Upload dataset.py

dataset.py

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+from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Features, Value, ClassLabel, Image, Sequence
+import csv
+import datasets
+import ast
+
+class CAFOSatConfig(datasets.BuilderConfig):
+    def __init__(self, split_column="cafosat_set1_training_train", **kwargs):
+        super().__init__(**kwargs)
+        self.split_column = split_column
+
+class CAFOSat(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        CAFOSatConfig(name="set1_train", split_column="cafosat_set1_training_train", description="Set 1 training split"),
+        CAFOSatConfig(name="set1_val", split_column="cafosat_set1_training_val", description="Set 1 validation split"),
+        CAFOSatConfig(name="verified_train", split_column="cafosat_verified_training_train", description="Verified training split"),
+        CAFOSatConfig(name="all_train", split_column="cafosat_all_training_train", description="Verified training split"),
+    ]
+    DEFAULT_CONFIG_NAME = "all_train"
+
+    def _info(self):
+        return DatasetInfo(
+            description="CAFOSat: Remote sensing CAFO dataset with bounding boxes and infrastructure tags.",
+            features=Features({
+                "patch_file": Image(),
+                "label": ClassLabel(
+                    names=["Negative", "Swine", "Dairy", "Beef", "Poultry", "Horses", "Sheep/Goats"]
+                ),
+                "barn": Value("float32"),
+                "manure_pond": Value("float32"),
+                "grazing_area": Value("float32"),
+                "others": Value("float32"),
+                "geom_bbox": Sequence(Value("float32")),  # Keep as raw list
+                "category": Value("string"),
+                "state": Value("string"),
+                "image_type": Value("string"),
+                "CAFO_UNIQUE_ID": Value("string"),
+                "verified_label": Value("string"),
+                "patch_res": Value("string")
+            }),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/oishee3003/CAFOSat/",
+            license="cc-by-4.0"
+        )
+
+    def _split_generators(self, dl_manager):
+        csv_path = dl_manager.download_and_extract("cafosat.csv")
+        return [
+            SplitGenerator(name=Split.TRAIN, gen_kwargs={"csv_path": csv_path, "split_flag": self.config.split_column})
+        ]
+
+    def _generate_examples(self, csv_path, split_flag):
+        with open(csv_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                if row.get(split_flag, "0") != "1":
+                    continue
+
+                # Parse bbox without scaling
+                try:
+                    bbox = ast.literal_eval(row.get("geom_bbox", "[5.0, 5.0, 700.0, 700.0]"))
+                except:
+                    bbox = [5.0, 5.0, 700.0, 700.0]
+
+                yield idx, {
+                    "patch_file": row["patch_file"],
+                    "label": int(row["label"]),
+                    "barn": float(row.get("barn", 0)),
+                    "manure_pond": float(row.get("manure_pond", 0)),
+                    "grazing_area": float(row.get("grazing_area", 0)),
+                    "others": float(row.get("others", 0)),
+                    "geom_bbox": bbox,  # ✅ unchanged
+                    "category": row.get("category", ""),
+                    "state": row.get("state", ""),
+                    "image_type": row.get("image_type", ""),
+                    "CAFO_UNIQUE_ID": row.get("CAFO_UNIQUE_ID", ""),
+                    "verified_label": row.get("verified_label", ""),
+                    "patch_res": row.get("patch_res", "")
+                    "refine_x": row.get("refine_x", "")
+                    "refine_y": row.get("refine_y", "")
+                }