzAfDx8XnLnQzg4OCg8b58+fIfjSR8V2RgSkoKTk5OhIWFffC5rKys6NKlC8OHD5c+i42NxcDAgKysLB49esTcuXPR1dXlyZMnzJw5k0WLFrF27doPXvdNiqorrCgitNYuQm/tIvTWLkJv7VFUtRbu6X/IihUrSEtLIz4+nrVr19K5c2cNs0jXrl358ccfadeuHXPnzuXChQv4+flRokSJD173Q5GEH0KpVJKRkfHRcffo0YPt27dz69YtIG+K3NXVlSNHjiCTyRg/fjxbt24lJycHCwsLydEtEAgEAoHg342YafwEXrx4ga+vL5cuXZKWea2trXFxcSElJQW1Wk2zZs3YsmULOTk5LFy4kEmTJrFkyRLGjRtHRkYGVatWZcWKFZw5c4atW7eSlZXFkiVLNDafZmZmcvLkSYyNjRk/fjwA5ubm9O3bl2HDhmFra0udOnV4/PgxJ0+epHHjxvj4+AB5eywBXFxcKFeuHABHjx5l48aN/PXXX+jq6nLx4kXat29PRkYG/fv3Jzs7G7VajZGREV27duXixYtYWFiwZs0aVqxYgY6ODi1btmTixInalFsgEAgEAkEhRBSNH0GtVjN69GgqV67MyZMnefr0Ka6urkRHR3Pu3DmuXLlC//79iYyM5JdffuHFixcMGzYMY2Njxo4dy/nz5wHw9fUlJCSEbdu2sXPnTmrUqMG6deuIi4tj8eLF2NvbM3XqVO7evcvevXuxtrZm9+7drFmzhk6dOknT2EqlkrCwMMzMzJg8eTLjxo3jwYMH0nL6kSNHsLGxkVzSGzZseMsl7erqyoEDB3j69Cn79u3D0NCQ9PR0vL29WbNmDQ4ODiQlJeHh4UGJEiU+Ojv6d4R7+vNTWB14xRWht3YRemsXobf2KKxaC/f0/4ikpCTu3LlDUFAQxsbGksHk7NmzJCUlAXl7DOfMmYOJiQkmJiYMHTqUTZs2MXbsWI1rHTp0iB49elCnTh0AvL292bt3LwBZWVkcOXIEf39/yWQzYMAAwsLCOHLkiLQH0dvbmwoVKgAwefJk2rdvT3R09Fvj/phLGvIc3flubgMDA44ePSrlZMfFxVGqVCni4+MLrJlwT2uPourAK6oIvbWL0Fu7CL21R1HVWhSNH+HKlSuoVCqpdU5+DrRCoSAqKgoAU1NTjX1/1tbWPH/+/K1rPX/+HGtra+m9jo6OtJScnJxMdnb2W6YbGxsbjaLwTdd2/rkJCQkaRhn4uEsaNF3YH8vJLgjCPf35KawOvOKK0Fu7CL21i9BbexRWrYV7+n/EV199hYGBAVeuXNFYIo6KiqJSpUr8+uuvpKam8vr1awwNDYE8N3V+QfcmVlZWUqEJeUvf+cVlmTJl0NfXJyoqSuqvCBAZGanhsI6Pj5d6NOYXk+XKlXsravBDLul83nRhvy8n++HDhwWRCyi6rrCiiNBauwi9tYvQW7sIvbVHUdVauKc/grm5OZUqVcLX15f09HQyMzNZtGgRAwcO1NibsGTJErKysnj8+DGBgYF069btrWt1796dvXv3cv36dbKzs9mwYQMJCQlAXi9FNzc3Vq5cydOnT1EqlWzbto1Hjx7h7OwsXcPf35+EhARSUlJYsmQJLVq0wNLSEn19fSAvYxo+7JJ+F+/Lyf6U3GuBQCAQCATFHzHT+BHys5+XLFlCu3btyMrKon79+gQFBUmFGuQtUTs6OgLQs2dPhg4d+ta1XFxcSEpKYty4cSQnJ9O+fXtsbW2l7ydPnoyfnx8DBw7k1atX2NraMmbMGKZNm8bTp0+BvGXn7t27k5SUhKGhIW3btqVJkybIZDLKlStHjx49mDZtGjdv3qRChQpMnz6d2NhYzMzMSEpKkmYpb9y4QXp6Onv27MHCwoI9e/YQGhpK69atpdzrChUq8Oeff6JUKgu03CyMMJ+fwrqZurgi9NYuQm/tIvTWHoVV608dj0yd30NG8I/Id08/ePDgf37tuLg42rZtKzmabW1tpf81bdqUadOmMXbsWIYOHcq9e/cYMGAACxYswNnZmalTpwJ5ru18bG1t2b59O/b29jg4OFCyZEm2b98OwF9//UX//v3Zvn079evXJy4ujgEDBvDdd9+9Zeh5HyqVihs3bvyvZRAIBAKBQKAF7OzsPrhsLmYaCzGlS5fWcDQDmJiYSI5mAwMDRo4ciUwmo379+tja2kqJNJ+Ck5MTJUuWBKBmzZpSu57k5GSeP39O6dKl/5F7WhhhPj+FdTN1cUXorV2E3tpF6K09CqvWwghTDPi7oxny4gLzndrm5uYaZhaFQkFBJo7fdE/L5XK2b99OWFgYRkZG2NrakpaWVqDr5VNUN/gWRYTW2kXorV2E3tpF6K09iqrWwgjzX2Jvb//Rpelr167RoEEDIM/B3KBBA2JjYwG4evUqrVu3pkGDBvzwww8a5+U7mnfs2MH58+d58OAB9vb2nzQuuVyuYWJ5+fLlW8e8WXB+//33/Pzzz4SFhXHmzBnWr19P+fLlP+leAoFAIBAIij+iaNQCjRo14vr160Bee5zr169LLXkOHTpE7dq1uX79Or1799Y4779xNFerVo1r164RHx9PZmYm/v7+GkXi38nvyahQKMjJyeHQoUNcuHBBuKcFAoFAIBAA/5Ll6Tt37uDr68vt27cxNjame/fueHl58euvv7Jq1SoePHhAyZIl6dSpE6NHj0ZPTw8/Pz+uX79OcnIyUVFR+Pv7U7p0aZYuXcqDBw94+fIlNjY2TJo0iTZt2nzwPhEREZJZJj/u78yZMyxdupQzZ84A0KBBA65cuUJCQgLLli3jypUryGQyDA0N+e6771AoFFID79u3b3Pv3j309PSYOnUqRkZGxMTEcO3aNR48eECjRo14+vQpL168oFWrVpiYmDBy5EjKlStHfHw8I0eO5NmzZyxZsoSnT58yZswYBg8ezN27d2natCm5ubno6upiYWHxj7rWC/f056ewOvCKK0Jv7SL01i5Cb+1RWLUWMYL/4dWrVwwePJh+/foRGBhIXFwc/fr1w9LSkoULFzJx4kSCgoJ49uwZnp6epKWlMXPmTAAuXbrE1q1bqV+/Pvr6+nTq1AlHR0fWrVuHWq1m+fLlzJ07lzZt2nzwPlWqVHnn2NauXavhcs7Ozmbw4MHUrVuXU6dOoVarmTdvHk+ePGHv3r38+uuv9O/fHy8vL4YPH05WVhYLFixg//79BAYGsmHDBiZNmsSQIUOktjs//PADy5Yto3///vTu3RsXFxecnZ1Zs2YNL1++xMvLi9zcXCZMmED9+vWRyWT4+fkhl8uZPXs2ycnJBdZcxAhqj6IaRVVUEXprF6G3dhF6a4+iqnWxLxrPnj2Lvr4+Y8aMQSaTUbFiRYKCgtiyZQu2trYMGDAAyIvnmzBhAl5eXkyfPh2AChUq0LRpU+lamzZtwtLSErVaTUxMDCVLlpTcxe+7j5GRkdRj8WNcu3aNqKgo9u/fj4mJCQDz5s2jcePG3L59WzquW7duKBQKFAoFAE2aNKFRo0bS6/Pnz9OvXz8gL3N64cKFvHjxghs3bqBUKhk/fjwymQxra2u8vb3x8vJiwoQJGBgYcP/+fQ4ePEizZs1YtGgRcnnBdzAI9/Tnp7A68IorQm/tIvTWLkJv7VFYtRbu6f+QkJCAtbW1xn6+qlWrolAoqFChgsaxNjY2ZGZmkpiYCCC1o8nnp59+Ys+ePSQkJFCtWjVKly4tuYvfdx/gk4vGxMRESpUqJRWMkNdix8zMjJiYGMqUKQNoup4BzMzMpNc6Ojoa484fT25uLjExMbx8+ZKvv/5a+l6tVpOdnU1iYiLDhg1DT0+PkJAQfHx8qFChAhMmTKBdu3afNP43x1CY/jEUZ4TW2kXorV2E3tpF6K09iqrWxb5otLKy4tmzZ1LKCcDp06extLTkzp07GscuXrwYuVyOqakpCQkJGrN7oaGh+Pj4sGHDBikL+uTJk5w6deqD90lLS8Pa2lq6Tv7MZHx8PDY2Nrx48YJr165x8uRJevbsSVJSEmlpaVLhmJqaSlJSEhYWFlKB+ndDy4cMLn/XomLFipw4cUL6LC0tjcTEREqXLs2DBw9wcHBg4MCBpKam8sMPPzBu3DguX75MiRIlPukeAH8lpiPXydL4zMxIjzIm+u85QyAQCAQCQWGn2LunW7duTU5ODhs3bkSpVBIZGcmiRYsoU6YMf/75J9u2bZM+j4+Pp2vXrujp6ZGVlaXRozA/I9rQ0BCAR48e4e/vD4BSqXzvfbKyNIsnS0tLjf+PjIzE1NSU69evM2HCBKpXr86cOXNITU0lNTWVuXPnUrFiRRo2bPhfa9GmTRvS09MJCAhAqVSSkpLClClTGDduHDKZjH379jF58mQSExMxMTHBxMQEIyOjAi81zz9yn5kHb2v871WG8r8ev0AgEAgEgv87iuVMY75DeebMmWzcuJHXr1+zY8cOtm7dir6+PsbGxqxYsQIDAwPWr1/PmjVrMDIywtTUlOzsbKKioggLCwPyXM0jR45k3bp1AAwcOBAjIyPKli0rFZBfffUVZcuWpVevXly6dImgoCAUCgUlS5Zk6dKl0kygUqkkLi4OyIsIXLp0KZGRkdJ9rly5wrx58xg7dqy0hGxlZYWfnx+6urrcu3cPgAkTJnD+/HmGDx/Or7/+iq6uLiNGjCAiIgIDAwPkcjnz58/n8OHDGtPfJiYmLF68mEmTJrF8+XIAKleuTEBAAAAjRoygZ8+eNGvWDLVajYmJCbNmzdLI2P4k1GrUatnfPlIXOrdYUaawOvCKK0Jv7SL01i5Cb+1RWLUW7mng1KlThIWFoVKpGDNmDJUqVUIul6NQKJgzZw5ZWVl4eXlRtWpVZs6cKTmZK1SoQFBQEP3795f6K1pYWLBu3TrCw8MBGDZsGGZmZuzevRs9PT22b9/O6tWr+eWXX9DX16dDhw40atSIqVOnkpmZyZAhQ/Dz88Pd3R3IKwbf5Z6eOnUqjRo1Yu7cuZJ7es6cOezdu5fatWsDeXslfX19ycrK4vHjxxw/fpz58+dL7unjx49TuXJlLl26JLmny5YtS0ZGBrNnz6Zbt254eHhI7ung4GAmTJjAnj17qFatGseOHZPc0ydPnqRLly4F0j09I52sv/39paalcSP6j3/6UwreQ1F14BVVhN7aReitXYTe2qOoal2si8Zp06ZRunRpALy8vBg1ahQuLi78+uuvHD16lKZNmxIQEPCPHMILFizA2NgYhUJBbGwsxsbGZGZmkpycTHR0NDExMUyfPh1DQ0OMjY1Zt24dubm5H7xmcXFPGxsZo/jbTGMJExOqVrYr8LUE76awOvCKK0Jv7SL01i5Cb+1RWLUW7mny2ujkY21tjVKpxMPDg9DQUAIDA5k6dSq1atVi5syZUtH1d6ZOnYqvry+nT5+W9jUCREVFsXTpUv766y8qV64s3Ss3N5eEhARKlSolLV8DODo6sn37dima7/Tp0wwcOJDMzEwuX76MnZ0dtWvXLhbuaWQy/m7NkclkheofSHGhqDrwiipCb+0i9NYuQm/tUVS1LtZFY3x8vNT2Jjo6GkNDQ549e4a7uzuenp68fPkSf39/PDw8uHz58idfNzs7mxEjRjB+/Hh69+6NTCbj9u3bHD58GMhbek5KSuL169caheOJEycYMmQIAN9++y2QZ7BJT0/n119/5c6dO/Tp06fIu6dnudRCrqP5p2VmJPo2CgQCgUBQlCnWReOKFStYsmQJ169fZ+zYsVKxZ2VlRXp6Oj/++CMPHjwgLS2NRo0akZmZiaWlJZmZmZL5Iz97WUdHB5VKxeTJkzl//jxpaWncvXsXmUxGbGwsAwcOlI6vX78+5cuXx87OjqNHj0ozgampqdLYTpw4gZ6eHr/99hu5ubnY29uzdu1aqlatSvfu3UlPTycjIwOFQoGlpSUNGzbk119/BfKWxsPCwmjQoAHJyck8ffqUJUuWsH//fnJyctDV1WXv3r1s2LBBI9GlTZs2+Pr60rNnT2JjY1Eqlejp6WFmZsbhw4fZt28fp0+fJjc3l6ysLGn5vaDu6crmxqK5t0AgEAgExYxiXTRWrFiRDh068Pz5c+rWrUtQUBC3b99m5MiRZGVl0bBhQ9RqNevWraN169aMGTOG8+fPExYWhrOzM+bm5hw/fpxOnTpRvnx5VCoVx44dIzg4mGPHjhEQEEBYWBhly5bF1NSUnJwcHj58SJUqVVi4cCF9+/alZ8+eUgHq6uqqMb7+/ftz/Phx7t+/Lxlu9u3bxy+//IKOjg5qtZqyZcsSGRnJ8+fPpfMiIyM5d+6cVAQnJiZiaWnJ5cuXmTp1KocOHeLKlSscO3aMX375hdGjR3Pr1i06dOhArVq1uHPnDrm5uajVanR1ddHV1UWtVtOiRQtCQkLQ19dHqVRibGyMrq5ugfc1iuzpz09hdeAVV4Te2kXorV2E3tqjsGot3NNAnz59qF27NsuWLWPv3r3I5XKaNm3K5MmTCQwM5PDhw6SlpWFlZcXLly8ZNGgQjx49Ij4+HiMjI1q2bAlAq1atuHXrFrVr1+bgwYMA1KlTh7i4OIyMjJg/fz79+vWjS5cuODk5AUgNvQ8ePIiNjQ22trbo6OhgY2ND+fLlJYNO9+7dpXY+WVlZhIeH4+/vT6tWraTn6NatG0eOHGH48OEAuLi4YGhoiKGhId27d+f+/fsMGDAAmUyGh4cHhw4dYsiQIRgaGuLo6Cjt50xMTOTixYscP35cWrZ//fo1jRo14s6dO5iampKbm0v//v1p06YNtWrVQi6Xf/Lydz4ie1p7FFUHXlFF6K1dhN7aReitPYqq1sW6aIS8fojlypXTmC3Ljw9Uq9WsWrWKs2fPUrp0aWrXrk12drZGU+83sbGx0XhvbW3Nw4cP/2djTU5OJjs7+6372NjYEB0dLb1/lxEmv7DLf843zTByuVwywgD06NFD43wdHR2io6Np3749fn5+7Nixg4CAAAwMDOjXrx+jRo0q0GyjyJ7+/BRWB15xReitXYTe2kXorT0Kq9bCPf0fypUrR2xsrEa8X2xsLACTJ0/m3LlzXL16VTKedOzY8b3XenOJGPIc1PluaLlcLu1/BEhKSpJeDx06VHo9e/Zs6Tu1Ws2ePXuIjY3FwcGBH3/8EX19faKioqhWrZp0TmRkpBRdCP/cCJOfQnP8+HEsLCykzx89ekSFChWIjY3F3NycwMBAlEolly5dwsPDgzp16tC6detPugcUXVdYUURorV2E3tpF6K1dhN7ao6hqXSxjBG1sbHjw4AE2NjY4ODigVquleL/Hjx8TGBgIgJGREc2aNUNHR4esrCy2bt3Kw4cPNYq/N7l16xb79+8nOzubs2fPEh4eTvfu3QGoVq0aFy5cICUlhdTUVLZs2SKdl5+4AuDj40OpUqWAvCL05s2blClThvDwcHR0dHBzc2PlypU8ffoUpVLJtm3bePToEc7Ozv+1LpaWlrRu3ZqFCxeSlJREdnY2GzZsoFu3bqSkpPD7778zdOhQ7t+/j56eHubm5gDSeAUCgUAgEPx7KfYzjUZGRqxfv55p06axdu1aAPT19dHT06N169ZMnDiRb775BiMjI2rUqIG5uTkBAQGEhoZKS9aQNztZokQJZs+ezYwZMyhVqhTLly/niy++wM/Pj+joaBISEvj666+Ry+XSfkhAarQNeX0fk5KSiI2NlfY/vnjxgrVr1+Ll5UX16tU5duyY9F3FihUJDAykSpUq0nXGjRuHnp4emzZtYtq0aZQqVYo2bdqQlJQk3XfYsGHExcVRv359aYOrWq3mq6++YvPmzTRt2hSZTEaNGjUICAiQZh51dHSkBJgSJUowffp0vvzyy8/18wgEAoFAICgiFPuiMSkpiaSkJF68eMGYMWMYPnw4GzZsYMuWLaSnpwNw/fp1Xr16hZOTE/369WP48OHExcXRr18/GjRowOPHjzly5AgTJ06kV69ePHv2DE9PT65evSoVdz/99BO+vr507NiRixcvMmLECIKDg6X9iR4eHtjb23PgwAGcnJzw8PCgS5cuODo6cvr0aWxsbAgNDWXlypWsW7eOxo0bExERgYeHB69evZKex9LSkn379mFoaEhKSgoATZs2ZdGiRURFRdGlSxdq1arF5s2bUSgU9OzZk+7du+Pq6squXbvYvXs3O3fupFq1ahw6dIglS5ZQuXJlMjMzmTRpElu2bMHe3p67d+/Sp08f6tSpU2DNhXv681NYHXjFFaG3dhF6axeht/YorFoL9/R/UKlUjBo1ihIlSjBmzBhiYmI4ceIEffr00Wi8ffbsWfT19RkzZgwymYyKFSsSFBSEkZERwcHB2NraMmDAACAvaWbChAl4eXkxffp0ACpXrizN0LVq1QoLCwv++usv7OzsPnms+/fvx93dnaZNmwJ5xaC7uzt79uyhbdu2ALRs2VLam5hfNA4ePBhDQ0Nq1qyJhYUFXbt2lY6xs7OTDDC7du1ixIgR1KpVC8hzZYeEhHD48GF69+6NgYEBISEh5ObmSn0h/0mMoHBPa4+i6sArqgi9tYvQW7sIvbVHUdW62BeNZcqUwcXFhZMnT9KwYUNKlixJ165d8fDwkJplQ14yi7W1tYapJL8tTWJiouS4zsfGxobMzEwSExMBNIwlAAqF4qNZ03/nxYsX77xPeHi49P7vzmn4cIygXC6X3OAxMTEsWbKE5cuXS9/n5ORQt25dDAwM2L17N+vXr2fChAmkpaXh5OTEzJkzMTU1LdBzCPf056ewOvCKK0Jv7SL01i5Cb+1RWLUW7uk3aN68OZcvX+b8+fNSUXj69GnS0tKkY6ysrHj27JmGyzr/mPLly3Pq1CmNa0ZGRqKnp1fggupD2NjYEBkZqfFZVFSURkH6Lqe0TCZj6tSpH72+lZUVNWvW5NWrV+zYsYPDhw+zbt06vLy8SEtL4/nz56xYsQKAe/fuMX78eDZu3MiUKVMK9BxF1RVWFBFaaxeht3YRemsXobf2KKpaF0v39N9p3bo1OTk5koM6MjKSRYsWkZWV9UnHODs78+eff7Jt2zbpu5UrV9KxY8f/6Yxat27dCA4O5tKlS6hUKi5fvkxwcDBubm7/k+v36NGDa9eu8fr1ayDPFf3s2TOuXr1Keno6w4YNIywsTEqikcvlwjktEAgEAoEA+JfMNJYsWZLAwEAWL15MUFAQhoaG9OnTh8qVK3/0GHd3dyCvbc7KlSvx8/NDR0cHAwMDjh8/zk8//UTlypVJTU2lVatWNGrUSJrRzM3NZfPmzdy8eZMbN25w/vx5SpQoIe03zDe4dO7cGV1dXb755hvGjh3LggULiI6Olv4rZNmyZVy4cAGlUimN98yZMyxZsgSA6dOnY2hoSKlSpVCr1cybNw+FQiH1nMzPtl65ciW1atXi119/pUGDBhgZGWFkZISjoyMAPXv2ZMaMGUycOBGA8uXL06lTpwLrLYwwn5/Cupm6uCL01i5Cb+0i9NYehVXrTx2PTP2++BPBO3mfy3rUqFHMmTMHLy8vhg8fTlZWFkFBQYSEhLB+/XpsbW05deoUU6ZMYdeuXdSvX59+/fphbGzM4sWLUSgUeHt7Y2pqypIlS3BxcaFu3brMnTtXKgSfPHnC3r17iYyMpFOnTixatIgOHTpw7tw5vLy86NSpE76+vsydO5fo6GipP+TJkydZsmQJZ86cYd26dURERLBjxw5CQ0NZt24d4eHh3Lp1i/79+7N9+3bq169PXFwcAwYM4LvvvmPs2LGfpI1KpeLGjRufT3yBQCAQCASfDTs7uw8um/8rZhr/l7zPZZ2/gbRbt24oFAoUCgX79+9n+PDhUtuaDh06cPLkSUJCQjA3NyciIoITJ05IS8C+vr68evWKa9euERUVxf79+6Wkmnnz5tG4cWNu377NxYsXqVu3rjQL+O2339KmTRtpjG5ubri7u5OQkICFhQUHDhzA1dX1g8kxNWvW5MiRI9jY2JCcnMzz588pXbo08fHxBdZIGGE+P4V1M3VxReitXYTe2kXorT0Kq9bCCPOZeJ/LOiEhAdB0N7/PDX3//n3p+PwYQshzYFtYWPDgwQNKlSolFYwAJiYmmJmZERMTQ3x8POXKldO4bsWKFaV4wnr16lGtWjWOHj0q9Y2cOXPmB59LLpezfft2wsLCMDIywtbWlrS0tPfmcH+IorrBtygitNYuQm/tIvTWLkJv7VFUtRZF4zvIdyL7+vq+9d2HXNag6W4uX748UVFRGudfuXKFv/76C2trawDWrVvH7t27AVi4cCF3796lVatWJCUlkZaWJhWOqampJCUlYWFhgZWVFefOndO4blxcHPr6+tJ7Nzc3jh49ikKhoFGjRlKT8ffx/fff8/PPPxMWFkaZMmUAGDly5IeFEggEAoFA8K/hX+Ge/l/yKU7sfLp3787mzZu5c+cOKpWK48ePc//+fdavX4+lpSXNmjVj+/btDB48mLNnz7J//36ioqKoV68e1atXZ86cOaSmppKamsrcuXOpWLEiDRs2pFOnTjx8+JC9e/eSk5PDxYsX+fHHHzXu3alTJ+7fv8++fftwdXX96HOlpaWhq6uLQqEgJyeHQ4cOceHChffmcAsEAoFAIPh3ofWZxujoaBwdHVmyZAlr1qwhKSmJ7777Djc3N3x8fIiKiqJ+/fqsWrWKpUuXApozfra2tmzfvh17e3tOnjzJ2rVriYuLo2zZsnTs2JHRo0cDeY2sly1bxpUrV5DL5TRp0oQpU6ZQtmxZrly5wqRJk3Bzc+OHH34A8oqsSZMmSXvxEhMT8fLy4sqVKygUCkaOHEnfvn0pWbIkc+fOZdasWaxZswa1Wk3p0qWlpWHIS0SZP38+9+/fx9jYmMGDB5OcnEytWrXo0aMHU6dOJTw8nHv37vH69WtWr17NunXr6NSpEx06dGDAgAFER0cTGRnJmTNn0NHRoVmzZrRq1Yrhw4eTnJyMjo4OW7ZsYdasWdjY2GBgYMDhw4d5+vQpM2fOZPXq1ahUKh48eCDNakJe38c7d+7QqFEjDA0NpaJw8ODBXL9+nSZNmqBWq9HT06NixYrcv3+/wL+xcE9/fgqrA6+4IvTWLkJv7SL01h6FVetCHyN4/vx5jh07JuUl3717ly1btkh5yfnF3Pt4X1Zy8+bNqV27NoMHD6Zu3bqcOnVKch+PHDmSvXv3AhAfH8+TJ084c+YML168YNiwYRgbG0tO4cuXL7Np0ybWrFnDwYMHmTZtGm3btsXS0pLVq1fTo0cPvLy8UKvVLF++nN27d/PgwQPS0tIYOnQo7u7ubNu2jSdPnjBy5EiMjY05ePAgoaGh0tLypUuXcHBwwMPDA1dXVx4/fkznzp2ZOHEiQUFBUsZ148aNmTlzJn5+fly6dImtW7dSv3599PX1adeuHfr6+hw5cgQTExPc3Nzo168fgYGBVKpUiXPnzuHn58f27du5f/8+J0+eZNmyZTg6OnLz5k1Gjx7NhQsXaNGiBWq1mjFjxjBmzBiSkpIYMGDAJ81S/h0RI6g9imoUVVFF6K1dhN7aReitPYqq1v9nReOn5iV/iPdlJV+6dOmD7mPI23s4Z84cTExMMDExYejQoWzatEkqGps1a8Y333wDgLOzM1OnTiUqKgpLS0s2bdqEpaUlarWamJgYSpYsKbmMw8PD0dHRwdPTE7lcjq2tLUOHDiUwMPCjzxMWFvbRjOsKFSpI2dT5uLm5YWVlBUD9+vVJTEwkMzOTsLAw+vbty8GDBwHYs2cPjo6OtGvXDoCGDRvSo0cPdu3aRYsWLdDX1+fChQtUq1aNpk2bcujQoX+UPS3c05+fwurAK64IvbWL0Fu7CL21R2HVutC7pz81L/l9fCgrOTEx8YPu4zJlymBqaqqRdmJtbc3z58/fOb78Aih/+vb+/fuMHj2ahIQEqlWrRunSpaXxxsXFUa5cOY1i6+8O6veRn3GtUqmIjY2lQoUKb2Vcf0r29IsXLxg9ejRDhw7F2tpaI3v68uXLNGrUSDpepVJRsWJFAFavXo2fnx+rVq1i/PjxNGzYkLlz51KjRo1PGv+bYyhM/xiKM0Jr7SL01i5Cb+0i9NYeRVXr/zMjzId6BuYjl8s1jBgvX76UXr+ZlfzLL78QHBzM7du32bhxI+XLl5fcx/m86T7Of58fpwd5ey3/3sbmXcTHx+Pt7c24ceNo3bo1FSpUwMXFBYChQ4dSrlw5YmNjNYre2NjYj14X8tzWkZGRjBs3Tpod/HvG9fuyp9/kiy++4Pr161IiTT5WVlZ07dqVa9euce3aNWrXro27uzubN28mNzeXu3fv4unpyalTpwgPD8fc3PyTMq3/zl+J6Tx6nsqj56m8SHvbICQQCAQCgaDoUajd09WqVePatWvEx8eTmZmJv7+/VCB9KCv5Y+5jyJthW7JkCVlZWTx+/JjAwEC6dev20TGlp6ejUqkwNDQEICUlBX9/fwDWr1+Pg4MDarVaclfnX/tTyM+4vnfvHiqV6n+ecd2tWzeOHDnCxYsXyc3NJTMzk9DQULZu3YpcLmfBggWsXr2arKwsSpcujb6+/j/Knp5/5D4zD95m5sHbvMpQfvwEgUAgEAgEhZ5C3afR3d2d33//nU6dOqGnp8eAAQOk2UBLS0vWrl3L6tWrmT17NgYGBnTo0IGBAweiq6vLpk2b8PX1xcnJCaVSyTfffENQUBC6uv//kU1NTXF0dESlUknZ07t378bExITatWtrxOzlM2fOHL755hsmTZpEUlIShoaGeHp64uvrS8+ePQkNDaV79+5s2rQJPz8/yYlsYGAgzT6q1WqWLl3K0aNHiY+PZ/PmzVSvXp369etjb2/PuXPn2LBhA4GBgVSpUuWt/Z0+Pj6kpaXh5eVFTEwMv/zyCytWrCAzM5NSpUrx5ZdfSscqlUqSkpJo3rw52dnZtGzZkpUrV+Lt7U1WVhZ16tSRlverVKlCSEgIO3fuRCaTUblyZYKCggr+w6nVqNUy6VkLm0usOFBYHXjFFaG3dhF6axeht/YorFoXWve0jY0NDx480PjszaIMNFvsrF69WuO7oUOHSq8dHBxwcHB4532sra1Zs2bNB8cybtw4RowYgYuLC+3atcPDw4OXL1/i5eUlmXLyyR/zunXraNiwIUFBQdLS7YABA0hJSSEiIoKkpCRUKhWvX79m4MCBjB8/ntWrV7Nt2zZCQkLo3r07ERERXLx4ke3bt2Ntbc3u3bsZOHAgR44cYdOmTfTr14/GjRvj6enJrVu3cHd3Jz4+Hk9PT5RKJS1atNB4tvj4eMLCwlCpVIwZM0ZjP2VsbCxLliyhU6dOXLlyhYEDB7J7924aNGgg3UdPT49FixaRkpLChQsXKFGiBKdOncLLywulsuAzhekZ6WT95+8vNS2NG9F/FPgagk+jqDrwiipCb+0i9NYuQm/tUVS1LtQzjdrg3LlzKJVKxo8fj0wmw9raGm9vb7y8vD4avfcuVCoVAQEBGBsbM3HiROLj4zl37hxfffUVhw8fplOnThw5cgR/f38qVaoE5BWdYWFhHDlyhOHDh2tcr379+lSrVo0jR44wZMgQzp07h4mJCfb29tIM5LRp0yhdujQAXl5ejBo1ioULFwJQo0YNOnfuDECTJk0oU6YMkZGRNGjQgJycHGlfp6enJzo6OpiYmGikyzx//lwa56dibGSM4j8zjSVMTKha2a7AOgo+TGF14BVXhN7aReitXYTe2qOwal3o3dOFhZiYGF6+fMnXX38tfaZWq8nOzpYcywWhTJky9OrVi3379tG4cWNKliyJjY0N8fHxyGQykpOTyc7OfivWz8bGhujo6Hde848//mD37t0MGTKEKVOm0Lx5cw3zS35RFx0dzZAhQwB49eoVoOmshjwneP409K1btyTndGJiohRjaGNjQ926dQHIzc0tsAbIZMikl7JC9Q+juFFUHXhFFaG3dhF6axeht/Yoqlr/K4tGe3t7abnZysqKihUrcuLECen7tLQ0EhMTuX79+lvLs28mv7yPOnXqcPLkSS5cuIBMJsPPz4+QkBCqVq1KmTJl0NfXJyoqimrVqknnREZGvnepHfKWma9fv05OTg7Tpk3T+C4+Pp6qVatK7z/VwJKTkyO99vb2xsHBgcDAQHR1dUlKSpIaoReUWS61kOvk/WmZGYl+jQKBQCAQFAcKtXtaG7Rp04b09HQCAgJQKpWkpKQwZcoUxo0bR7Vq1Xjx4gWXL19GrVZz6NAh/vzzz3deJ39P444dO1i4cCEJCQl07dqVpKQk4uPjSUhIIDc3l5YtWwJ5S8pPnz5FqVQSEBDA/fv32b17N19//TX379/nyZMnGtf/8ssv8fHxQSaTcfnyZQAyMjIAcHFxoXnz5lKKTtu2bVEoFKSkpPDo0SOaNm1Ks2bNmDBhgjTL6OTkBOQ1FN+yZQspKSncuXOHLl268NVXX9GiRQsgzy1eUCqbG1O9bAmqly1BGRP9Ap8vEAgEAoGg8PGvnGl8ExMTE77//nt8fX0JCAggNzcXe3t7NmzYgKWlJaNGjWLq1Kmkp6fz7bffSsXW+zh16hTe3t5s3LiR+Ph4WrRogYGBASqVCldXVzp16sSFCxcYNmwYvXv3JiMjAwMDA8qXL09AQADW1tbMnDmTQ4cOERkZSUhICAAtW7Zk9erVGjOIa9euBaBHjx6Eh4ezbds2AMaMGUN2djaHDh1CR0dHI0oxMTERlUrFyZMnsbW1pWPHjgwbNozU1FQCAgJQKBSUKlUKJycnDh48yKFDhz44A/ouRPb056ewOvCKK0Jv7SL01i5Cb+1RWLX+1PHI1B+LXhF8EtHR0Tg6OnLgwAHu37/PunXr8PHxYdSoUQwcOJBz584RFhYmHd+8eXMmTZpE+/bt+eqrr/D396dVq1bS9926daNdu3YMHz4cW1tbFixYwNKlSzExMcHT0xMXFxcaNmxIdnY2Z86cwcbGhvv379O5c2fOnDlDVFQUQ4YMISIiQkrGSUtLo3Hjxvzwww/Y2dlha2vL9u3bsbe3Jy0tjbS0NKysrHj58iWPHz9mxowZdOzYEQ8Pj0/SQKVScePGjf+prgKBQCAQCLSDnZ3dB/da/utnGj+F1NRUsrOzJYfyh6hUqRL3798H8tr+KJVKkpOT32tI+ZgxJj/VJiQkBFdXV3788Ucgb2/lm2k5oBlX+LEoRTs7O41z1Wo1q1at4uzZs5QuXZratWuTnZ390TjHdyGypz8/hdWBV1wRemsXobd2EXprj8KqtXBP/w9p27Yta9aswd7e/p3fDx06VMpnjo+Plz6Pjo7G0NCQUqVKSXsUnZ2dGTFiBJC3D3L06NEAzJ07lx07dkjn5htj4uLigLy9haNHj5aKxlKlSqGnp6dh1Mk/FtCIUswvHP8epfgmy5cvJzY2lvDwcOn4jh07FkQmiaLqCiuKCK21i9Bbuwi9tYvQW3sUVa3/9UaYT+FjjumAgAD69OkDwIoVK2jXrh27d+9m7dq1dO7cWSOF5ujRo3Tq1AmAP//8k5iYGNzd3UlOTpaMMdu2bePRo0c4OztTvXp1AGbNmiXlT0PeTGXXrl2pU6cOurq6pKamsmzZMun7T4lS1NPTIzU1FchbutbX10dHR4esrCy2bt3Kw4cP35rNFAgEAoFA8O9EzDS+QX5rnNevX1OhQgVGjx7N0qVLARg2bBienp6Ym5uzc+dOzMzMuHXrFnPmzGHv3r3UqlULyFtWbtWqFenp6ejr61O7dm1CQkKkrGoHBwc8PDxIS0sjNDQUlUrF4cOHadWqFQMGDCAhIQGZTIZCoWD27NlMmjRJGp+trS3GxsbMnz+fU6dO4eDgQHh4OG3btiU7OxsDAwMgrzDds2cPycnJZGRkSFGKjRs3xs7ODkdHR5RKJdbW1owfP55BgwYxduxY+vfvT4MGDQAoUaIEjRs35uHDh9r8CQQCgUAgEBRSRNH4Hy5fvkxwcDChoaFYWFgQHBzMjBkzuHDhAnXr1mXLli3Y29sTGhrKnTt38PX1ZePGjeTm5mr0M9TV1cXU1JTg4GAqVqyIn58fcXFxLF68WON+ERERb2VbT506lbt37+Ln56cRL3j27Fkpc7tRo0asWbOG7OxsTp8+TUJCAtOmTaN///7s2bOHBQsW8PDhQ44dO8ajR49wd3dn27ZtfP3113h5efHixQtCQ0MxMDDA19eXP/74g7Fjx3LlyhWys7P56aefpOdfvXo1Fy5cKLCWwj39+SmsDrziitBbuwi9tYvQW3sUVq0LbfZ0YUVfX5/k5GT27t1LmzZt6N69O+7u7hrJK/koFAo6d+6skfGcz/Hjxxk+fLi0rDx27FgOHDjw0ftnZWV9Urygi4sLhoaG0sylkZERAwYMQCaT0bx5c1QqFUOGDMHQ0JB69epRtmxZYmJiqFq1KidPnuT48eOYm5sDMH36dBo1asSdO3cK9Pwf4+7duwU+R/DPKKr5pUUVobd2EXprF6G39iiqWoui8T80aNAAPz8/duzYQUBAAAYGBvTr149Ro0a9dayFhcU7C0bIy2ouX7689F5HR0eaJfwQnxovWLZsWY3vzczMpMIuf0wlS5aUvpfL5eTm5ko51T169NA4X0dHh+joaNq3b//e53/fs74P4Z7+/BRWB15xReitXYTe2kXorT0Kq9bCPV1AYmNjMTc3JzAwEKVSyaVLl/Dw8KBOnTpvHZuamkq/fv003M4lS5bkwYMHODk5ERsbK32uVqt59uyZxvlz5syRMp+zs7Np37498fHx6OjofDRe8O8zf586E2hpaQnkzYS+6Z5+9OgRFSpU+ODzt27d+pPukU9RdYUVRYTW2kXorV2E3tpF6K09iqrWwj39H37//XeGDh3K/fv30dPTk5Zw81vb5LuMP4a7uztbt27lyZMnKJVK/P39ef78ucYx8+bNo1GjRgBkZmZiYGDAtWvXcHd3Z+XKle90Uf+3WFpa0rp1axYuXCj1eNywYQPdunUjJSXlg88vEAgEAoFAIGYa/4OTkxN//fUXo0aNIikpCXNzc6ZPn86XX36Js7MzY8aMoX79+jx8+BAdHR3S09OZOXMm586dIykpCZVKhaenJwMGDCAuLo7OnTuTnZ2Nrq4uenp6JCcnS/eaNm0a5cuX5+jRo6SkpJCWlkajRo3YvXs3MpmMLl26kJGRgY6ODvXq1dOYTezfvz+DBg1i//79fPHFF6SkpODt7Y2BgQGnTp0C4Pz587x69Ypdu3bx8uVLwsPDcXV1ZenSpSxcuJDWrVuTlZWFjo4OLVu2xNDQECcnJx4/fkyvXr3IyMhALpdjY2NDTk5OgbUURpjPT2HdTF1cEXprF6G3dhF6a4/CqrWIEfwfkh8R2KNHD2bNmsWyZcvYsWMHS5YsoVOnTly5coWBAweye/du5HI5Bw4c4ObNm6xduxYLCwvs7e0xMTEhPDwcfX19jfg+Pz8/IiIipKXufv36IZfLWb58OSVKlGDNmjUcP36cI0eO8OrVK41xZGZmsm3bNvz9/Vm9ejVOTk6sWLGCrVu3MmDAAMaPH8/58+cZM2YM4eHhWFtb07NnTypXrszs2bPJzs5m2rRpGBkZsXLlSvbv38/27dvZsWMHxsbGrF27lrNnz3L48OFP0knECAoEAoFAUHQRMYL/Q7p06YKenh4lS5akRo0adO7cGYAmTZpQpkwZIiMjuXXrFsHBwaxZswZra2u2b9+Ovr4+crmcc+fO4eTk9N7rR0VFERERwdGjR6V9hxMnTiQsLIzz58/z5Zdfaowj32xSvXp12rdvD0CzZs3YsmULI0eORKFQSPshY2NjSUxM5M6dOwQFBWFsbAzAlClTaN++PbNmzcLAwIDo6GhCQkJo2bIl3t7ejBs3rsA6CSPM56ewbqYurgi9tYvQW7sIvbVHYdVaGGE+A286l9+XJT1gwAB27tyJp6cnkLfZVV9fn6ysLMnB/D5evHgBaGZI6+joYG1tTUxMjFQ09u7dmzNnzmBjY8OmTZs0jDP5TmdTU1OuXLlC//79AcjNzSU6OhqVSkWrVq3eGntUVBTOzs5kZ2ezb98+Vq5cibm5OSNHjqRXr14FkanIbvAtigittYvQW7sIvbWL0Ft7FFWtRdFYAD7FqWxjY4O+vj7bt2/Hzs5O+vzx48eSg/l95LfqiYyMlLKsVSoVsbGx78yLBhgxYgQRERGfNH4rKysMDAy4cuWK9MeqVCqJioqiUqVKPHnyhDp16tClSxcyMzM5ceIEU6ZMoVGjRtJ4BAKBQCAQ/DspFkVj/p7DJUuWsGbNGpKSkvjuu+9wc3PDx8eHqKgo6tevz6pVq9DT08PX15eIiAieP39OiRIl6NOnDyNHjgTg5cuXLFq0iPPnzyOXy/nmm2+kxtqOjo7069ePffv2SZF9p0+fZv369cTGxrJixQpSUlJwc3NjxYoVjB49Gj8/P+7cuUNmZiatW7dmxYoVQF4zbx8fH0JDQ1EqlQwbNoy5c+fSqlUr5s2bR4UKFfj5559JTk5GpVJRv379dz77unXrpAjD58+fs2rVKmmsf3ddm5mZIZfLadCgAaVKlcLFxYWUlBTOnTvHjz/+yOLFi/nll1/Q09NDR0eHWrVqoaOjQ4kSJf73P5pAIBAIBIIiRbEoGvM5f/48x44dIyoqii5dunD37l22bNmCQqGgZ8+e/PDDD7x48ULat1eiRAlOnTqFl5cX3333HZUqVcLb2xtjY2NOnTqFQqHA29ubNWvWSPeIjIxkwIABXL16lcuXLzN27FiWLl3KsmXLcHV1JSgoiH79+mFsbMzw4cNRq9VUrVqVwYMHExgYyL59+wDYtm0bKSkp9OjRgzt37lCmTBnGjx/Ppk2bcHNz48aNGygUCuzs7ChZsiQzZsxg2bJlH3z+cePGScvTO3fu1GhMnpWVxdChQ+nSpQsvXrwgIiKCrVu3Ym1tTVBQEOHh4dy5c4eOHTty4cIFUlJSuHbtGj179sTKyqpAv4NwT39+CqsDr7gi9NYuQm/tIvTWHoVV639ljODgwYMxNDSkZs2aWFhY0LVrV2lJ2M7OjpiYGCZOnIiOjg4mJibExcWhr68P5M3S6erqEhERwYkTJ6T+hL6+vrx69YqNGzdia2uLi4sLnTp1AmDy5Mk4OjrSoUMHOnToAEDp0qXZsWMHJ06c4N69e6SlpTFw4ECaNm1Kp06dkMvl9OnTh6+++ooNGzbQvHlzAFJSUnj69CkqlYro6GiOHz9O1apVAXj9+jWNGjUiOTmZM2fO4OjoqPHc06dPJyYmhmvXrnHy5EkqV64MgIeHB2PGjOHBgwccO3YMpVLJrFmzpGX2ixcv4uXlRfXq1bGysqJhw4ZYWVnx8uVLHj9+zIwZM6R+jQVBxAhqj6IaRVVUEXprF6G3dhF6a4+iqnWxKhrfNKfo6Oi8FaenVqtJTExk4cKF3L17FxsbG+rWrQvkGUUSEhIANGIALSwsNPYTvmmGSUxMpHbt2hpjsLGxkQwvq1evxs/Pj1WrVjF+/HgaNmzI3LlzMTMzQ6lUasQLlixZknr16nHr1i3g/XF/fzfg5BMfHw+gcc381BmAmJgYXr58yddffy19plaryc7OJjExET09PVatWsXZs2cpXbo0tWvXJjs7m3/SkUm4pz8/hdWBV1wRemsXobd2EXprj8Kq9b/SPf0pRhVvb28cHBwIDAxEV1eXpKQk9u7dC4C1tTWQ154mf7bu0aNHHDlyhKysLABGjx5NeHg4ZmZmlC9fnsjISI3rR0VFYWFhQW5uLnfv3sXT05Pp06fz7NkzFi9ezNSpU9m3bx96eno8e/ZMmk1MTExky5YtDBo0CHh/3F9+Ybtr1y6mTJkifZ+/hPxmDOG0adM0vq9YsSInTpyQPrO1tWXZsmWULl2auXPnEhsbS3h4OCYmJgB07Njxo3q+i6LqCiuKCK21i9Bbuwi9tYvQW3sUVa3/dTGCqampGBgYoKOjw8uXL1mwYAGQlwFtaWlJs2bNWLp0qZTUsmzZMqKioti+fTsAGzZskGb73NzcCA8P5/jx46hUKmkPpZubG3K5nAULFrB69WqysrIoXbo0+vr6lCpVCrlcTpcuXfDz8yM+Pp6srCxWr17NjRs3Phr3l0+fPn00nqtcuXI0b96cxYsXk5ycTEJCgsYfZJs2bUhPTycgIAClUildy9/fH5lMRlpaGvr6+ujo6JCVlcXWrVt5+PAh2dnZn/PnEAgEAoFAUEQoVjONn8LixYtZtGgRW7duxdTUlA4dOvDFF1/w8OFDlEolycnJXL16la+//hodHR1atGjBmTNnpDi9zZs3ExMTw86dOzEzM0NXV5dly5Yxbdo0qUj7/vvvuXz5MqNHjyY4OJjmzZuTlZWFTCZDR0eHjh074u7ujr6+Pt27dyc9PR19fX2USiUtW7akefPm5Obm0qVLF5KSkpDL5VSrVo3vvvtO6v+4bds2ZsyYAcD+/fuZPHkyKSkp6Ovr07JlS0xNTTWKRn19fb755htWrVrF8uXLpb2c+c27u3btyvjx42nQoAFqtRpjY2Ps7e15+PCh1n4bgUAgEAgEhZdiUTTa2Njw4MEDjc/Cw8M13vv6+kqvjx8//tY14uLiaNu2LWvWrMHBwYGkpCQ8PDwoWbIkN27c0Ij+Cw0N5c6dO/j6+rJx40Zyc3OZO3cud+/exc/PD2tra3bv3s2MGTM4cuQIKpWKDh06cPDgQapWrcqFCxcYM2YMp0+fZsyYMbRv357OnTvj7e1NamoqEydOxMjIiPPnzxMaGsq0adPo27cvzs7O5Obmcvr0aWm/poeHB4cPHyYwMJAqVaqwbt06QkNDCQ8PZ+DAgbi6ugKwfv16bty4wYkTJzA3N2fu3LkcOnRIMvwsWrQId3d3vLy8UKvVLF++nJMnT3L+/PkC/x7CPf35KawOvOKK0Fu7CL21i9BbexRWrf+V7un/htKlS3P06FEqVqxIWloacXFxlCpVSjKY/B2FQkHnzp2Ry+VkZWVx5MgR/P39qVSpEgADBgwgLCyMI0eO4OLiglqtZs+ePTg5OdG0aVNu3LiBXC4nJCQEhUIhuboNDAyYNWsWzs7O0v7FN+/1dw4cOMCIESOoXr06AKNGjaJVq1ZvGVgOHTrEyJEjpbSZmTNnamRKb9q0CUtLS9RqNTExMZQsWfK9z/4xhHtaexRVB15RReitXYTe2kXorT2KqtaiaPwPCoWCI0eOsGfPHmQyGTVr1iQtLQ1d3XdLZGFhIRVxycnJZGdnY2Njo3GMjY0N0dHRlCtXjh07dhAQEMDIkSNRqVS4uroyadIkEhMTKVeunMZScv518l3Yb97r7yQkJGg4pvX09DSSaPJ5/vy5ZPSBPLe2qamp9P7+/fuMHj2ahIQEqlWrRunSpf+RcxqEe1obFFYHXnFF6K1dhN7aReitPQqr1v9K9/R/w/Hjx9m5cye7d++WZgu7devGH3/8IR0zfvx4fv75Z0DTqV2mTBn09fU1nMuQ1wjcwcGBxMREJkyYwNixY/Hz88Pd3Z2QkBCqVKlCmTJluHv3Ll9++SV169Zl165dkiPbwsKCx48ff9AVbm1tzbNnz6T32dnZLFu2jKFDh2ocZ2VlRVRUlPQ+IyOD1NRUIK9dj7e3N+vWrcPBwQGAkydPcurUqYKJ+B+KqiusKCK01i5Cb+0i9NYuQm/tUVS1/te5p99HamoqcrkcAwMD1Go1P/30E/fv3/+k2Ta5XI6bmxsrV67k6dOnKJVKtm3bxqNHj3B2diY2NpYXL15IM4YrV67EyMiIUqVKUbZsWbKzs3FzcyMwMJCEhAQWLlxIkyZNNPpFvg9XV1cCAwN58uQJOTk5bNq0idOnT0t7FfPp3r07AQEB/Pnnn2RlZeHr6yvtYUhPT0elUmFoaAjktffx9/cH8rKpC0pSunBcCwQCgUBQ3CiUM435WdIzZ85k48aNZGZm4uDgwJw5cwgKCuL69eskJycTFRWFv78/1atXZ+XKlZw9e5bs7Gzs7OyYNm2a1Gsx37Ry+/ZtjI2N6d69O15eXshkMum7e/fukZubS9u2bTEwMKBatWrY2dnx22+/vbNwSk9Px9XVlZiYGJRKJV999RVfffUVAwcO5Pnz5xgaGlKuXDnc3d3x9/fH0NCQCRMm8Pr1a7Kzs2nQoAEKhYIxY8YAsHv3bkJDQ9HT06NChQokJSXx1VdfYWZmRmZmpnRfBwcHlEolt2/f5syZM0ycOJGAgAB69OghFb5ubm789NNP3Lx5k+vXr/Pw4UOWLVvG69ev6dOnj+TWlslkLF++nOXLlzN58mQmTZpESkoKKpVK+i+g9evXM3bs2AL9fsmZSiwx/mc/vkAgEAgEgkJJoSwa8zl16hRhYWGoVCrGjBnDvHnzqFixIpcuXWLr1q3Ur18ffX19Bg0ahFwu58CBA5QoUYI1a9YwcOBAjhw5Qk5ODoMHD6Zfv34EBgYSFxdHv379sLS0pE2bNgwYMIBx48axdetWnj59yujRoxk8eDA9e/bEz88PmUyGnp4eixcvZt26dQBUr16drKws5s6dS/369YmLi2PAgAHUrVuXs2fP4ufnx/r16/Hz85PGaGxsjIeHB66urvTr14/GjRvTtm1btmzZQv/+/bl37x4Afn5+hISEsH79emxtbTl16hRTpkzh1q1b1K9fH8hbDj927BgA9+7dIzU1lYEDBzJx4kR++eUXhg8fTkxMjJQh7ebmxokTJxg7dixZWVlcvnyZtWvXUrZsWbZs2cLgwYM5duwY7dq1o0OHDoSFhWm4vHv37q2RhPMx1OrC5wwrbhRWB15xReitXYTe2kXorT0Kq9bFwj09bdo0SpcuDYCXlxejRo1i4MCBVKhQgaZNmwJ5CSgREREcPXpUSlCZOHEiYWFhnD9/HqVSib6+PmPGjEEmk1GxYkWCgoIwMjLi8OHDVKtWTWqUXb16dYYMGcLOnTvp2bPne8dVs2ZNjhw5go2NDcnJyTx//pzSpUtruI3fHGNB2L9/P8OHD6dOnToAdOjQgZMnTxISEiIVjU5OThoRiQAjRoxAoVBIWda9evXC1NQUU1NTatSoQXR0tOTgXrt2reSiHjNmDHv37uXcuXPUq1fvvS7vgpCWkc6NG48L/OyCglNUHXhFFaG3dhF6axeht/YoqloX6qIx35ACeYaP/Obbb856vXjxAkAqgiBvg6m1tbXkPra2ttYwk+RH98XExHDnzh0aNWokfZebm/vRzalyuZzt27cTFhaGkZERtra2pKWlaex/LMjM3Ju8ePFC41kgz019//79D147fw9j/tjflbv98uVLMjIy8Pb21igEs7OziYmJwcnJ6b0u7/xm4J+CiZEx1avYffLxgoJTWB14xRWht3YRemsXobf2KKxaFwv3dHx8vFTgRUdHY2hoSKlSpTQKwHyzSGRkJDVq1ADyHj42NhYLCwt0dHR49uwZarVaOu/06dOkpaVhZWWFvb09gYGB0vWSkpJIT0//4Li+//57fv75Z8LCwihTpgyQN/t34cIF6ZhPycF+F+XLl9dwOUPebOqbheK7rv0p9ytVqhT6+vps3boVOzs7qWG5hYUFlpaWJCYmolKp8Pf3Jzc3l99++w0vLy+qVKnyVmzhh5DJKFT/GIozRdWBV1QRemsXobd2EXprj6KqdaF2T69YsYK0tDTi4+NZu3YtnTt3fqtvYtmyZWnVqhULFiwgISGBzMxMli9fjkqlok2bNrRu3ZqcnBw2btyIUqkkMjKSRYsWkZWVRceOHblx4waHDx8mJyeH58+fM3LkSI30mHeR379RoVCQk5PDoUOHePr0Kbm5uf/1M3fv3p3Nmzdz584dVCoVx48fJzw8nK5du/7X15bL5XTr1o0VK1YQFxcHwIULF3BxceHp06fExsYyePBgLl26hFwux9LSEuAtJ/bHMDUQPRoFAoFAIChuFOqZxooVK+Li4sLr16/p2LEjkyZNYvPmzW8dt3TpUpYvX07Xrl3JyMjAzs6Obdu2YWZmBkBgYCCLFy8mKCgIQ0ND+vTpg7u7OwABAQEsX76cBQsWoKOjQ+vWraVM5/cxePBgrl69SpMmTcjNzaVEiRJUqVJF6pcYFRUlLXtbWloyYsQI6dycnByio6O5e/cuQUFBGg3B4+Pj+f3338nIyMDNzQ3I66+4cuVKGjdujK2tLSVLlsTX15f58+fTuHFjjWLy9OnTrF+/HoDJkyczaNAg+vfvD+TNvi5evJijR4+SmZlJx44dAThx4gRr165FX1+fpUuXYmBgwKBBgwAwNTWlX79+fPfdd5/+owElDeSFbpNvcaOwbqYurgi9tYvQW7sIvbVHYdX6U8cjU//T2I/PSH7LnTNnzryVslIYSEpKol27dgwbNoxBgwZx69Ythg8fzhdffMGMGTNwd3dn2bJlODo6cvPmTUaPHs2yZcto0aIFfn5+HD58mA0bNryVFT1y5EjMzMzw8fFBT0+P7du3s3r1an755ReMjY2xtbWlRo0abNiwATMzMyZPnkxycjI//PADly9fZujQoSxdupR27drx4MEDRo8ezaBBgxg4cCBr1qzh2LFjBAQEaGRP5+dpd+jQAUdHx/8qe1qlUnHjxo3PJ7xAIBAIBILPhp2d3QeXzQv1TGNh5dy5cxgaGjJs2DBkMhlfffUVbm5u3Lt3jz179uDo6Ei7du0AaNiwIT169GDXrl20aNHig1nRCxYswNjYGIVCQWxsLMbGxmRmZpKcnIyxcV7fQ29vb8koM3nyZNq3b090dDShoaE4OjrSoUMHAOrUqcPw4cPZsWMHAwcO1Gr2tIgR/PwU1s3UxRWht3YRemsXobf2KKxaFwsjzH9DVlYWSUlJWFlZ/c+vHR8f/5Yju2LFity7d4+YmBguX76s4chWqVRUrFgR+HBWdFRUFEuXLuWvv/6icuXKknv8zb2SbzrK86+TkJBAYmIitWvX1hinjY2N5CDXZvZ0Ud3gWxQRWmsXobd2EXprF6G39iiqWhfKotHGxoYHDx78V9fo3bs3ffr0wdXV9X80qv+PlZUVMTEx5ObmSq1r8o0lVlZWdO3aFR8fH+n458+fSwWYnp4emzZtokWLFqjVasaPH8+pU6coU6YMqampTJgwgd69eyOTybh9+7bGbCDkFaw1a9YE8pbxIa94LF++vJRZnU9UVJTUu1Kb2dMCgUAgEAiKH4XaPf3fkJSU9Nmu7eDggFqtxs/PT4r027dvHwDdunXjyJEjXLx4kdzcXP766y/69u3L1q1bARg2bBgJCQk8efKE2NhYjh07RqlSpThy5AhZWVkYGBggk8mIjY1l2bJlQF4fxXz8/f1JSEggJSWFJUuW0KJFCywtLXFzcyM8PJzjx4+jUqm4e/cuW7ZskQw12syeFggEAoFAUPwolDON/y2DBw8mNjaWOXPmsHXrVlJTU2nUqBHnz59n+PDhDB06lICAAPbu3UtSUhJVqlTB29ubFi1aAHkFp4+PDz/99BNmZmYMHDiQxYsXc+rUKWxsbHj27BmVKlVi06ZNrF+/HjMzM9q0aUNMTAx//vknVlZWTJo0iZcvXyKTyahevTpeXl4APH78GAMDA/r160dCQgIAKSkpBAUFsWjRIhYtWiS5t83NzTExMeHhw4dUqVIFgOTkZFq1akVubi4tW7bkwoULHDx4kDVr1iCTyZg7dy5TpkxBpVIhk8m4cuUKvXr1YtiwYWRkZNCtWzcyMzPR1dVFR0eHJ0+eYG9vz+TJk/Hy8iItLQ3Ia7Mjl8t5+PAhdevWLZD+KpWq0DnDihuF1YFXXBF6axeht3YRemuPwqp1sYgR/Kds3boVBwcHPDw8KF++PP3796dq1ar4+vqSlZWFv7//W/nOo0ePZteuXdSvX5+JEycik8k4c+YMubm5TJw4URI0KSmJ/v374+rqKhWkEydOJDk5mR07dhAaGsqff/7J2LFjGTp0KPfu3WPAgAGcO3cOZ2dn5HI5tWvXxtfXV3KJHzt2DBsbG0JDQ8nJySEoKIjGjRsTERGBh4eHRnpLUlISZ8+exdDQkJSUFBwdHTl//jzHjh0jKiqKLl26UKNGDTZv3oxCoaBnz5788MMPeHh4ULZsWczMzNiwYQPVqlXj0KFDLFmyhG+//ZY+ffqwZs0avv/+e+zt7bl79y59+vT5R70n7969+z/7LQUfpqhGURVVhN7aReitXYTe2qOoal0si8Z30a1bNxQKBQqF4oP5zpaWlly8eJHjx49LfR6nT5+Os7MzAGfOnEGhUDBx4kR0dHQwMDBg1qxZODs7SzOHBgYGjBw5EplMRv369bG1teXJkycfHeP+/ftxd3eXMqubNm2Ku7s7e/bsoW3btgDUr19farqdkpIC5M2sGhoaUrNmTSwsLOjatat0jJ2dnWSG2bVrFyNGjKBWrVqSJiEhIRw+fJjevXtjYGBASEgIubm5NGzYkF9//bXAudMg3NPaoLA68IorQm/tIvTWLkJv7VFYtf7Xu6f/zt/zqt+X75zfoPvN/pBvHpuYmEi5cuU0fuz8Y/OLM3Nzcw1ntUKh+CQn8vvGFR4eLr1/VzpLfnGrUqlQq9XvzJ3OH9+SJUtYvny59H1OTg5169bFwMCA3bt3s379eiZMmEBaWhpOTk7MnDlTw2X9KRRVV1hRRGitXYTe2kXorV2E3tqjqGr9rykaY2JipIbhH8p3zm9jExMTI+0jzC8GIS8bOjY2FpVKJf3g+a5lCwsLHj9+/I/HaGNj80EHdOPGjd/Z7Dy/QB03bhwZGRnvvb6VlRVeXl7SrGn+2M3MzEhLS+P58+esWLECgHv37jF+/Hg2btzIlClT/vEzCQQCgUAgKB4UW/e0np6e1FLm73wo37ls2bK0adOGZcuWkZycTHJyMkuXLpXObdWqFQDLly8nMzOThIQEFi5cSJMmTShfvvx/NeZu3boRHBzMpUuXUKlUXL58meDgYMkB/TE+5hjv0aMHGzZs4M8//wTycqednZ25evUq6enpDBs2jLCwMNRqNWXLlkUulxc4d1ogEAgEAkHxpNjONHbr1o1Vq1ZpLNUChIWFcfjwYV68eEHv3r2BvObZTk5ONG7cGICFCxfStm1bmjdvjpGRkdSKpkuXLujq6tKjRw/OnDlDUFAQarWaKlWqsGfPHiCvqfirV69o1aoVSqWSJk2aSC1zoqOjOXDgAA0aNMDBwYFXr14Bec25bWxs+O6774iIiGDEiBFkZWWhUChwdHSkU6dOQJ7zOjU1latXr3Lr1i1pBhJgxowZXLt2jdzcXLZt24arqyuRkZFcunSJly9fcuXKFVxcXOjYsSOjR48mNjYWyFt6nzJlCnPmzMHHx4d58+YxadIk1Go1JiYmBV6aBuGe1gaF1YFXXBF6axeht3YRemuPwqr1v9o9DTB06FCGDh0qvc9vhH3nzh327t1Lbm4uAwcOpF69elSoUIHQ0FDp2BMnTpCbm8vly5c5efIk06ZNQyaTceHCBQ4cOMCCBQv47rvvCAkJ4dGjR7i7u/PHH3/w9ddfc+nSJRo0aMDSpUsxMDDA19cXAA8PD409j5s3b0apVDJo0CCpkLx8+TL79u17Kz96+/btDBw4kBYtWnDo0CFWrFjB1q1bOXLkCDNnziQrK4uFCxcSGRlJ48aN8fT0JCMjg4EDB9KpUyc8PDx4+fIlXl5eNGnShJMnTxIaGsq0adMYNmwYzs7O5Obm4unpSZs2bTSyrxctWoSLi4sUY/gpCPe09iiqDryiitBbuwi9tYvQW3sUVa2LbdH4PkaOHEmJEiX466+/aNGiBbdu3cLT05PVq1dLjqa1a9dSsWJF9PX1yczMREdHhyZNmmBoaEjz5s1RqVQMGTIEQ0ND6tWrR9myZYmJiaFq1aqcPHmS48ePY25uDuQ5rxs1asSdO3ckw8qwYcOkGVAHBweuX78O8NH8aIDWrVtL33fp0oU9e/Zw7NgxPD09NZ7z3LlzKJVKxo8fj0wmw9raGm9vb7y8vJgwYQKQZ9Dp3Lmz5JD+lOzrT0G4pz8/hdWBV1wRemsXobd2EXprj8KqtXBPvwczMzN27drFiRMnaNy4MSqVCgsLC2kWr1atWuTm5kqFYk5ODnp6elI6S36B9XeHcm5urjST2KNHD4176ujoEB0dLRWNJ0+exM7OjoyMDI4ePUpMTAx9+vTBwMDgg/nRAJUrV9b43traWmr18yYxMTG8fPmSr7/+WvpMrVaTnZ1NYmIikGfcebOlzqdkX38KRdUVVhQRWmsXobd2EXprF6G39iiqWv/rikaAly9fvvWZm5sb8+bNo1mzZpiZmXHgwAEgb/Zv3bp10szhh8jvjXj8+HGN/YaPHj2iQoUKUnHXp08fIM+hHBUVRcOGDdm1axezZ8/+oHsa8nKi3yQ6OlrKi34TKysrKlasyIkTJ6TP0tLSSExMpHTp0gAabYGys7MZMWIE48eP/2D29afwV2I6cp0sAMyM9Chjol/gawgEAoFAIChcFOmiMTw8nM2bN/P06VMyMjKoV68eCxYs4LfffmPPnj3UqlWLI0eOYGRkhIuLC5A3y7dp0yZUKhU3btzAzs6OtLQ0fv75ZxITExkzZgzVqlXjxYsXlClThqSkJGJiYvD19WX//v188803QF6T7wEDBgB5M3g+Pj5s2LCB1q1bM2zYMJKSknj9+jWWlpY8evSIAQMG0LdvXwC2bduGvb0948ePB+DGjRvs27ePrl270rt3b7755huysrKwsrIiMTGRfv36SWPPzMzkwoULlCtXjm+++YZbt25hb29Ps2bNpHt+8cUXrF27lqioKFxcXNizZw+5ublMmTKF33//HX19fRITE8nNzeXp06dUqlSJ7OxsMjIyWLduHatWrdIoVN/Mvv4U5h+5T/Z/2lIu6FJXFI0CgUAgEBQDimzRGBcXh7e3N2vWrMHBwYGkpCQ8PDzw9/enadOm3Lx5kwYNGnDp0iUePnzI4MGDAXByciIjI4OIiAgpqm/69Omkp6fTvXt39u7dS82aNfHw8GD37t3S/dLT0/n55595/PgxJ06c4Mcff5SKxszMTAwMDGjSpAnR0dH4+PhgYmJCTk4OGRkZ5ObmvrUn8Ntvv2XLli3079+fRo0a0b17d/z8/ChZsiSmpqY8e/aMxMREUlJSaNasmXSeoaEhtWrV4vbt22RmZgJ5Rd25c+dYtWoVgYGBTJkyhTNnznD79m2GDx9OixYtUCgUmJmZSTGCv/zyC8uWLWPw4MEcO3aMxMRE5HI5urq6ZGZmkpKSwqtXryhdurRG9vUnoVajVsv+81Jd6FxixYHC6sArrgi9tYvQW7sIvbVHYdW62LunS5cuzdGjR6lYsSJpaWnExcVRqlQpafnWzMyMiRMnolAoqFevHr169eL69esazbE9PT1JTEzkm2++4fjx41y8eJHmzZuzYMECybzi5OTE0qVL6dKlC3p6etSqVYuQkBDc3d1JSEjAwsKCL7/8krp16yKTyTh27BgDBw6UzCaQ1/4H8vYnNm7c+K02QDt27ADyYgS9vLyk5WsAb29vQkNDsbOzA6BSpUrS8VeuXKF///6MGDEChULBhAkTCAwMZPHixZiamtKsWTPs7Oxo2rQpY8aMoWHDhqxdu5YKFSrg7u5Ojx49aNWqFefOnaNevXrIZDK+++47nJyc+PLLL5HL5f8oRjA9I52s//z9paalcSP6jwJfQ/BpFFUHXlFF6K1dhN7aReitPYqq1kW2aFQoFBw5coQ9e/Ygk8moWbMmaWlp6OrmPVL58uVRKBTS8dbW1pw8efKt6+SbTNzc3Hj9+jUGBga0aNHiLfPKmzGE9erVo1q1ahw9epSOHTty8eJFZs6cCcCzZ89wcnLSuMffowHfx4fiDfMxMDB467z8Btz5m2rfFSP48uVLMjIy8Pb21igEs7OziYmJwcnJiR07dhAQEMDIkSNRqVS4uroyadIk9PULtrxsbGSM4j8zjSVMTKha2a5A5ws+TmF14BVXhN7aReitXYTe2qOwal3s3dPHjx9n586d7N69W3L5zp8/nxMnTrBv3z6eP3+OWq2WzB7R0dFSROCb5JtXVCoVPXr0wMfHB3jbvCKTyTh8+DCbNm3i6NGjuLm5cfToURQKBY0aNZJmMPNjBt8kNjaWqlWrSq8PHz6s0SKnQYMG6OnpYU+2iAAAIPVJREFUYWVl9d54ww/xpqHlfZQqVQp9fX22bt0qzVpCXsNwS0tLEhMTUalU+Pv7k5uby2+//YaXlxdVqlTRmPn8JGQyZNJLWaH6h1HcKKoOvKKK0Fu7CL21i9BbexRVrYtsjGBqaipyuRwDAwPUajU//fQTBw8elNrDJCQksHnzZrKzs7l16xb79u2je/fuAOjr65OWloZarcbS0pLWrVvj4ODAuHHjyM7OZsOGDXTr1o2UlBSNe3bq1ImjR49Kr+/fv8++fftwdXWVjunRowd79+7l1q1b5OTksH//fm7cuCF9X65cOSnhJZ8lS5Zw5coVevbs+d54Q8gr/PKfoaDI5XK6devGihUriIuLIzc3lwMHDuDi4sLTp0+JjY1l8ODBXLp0CblcLhXT/yRGcJZLLRZ0qcuCLnUxMxL9GgUCgUAgKA4U6plGPz8/QkJCeP36NRUqVGD06NGYmJgwadIkunTpwqtXr2jZsiV6enp88cUXDBgwgM2bN5Oamoqenp7kBC5ZsiTe3t506NCBtLQ07t+/z/3796lVqxZt27ZlypQpfP/997i4uPDixQsqVqyIsbEx7du3p2bNmtJ48tvvhIeHU7JkScqWLcv9+/dZuHAhe/bsYcKECTg5OREZGcngwYNJT09HJpOhr6/P48ePgbwZz+joaPr27cuwYcMAmDhxIh06dGDhwoU8ePCAnj17olQq0dPTo2PHjjRq1AjIy5betWsXW7ZsISkpienTpwMQHBzM1q1biYuLA+DJkyesWrWKBw8eSEv3AFOmTGHs2LE4OjqSk5ODvr4+o0aN4osvvgCgX79+DB8+HKVSiY6ODnXr1qVt27YF/t0qmxuL5t4CgUAgEBQzCm3RePnyZYKDgwkNDcXCwoLg4GBmzJjBihUriI+PJzIyksuXL/PixQuGDRtG06ZN8fLyIjY2lrCwMExNTfn55585ePAg06ZN49tvvwWQnNIXL16UYv6mT5/O7t27GT58OI6OjtSsWZPFixdLMX/u7u7Y2NgQEREhje/QoUOkp6fTtWtXFixYwNq1a5k3bx5Lly6lXLlyZGVlsXHjRlq0aIGTkxMnT57k0aNHuLq6EhERQalSpbh16xa2trZs2bIFe3t7Ll++zPHjx98bI5h/neDgYKysrChZsiSTJk0iLCyM4OBglEolzs7OrF+/nqCgIKytrRkyZAgZGRmSplevXuX777+nYcOG/PTTT4wfP5727dtTo0YNjh07ho+PD127diU6OppevXoRHh7+1h7NjyGypz8/hdWBV1wRemsXobd2EXprj8KqdZF3T+vr65OcnMzevXtp06YN3bt3x93dnYiICGQyGXPmzMHExAQTExOGDh3Kpk2bGDt2LADVq1cnNTUVmUyGs7MzU6dOJSoqCl1d3f8q5i+fZ8+eERkZSVJSEiVLluTJkyd4e3szbtw4vv/+e/z9/WnZsiUtW7bk+PHjxMfHs2bNGmnJ9318SoygnZ2dxuwnQN++faWx16hRgy+++IJq1aoB0KRJE3799VcAdu7cSa9evaSUmDZt2uDg4MCePXuYNWsW+vr6HD9+HDMzM77++mvOnz//j9zTIntaexRVB15RReitXYTe2kXorT2KqtaFtmhs0KABfn5+kqPXwMCAfv360bBhQ0xNTTX22llbW/P8+XPpvZGREampqQDSMqlKpfrkmL8yZcpI3+nq6qJWqzWO37t3L9u3b6dt27bcvn2bXbt2YW5uzsiRI+nbty/btm3j559/pmHDhlStWpUVK1bw1VdfUaJEiQ8+c2Ji4kdjBN9liskfd/6zmJqaSu/z3dOQ5xS/cuUKP/zwg2SeUalUNGnSBMhrOu7n58e8efNISEigRYsWzJ07Fysrqw+O+++I7OnPT2F14BVXhN7aReitXYTe2qOwal3k3dOxsbGYm5sTGBiIUqnk0qVLeHh44OfnR2pqKq9fv8bQ0BB42xldqVIljcbc+fw95s/Pz4+IiAhatmzJ2rVr2bx58yeNzdvbm06dOpGTk0ONGjXIzMzkxIkTTJkyhUaNGtGsWTNev37NihUrgLzZvvbt279lgPk75cuXf2eMoFKp5MqVK8C7ndKf4p6GvN6WkZGRHDp0CBsbGzZu3MjPP//MwoULycrK4tGjR8ydOxddXV2ePHnCzJkzWbRoEWvXrv2k6+dTVF1hRRGhtXYRemsXobd2+X/t3XlU1PX+x/EniywJBSiFB1NvZnhdUhG3cEm9aSmmgMv1llZmmhtXDPoZqeGtTG4qpTfNWDSTsnLJRE3MpTQXIis1M70qouBRA1woZQTm9wfH75HrMiA6A+Prcc6cI9/vzHzf846j7z7fz3veyrf1VNdcV9nu6T179jB8+HD279+Pi4uLcTv5wIEDFBcXExcXR2FhIYcPHyYpKcn4Au0budwp/eabb5Kfn2+sPr733nt8+OGHFYpv06ZNjB07luPHj+Pm5oaXlxfOzs54enoSGhrK+vXr2bp1KyUlJeTn57N8+XKLK43h4eFs3LiRtWvXUlxczL59+0hISKhQXDfyxBNPcOnSJeN7H4ODgzl48CCpqak4ODgwYcIEkpOTKSoqwtfXF2dn55vqns7M/YP/njpfqcfvBYW37HOLiIhI5VXZlcaePXuSmZnJqFGjyM/Pp1atWsTExBjfd3jPPffQvXt3AP7+978zfPhwi++5a9cuTp48yYEDB/j6668xm83Gbe9BgwaxaNEiAGNayscff8yFCxfw8vLCZDIBpR3MERERHDx4kBMnTjBgwAD++OMPatSogZubG+Hh4bRv356YmBji4uI4eLB0GkpJSQmbN2823iMsLIzMzEwAvvrqK9q0aUOLFi2YNWsWsbGxjB8/HkdHR4KDg42uaIDCwkImT57Mpk2bjJnQl88fP36c9PR0PDw86NatG2fPnsXb25tatWpRXFzM22+/DcDYsWNxcXHBxcWFe+65x5ht/cQTT/Dee+8ZK6T+/v7MmDGjwv/tXl/1KyZz+VY/r+eNfk3xdq+yv542V1U3U9sr5du6lG/rUr6tp6rmuto3wgCMHDmSkSNHljl2+TZtZGQkkZGRV71m+vTpVx377bffyM/Pp0ePHrzwwgt8/vnn7N69mxEjRtCkSRNjrnLdunVZtGgRQ4cO5ciRI2zYsMHozp47dy7jx48nPT29TAezu7s7ISEhNGvWjNjYWMxmM1OnTuWzzz5jxYoVODs7ExAQQFJSktEhPX/+/Ks6pOvXr8+zzz7Lr7/+iqenJ59++im1atUiNja2zGcbMmQIeXl5rFixAk9PT959913i4+ONAhpK9zF+8cUXRvd3QEAATk5OpKam0r17d77++mvq1q1r3J6/PP7w66+/ZuXKlTRo0IBDhw7xj3/8g82bN1f4uyGvHCN4szR+sHyq62bq6kr5ti7l27qUb+uprrmu0kXjrbR582bc3d154YUXcHBwoHXr1oSHh/Prr79e9VxL3dlXdjBv376dY8eOsWzZMjw8PACYOnUqbdu2Ze/evWWmr4DlDumVK1fy4osvGuMEJ02axJdffgmU7m9MT09n9erV+Pr6AqXf8bhq1Sq++eYbWrRoAVju/r6Wzp07ExgYiJ+fH3l5eeTn5+Pl5WXM8q6IK8cI3iyNH7yxqrqZ2l4p39alfFuX8m09VTXX1b4R5lY7efIkderUKdM0Uq9evWsWjZa6s6/sYM7NzcXb29soGAE8PDzw8vIiOzv7qqLRUof0qVOnqFOnjnHu7rvvNrqhf//9d6DsLGsnJyfq1KlDdna2UTRa6v6+FrPZTHx8PJs2bcLHx4e//vWvXLp0qVyvvcoVYwRvlsYPlk913UxdXSnf1qV8W5fybT3VNdfVrmhs164dv/32W4Vf5+fnR3Z2NiUlJcZ3D165V/BKlrqzryw8/f39yc/Pp6CgwCgcz58/T35+vrEaCDBt2jRWrlzJpUuX+Pjjj4mKigJg4cKFxMfHGw0q/zt/+s8//zS+Psjf3x+ArKwsGjVqBJT+30FOTk6Za92MGTNmkJOTw8aNG43P0adPn5t6r8khjXF0qtyvlsYPioiIVC1Vtnv6VuvWrRtms5k5c+ZgMpnYu3cvn3/++TWfW5Hu7ObNm/Pggw/y2muvcf78ec6fP09sbCz16tUjMDAQKN1jeHmT6UsvvYTJZDI6pBcuXEiNGjUYN24cjRs3ZsCAASQmJnLo0CEKCwuZPn268dp7772XLl268MYbb3D69GkuXrzIjBkzKC4upmvXrhZz4OrqCkBBQcFV5woKCnB1dcXJyYnCwkKSk5M5cOCA0WxTEQ1q1eTBez0r9ajt4Vrh64qIiMjtU+1WGm/W3XffTVJSErGxsSxYsID69evTs2dPjhw5cs3nX687+9dff+Xo0aNs3LjRmIf94osvEhkZyfbt2zGZTDzyyCMEBAQwadIkpk+fTrNmzdizZw/x8fHUr18fT09PEhISjD2STk5O7Ny5kzFjxtCqVSs++eQTevfuDUCLFi2M29Nz5szhwoULHDx4kE6dOuHq6orZbOapp55i6NChRjf2gQMHmDp1KhkZGbi6uhq3u2vXrk1gYCD9+vXDxcWFmjVrGl8MPn78eMaPH09gYCBmsxlXV1fuv//+m5ruojGCt19V7cCzV8q3dSnf1qV8W09VzbVddE/fao0bN2bJkiXXPBcWFlbm52t1Z+/YsYPc3FzS0tKumocNsG3bNuO5EydONP7cuXNn3NzciIyMZPny5bi7u7N8+XKgdAV07NixhIWFcfjwYZ5//nmioqIYPHgwJ06cYNy4cTz55JO0a9eO9PR0MjIySE5O5uGHH8bV1ZUePXrwzTffkJycjIeHB+Hh4URFRZGUlETTpk15+eWXycvLA0q7yPft28fs2bPp3r07P//8M6NHj2bLli106tQJd3d3xowZw5gxY8jPz+eZZ54hODi4wnnWGEHrqa4deNWV8m1dyrd1Kd/WU11zfUcVjZV1o3nYt8KqVasICAjgmWeeAUon27z00ktEREQQExMDlDbBdOjQoczrwsPDjVF/Dz/8MAUFBbRq1QqAjh07Mm/ePACWLFlC9+7d6dGjBwCBgYEMHDiQlJQUY+Vyy5YtNGzYkA4dOrBy5cqbmj2tMYK3X1XtwLNXyrd1Kd/WpXxbT1XNtbqnryMzM5MGDRrc1GtvNA/7VsjNzS3TGQ2lndUXL14kNzcXqPzs6R07dhAUFGScLy4upl69egC88847zJkzh/j4eCZMmEBgYCCxsbFG0015VdeusOpIubYu5du6lG/rUr6tp7rm+o5phIHS26YhISE3fM6NurOvnIednp5OXFwc77//Pj/88AOAMTUGSqe+XOnw4cPGLevz58+XmWCzZs0aWrVqxcqVKzl8+HCZ12VlZRmTW6Bys6f9/PwIDQ0lIyPDeKxbt44PPviAkpIS9u3bx7hx40hLS2Pjxo3UqlWrzG12ERERuXPdUUXj+fPnb6ob+LLrzcNu27Ytzs7OrF69Gijd27hjx47rvo+npyeJiYnGz9999x1xcXGsXr2ao0eP8uGHH2IymcjKymLWrFn06dPnltzu7d+/P6mpqcZM7MzMTJ5++mmSk5NxdHTkjTfe4J133qGwsBAfHx9cXV1vava0iIiI2B+7uz19/PhxunfvzqRJk3j//fe5ePEi3bp14/nnn+eFF14ASm8zJycn07JlSz766CNSUlLIzc3loYceIiYmhmbNmgGlTSodO3Zkw4YN+Pr6snz5crZu3cqAAQMwmUw4OTkRHBxMy5YtiYmJYe7cuUyZMgUnJyeKiorYtGkT27dvvyrGc+fOMWTIEGbPnk1OTg5ms5moqCh69epFYmIiU6ZMYfr06ZjNZjw9Penbty8lJSVA6Yrl5dnX+fn5uLi48Morr3Dp0iWSk5M5duwYvr6+7N27lzfeeINffvkFs9nM0aNHjdnWr732mnFdHx8fY4/kO++8Q3R0NC1btqSkpAR3d3d69epFUVERzs7l/1VR9/TtV1U78OyV8m1dyrd1Kd/WU1Vzfcd3T6elpbFq1SqKi4sZM2YMSUlJJCQkMHToUGOsXkpKCgsWLGDevHk0bNiQlStX8txzz7F27Vpjqsru3btZu3YtULof8osvvriqu/nf//43kyZNwt3d3bhl3aJFC5YtW8aoUaPYvHmzMfElLCyM7Oxs0tPT8fb2Zv/+/QQEBJCQkGDMps7KymLmzJllZlM7Ozszbtw4srOzy8y+vvvuuwkICGDVqlV8+umnmEwmevfuzejRo1mwYAF16tTh+eef5/333+ett97CwcGBs2fPsmjRIgIDA/n222+JjIzks88+o1GjRuTn5zNt2jRCQ0M5fvw4gwcPZsOGDfTs2bPcuVf3tPVU1w686kr5ti7l27qUb+uprrm226LxlVdewcfHB4CIiAhGjRrFk08+WeY5KSkpjBw5ksaNGwOlt2+XLl3Kl19+ybBhwwDo2bOnMcd5wYIFN+xuXrFiBYMGDTI6lwcMGEDDhg1xc3Mrd9yWZlND2dnXlz399NNGQ0yjRo1o0qQJDRs2BKB9+/bGvsvFixczePBg2rRpA0DXrl3p1q0bS5YsYfLkybi6urJ27Vq8vLxo06YN33zzTYU7qNU9fftV1Q48e6V8W5fybV3Kt/VU1Vzf8d3T9evXN/5cp04dTCYTZ8+eLfOc7Oxs4uLimDFjhnGsqKjIuD0NV8+ZvlF38+nTp8uMGwQq3FltaTb1/8Z0WUU6qNPT0/nkk0+M88XFxbRv3x6ADz/8kDlz5jB16lROnz5Np06diI2NNb7Spzyqa1dYdaRcW5fybV3Kt3Up39ZTXXNtt0XjyZMneeCBB4DSfY7u7u5XNXX4+fkRERFhTF+B0m7lKwuw/50znZaWVuY9ruxurlOnDidOnChzPj4+/qoVzhvx9/cnKyurzLHL+xSvFdONjl2Ln58f/fr1Y8SIEcaxnJwc3NzcKCws5L///S+xsbE4Oztz5MgRJk2axLRp05g9e3a5P4OIiIjYH7vtnp45cyYFBQWcPHmS2bNn07dvX2rWrAmUdlEDDBw4kHnz5nHo0CEAtmzZQu/evfn++++v+Z69e/fm0KFD1+1uDgsL49NPP2X37t2UlJSwbNkyUlJSKtSBHB4ezsaNG43Z1Pv27SMhIYHw8PBKZgTjMy9atIjdu3cDpfsqwsLCSE1NxcHBgQkTJpCcnExRURG+vr44Ozurg1pERETsd6WxXr16hISEcOHCBfr06UN0dDTFxcW0bt2aTp068e677/Lss89iNpsZPXo0p06d4r777mPKlCnGzOn/VbduXRITE5k1axZz5szBzc2NkJAQY4Z0nz59OHfuHNHR0Zw+fZoHH3yQhIQEY29lebRo0YJ3332X9957j5iYGLy9vRk8eLDR+V1Zjz/+OH/++ScxMTHk5OTg5eXFs88+y5AhQ3BwcGDevHnExcUxf/58nJyc6Ny5M1FRUbfk2iIiIlJ9OZgvb3azE5e/cmfDhg3UrVvX1uHcUYqLi/npp59o3ry5GmFus8u5btmyZbXcF1PdKN/WpXxbl/JtPVU11+WNy25vT4uIiIjIraOiUUREREQssrs9jXXr1r3u7GgRERERuTlaaRQRERERi1Q0ioiIiIhFKhpFRERExCIVjSIiIiJikYpGEREREbFIRaOIiIiIWKSiUUREREQsUtEoIiIiIhapaBQRERERi+xuIozYjtlsBkoHnxcXF9s4Gvt2Ob/Ks3Uo39alfFuX8m09VTXXl+O5/O/49TiYLT1DpJxMJhN79uyxdRgiIiJyE5o3b46Li8t1z6tolFumpKSEoqIiHB0dcXBwsHU4IiIiUg5ms5mSkhKcnZ1xdLz+zkUVjSIiIiJikRphRERERMQiFY0iIiIiYpGKRhERERGxSEWjiIiIiFikolFERERELFLRKCIiIiIWqWgUEREREYtUNEql5ebmMnr0aIKCgmjXrh1vvvkmRUVFtg7L7uXl5fHYY4+xc+dOW4di1/bv389zzz1H27ZtCQ4O5uWXXyYvL8/WYdmt7du3M2DAAAIDAwkODub111/n4sWLtg7LrhUXFzNkyBAmTpxo61Ds2po1a2jSpAmtWrUyHtHR0bYOq0JUNEqljR8/nrvuuostW7awdOlStm/fzsKFC20dll374YcfGDRoEFlZWbYOxa5dvHiR4cOH06pVK7Zu3UpqaipnzpwhJibG1qHZpby8PEaOHMngwYPJyMhgxYoVpKen88EHH9g6NLv2n//8h4yMDFuHYff27NlD3759+fHHH43H22+/beuwKkRFo1TK0aNHSU9PJzo6Gnd3d+6//35Gjx5NSkqKrUOzWytWrCAqKorIyEhbh2L3cnJyaNy4MWPGjMHFxQVvb28GDRrE999/b+vQ7JKPjw/btm0jLCwMBwcHzpw5Q2FhIT4+PrYOzW5t376dtLQ0evToYetQ7N6ePXto1qyZrcOoFBWNUikHDx7Ey8uL++67zzjWsGFDcnJyOHfunA0js18dO3Zk/fr19OrVy9ah2L0HHniAxMREnJycjGPr1q2jadOmNozKvnl4eADQpUsX+vTpg6+vL2FhYTaOyj7l5uby6quvMnPmTNzd3W0djl0rKSnhl19+YfPmzXTt2pXOnTszefJkzp49a+vQKkRFo1TKH3/8cdVfNpd//vPPP20Rkt3z9fXF2dnZ1mHcccxmM/Hx8WzatIlXX33V1uHYvbS0NL799lscHR2JiIiwdTh2p6SkhOjoaJ577jkaN25s63DsXl5eHk2aNKFnz56sWbOGJUuWkJmZWe32NOpfHqmUu+66iwsXLpQ5dvnnmjVr2iIkkVuuoKCAV155hV9++YXFixcTEBBg65DsnpubG25ubkRHRzNgwADOnj3LPffcY+uw7Mb8+fNxcXFhyJAhtg7ljlC7du0y27bc3d2Jjo5m4MCBFBQUGCvsVZ1WGqVSGjVqxJkzZ/j999+NY4cOHcLPzw9PT08bRiZya2RlZREeHk5BQQFLly5VwXgb7dq1i8cffxyTyWQcM5lM1KhRQ7dPb7GVK1eSnp5OUFAQQUFBpKamkpqaSlBQkK1Ds0v79+9nxowZmM1m45jJZMLR0REXFxcbRlYxKhqlUho0aEDr1q2ZNm0aBQUFHDt2jLlz59K/f39bhyZSaWfPnuWZZ54hMDCQpKQkNWTcZgEBAVy8eJGZM2diMpnIzs4mLi6O/v37V6t/WKuDr776il27dpGRkUFGRgYhISGEhISoi/o28fLyIiUlhcTERIqKisjJyeHtt98mNDS0Wv1uq2iUSps9ezZFRUV0796dgQMH0qlTJ0aPHm3rsEQqbfny5eTk5LB27Vpat25d5vvV5NarWbMmiYmJHDx4kODgYIYMGcIjjzyirziSas/Pz4/58+ezYcMG2rZtS3h4OM2bN2fKlCm2Dq1CHMxXrpWKiIiIiFyDVhpFRERExCIVjSIiIiJikYpGEREREbFIRaOIiIiIWKSiUUREREQsUtEoIiIiIhapaBQRERGpZvLy8njsscfYuXNnuV+zbt06QkJCaNmyJY899hhLly6t0DVVNIqIiFVkZmbaOgQRu/DDDz8waNAgsrKyyv2aHTt2MHHiRKKjo/nxxx95/fXXmTp1Krt37y73e6hoFBG5Qxw5coT/+7//o3PnzrRq1Yq//e1vzJgxgz/++OO2XzslJYXJkyff9uuI2LsVK1YQFRVFZGTkVee2bdtG//79CQoKonfv3nz55ZfGuYULFzJ06FC6dOmCg4MD7du3Z9myZdSrV6/c11bRKCJyB9i1axehoaH4+/vzxRdf8OOPP5KQkMDPP//MsGHDKC4uvq3Xz8vLu63vL3Kn6NixI+vXr6dXr15lju/fv59Ro0YxYsQIdu7cyeuvv860adPYsmULALt378bLy4sRI0bQrl07+vbtS1ZWFl5eXuW+topGEZE7wJQpU+jXrx8RERH4+PgA8Je//IX4+Hhq1arFsWPHyM7OZvz48XTo0IHg4GBeeuklTp06BcDOnTsJCAgo854TJ05k4sSJAMyZM4eIiAiioqIICgqic+fOzJw5EyhdGZk/fz4ZGRkEBQVZ8VOL2B9fX1+cnZ2vOr5kyRK6d+9Ojx49cHJyIjAwkIEDB5KSkgLA2bNnSUpKYtSoUXz33XeMGTOGyMhIfv7553JfW0WjiIidy8rK4uDBg4SEhFx1rnbt2sydOxd/f3+GDRuGk5MTaWlprF27FoAXX3yRoqKicl0nLS2Njh07GqscCQkJ/PTTT4SGhjJy5EiCgoLIyMi4pZ9NREplZ2ezfv16goKCjMdHH33EiRMnAHBxcSE8PJxWrVrh7OxMjx496NChA+vWrSv3Na4uVUVExK5cvjVcu3bt6z4nIyODY8eOsWzZMjw8PACYOnUqbdu2Ze/eveW6ToMGDejXrx8AXbp0wdfXl8zMTFq2bFmp+EXEMj8/P0JDQ/nXv/5lHDt16hRmsxmAhg0bYjKZyrymuLjYOF8eWmkUEbFzvr6+AJw+ffqa53///Xdyc3Px9vY2CkYADw8PvLy8yM7OrtB1LqtRowYlJSU3GbWIVET//v1JTU1l69atlJSUkJmZydNPP01ycjIAgwcP5pNPPmHbtm2UlJSwbt06du7cec07ENejlUYRETvn7+/PQw89xJo1a2jTpk2Zc7m5uXTt2pWxY8eSn59PQUGBUTieP3+e/Px8fH19cXQsXWMwmUy4uLgAkJ+fj7e3t3U/jIhcU4sWLZg1axazZs3in//8J+7u7oSEhDBhwgQAwsPDcXR05K233uL48eP4+/sTHx9P06ZNy30NFY0iIneAyZMnM3z4cGrVqsVTTz2Fl5cX+/fvZ8qUKTRt2pRhw4axevVqXnvtNWJjYwGIjY2lXr16BAYGkpeXh7OzM6tXryY0NJRt27axY8cOnnjiiXJd39XVlYKCAsxmMw4ODrfxk4rcOX777bcyPz/66KM8+uij131+aGgooaGhN3093Z4WEbkDtG3blsWLF7Nv3z569+5NYGAgERERtG/fnsTERGrUqMH8+fMpKiqiZ8+edO3alUuXLrFgwQKcnZ259957iYmJYe7cuQQGBrJ48WLCwsLKff2uXbty5swZWrduzblz527jJxWR28XBXJEdkCIiIiJyR9JKo4iIiIhYpKJRRERERCxS0SgiIiIiFqloFBERERGLVDSKiIiIiEUqGkVERETEIhWNIiIiImKRikYRERERsUhFo4iIiIhYpKJRRERERCxS0SgiIiIiFv0/pMmXXdNiEi4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "len(serengeti_df_mdv4)"
+    "sns.histplot(df_clean.loc[df_clean[\"original_label\"] != \"human\"], y = 'order')"
    ]
   },
   {
@@ -21256,6 +3901,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,py:percent"
+  },
   "kernelspec": {
    "display_name": "std",
    "language": "python",
@@ -21272,8 +3920,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.3"
-  },
-  "orig_nbformat": 4
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2