add more explanation of the dataset

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README.md

@@ -1,30 +1,39 @@
----
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: image
-    dtype:
-      image:
-        decode: false
-  - name: prompt
-    dtype: string
-  - name: score
-    dtype: float32
-  splits:
-  - name: train
-    num_bytes: 145918590.0
-    num_examples: 3200
-  - name: test
-    num_bytes: 53594464.919
-    num_examples: 1719
-  download_size: 207408977
-  dataset_size: 199513054.919
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+# CogIP-Bench: Cognition Image Property Benchmark
+
+**CogIP-Bench** is a comprehensive benchmark designed to evaluate and align Multimodal Large Language Models (MLLMs) with human subjective cognitive perception. While current MLLMs excel at objective recognition ("what is in the image"), they often struggle with subjective properties ("how the image feels"). This gap is what the **CogIP-Bench** aims to measure.
+
+This dataset evaluates models across four key cognitive dimensions: **Aesthetics**, **Funniness**, **Emotional Valence**, and **Memorability**.
+
+## 📂 Dataset Structure & Files
+
+The dataset is organized into two main formats: standard benchmark files (`.jsonl`) for evaluation and a source JSON file (`.json`) used for Supervised Fine-Tuning (SFT).
+
+### 1. Benchmark Data (`metadata_train.jsonl`, `metadata_test.jsonl`)
+
+These files contain the image-prompt pairs and ground truth scores used to benchmark model performance against human judgments.
+
+* **`metadata_train.jsonl`**: The training split, containing **3,200 examples** across the four dimensions.
+* **`metadata_test.jsonl`**: The testing split, used for final evaluation.
+
+#### **Data Fields**
+
+Each line in the `.jsonl` files represents a single datapoint:
+
+| Field | Type | Description |
+| :--- | :--- | :--- |
+| `id` | `string` | A unique identifier for the image (e.g., `"000800"`). |
+| `image` | `Image` | The image file (stored locally in the dataset structure, e.g., `"images/Aesthetics/000800.jpg"`). |
+| `prompt` | `string` | The specific instruction given to the MLLM, employing the **"Describe-then-Predict"** strategy. |
+| `score` | `float32` | The ground truth human-preference score, which is the target value for the model to predict (e.g., `4.105`). |
+
+#### **Example Entry (from `metadata_test.jsonl`)**
+
+This example shows a prompt for the Aesthetics sub-task, which includes detailed instructions and the scoring scale.
+
+```json
+{
+  "id": "000800", 
+  "image": "images/Aesthetics/000800.jpg", 
+  "prompt": "<image>  \n**Visual Aesthetics Analysis Sub-Task (Aesthetics):** \nIn this sub-task, you are asked to assess the aesthetic appeal of the image based on elements such as visual harmony, composition, color, lighting, and emotional impact. Your goal is to provide a descriptive label that captures the overall aesthetic quality of the image, followed by a numerical score that reflects its aesthetic value.\n\nPlease first give a description label for the corresponding image, then predict the scores based on the following rules:  \n- (0.0, 3.5, 'very low')  \n- (3.5, 5.0, 'low')  \n- (5.0, 6.5, 'medium')  \n- (6.5, 8.0, 'high')  \n- (8.0, 10.1, 'very high')  \n\nThe score should be a number with exactly three decimal places (e.g., 7.234).  \n\nPlease return only the label and the scores number, nothing else.", 
+  "score": 4.105
+}