Add dataset card

README.md

@@ -1,34 +1,86 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_id
-    dtype: string
-  - name: task_type
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: metadata
-    dtype: string
-  splits:
+language:
+  - en
+license: apache-2.0
+task_categories:
+  - visual-question-answering
+  - image-classification
+pretty_name: FineSightBench
+size_categories:
+  - 1K<n<10K
+tags:
+  - VLM-evaluation
+  - fine-grained-visual-perception
+  - fine-grained-visual-reasoning
+splits:
   - name: perception
-    num_bytes: 7881595
     num_examples: 3500
   - name: reasoning
-    num_bytes: 10605766
     num_examples: 2520
-  download_size: 55425872
-  dataset_size: 18487361
-configs:
-- config_name: default
-  data_files:
-  - split: perception
-    path: data/perception-*
-  - split: reasoning
-    path: data/reasoning-*
 ---
+
+# FineSightBench
+
+FineSightBench is a fine-grained visual benchmark for evaluating Vision Language Models (VLMs) on pixel-level perception and reasoning tasks.
+
+## Dataset Structure
+
+The dataset consists of two splits:
+
+### `perception` (3 500 images)
+
+Fine-grained single-target identification tasks at 8 pixel-size difficulty levels.
+
+| Task | Description |
+|------|-------------|
+| Color Identification | Identify the color of a target object |
+| Letter Recognition | Identify a rendered letter |
+| Animal Recognition | Identify an animal silhouette |
+| Shape Recognition | Identify a geometric shape |
+| Dot Color Recognition | Identify the color of a tiny dot |
+
+### `reasoning` (2 520 images)
+
+Chain-reasoning tasks requiring counting, ordering, and spatial reasoning.
+
+| Task | Description |
+|------|-------------|
+| `spatial_chain` | List objects left→right or top→bottom |
+| `comparison_chain` | List objects smallest→largest by size |
+| `counting_chain` | Count objects per type + total |
+| `blur_chain` | Count objects on blurred background |
+
+## Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `image` | Image | 448×448 PNG canvas |
+| `image_id` | string | Unique identifier |
+| `task_type` | string | Task category |
+| `question` | string | Prompt for the VLM |
+| `answer` | string | Ground-truth answer (JSON string for reasoning) |
+| `difficulty` | string | `easy` / `medium` / `hard` / `extreme` |
+| `metadata` | string | JSON with canvas_size, pixel_size, targets list |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Volavion/FineSightBench")
+print(ds)
+# DatasetDict({
+#     perception: Dataset({features: [...], num_rows: 3500}),
+#     reasoning:  Dataset({features: [...], num_rows: 2520})
+# })
+
+sample = ds["perception"][0]
+sample["image"].show()
+print(sample["question"])
+print(sample["answer"])
+```
+
+## Canvas Design
+
+All images are 448×448 pixels on a white background.  
+Object pixel sizes range from **3 px** (extreme) to **48 px** (easy), controlling task difficulty.