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README.md

@@ -1,23 +1,116 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_path
-    dtype: string
-  - name: ground_truth
-    dtype: string
-  - name: conversations
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2520353520
-    num_examples: 10000
-  download_size: 2526354405
-  dataset_size: 2520353520
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+language: en
+license: apache-2.0
+size_categories:
+- 10K<n<11K
+task_categories:
+- visual-question-answering
+- object-detection
+tags:
+- uav
+- drone
+- reasoning
+- vqa
+- surveillance
+- chain-of-thought
+- military
+- object-detection
 ---
+
+# UAV Drone Reasoning VQA Dataset
+
+A visual question answering dataset for drone/UAV detection and reasoning, featuring
+chain-of-thought reasoning with **thinking → answer** pairs.
+
+## Dataset Description
+
+This dataset contains **10,000** surveillance images annotated with:
+- **72,019** question-answer pairs (avg 7.2 per image)
+- Ground-truth bounding boxes with UAV class labels
+- Chain-of-thought reasoning ("thinking" field) for each answer
+
+### Intended Use
+Training and evaluating multimodal models for:
+- UAV/drone detection and classification in surveillance imagery
+- Visual reasoning with chain-of-thought explanations
+- Scene understanding (weather, visibility, lighting conditions)
+- Threat assessment and detection difficulty analysis
+
+## Dataset Structure
+
+Each row contains:
+| Field | Type | Description |
+|-------|------|-------------|
+| `image` | Image | The surveillance image |
+| `image_path` | string | Original relative path |
+| `ground_truth` | string (JSON) | Bounding boxes and class labels |
+| `conversations` | string (JSON) | List of QA pairs with thinking/reasoning |
+
+### Conversation Format
+```json
+[
+  {
+    "question": "Is there a UAV visible in the sky?",
+    "thinking": "Scanning the sky reveals a small object...",
+    "answer": "Yes, a UAV is present."
+  }
+]
+```
+
+### Question Categories
+- **Detection**: Presence/absence of UAVs
+- **Counting**: Number of objects
+- **Classification**: UAV type/model identification
+- **Localization**: Bounding box coordinates
+- **Scene Analysis**: Weather, lighting, visibility
+- **Characteristics**: Visual features of detected UAVs
+- **Risk Assessment**: Threat level and detection difficulty
+
+## UAV Classes
+
+| Class | Count |
+|-------|-------|
+| mq9_reaper | 3039 |
+| mohajer_6 | 3016 |
+| shahed_238 | 2995 |
+| shahed_136 | 2962 |
+| dji_mavic | 2954 |
+
+## Source Image Splits
+
+| Split | Images |
+|-------|--------|
+| train | 7003 |
+| val | 2001 |
+| test | 996 |
+
+## Generation Pipeline
+
+QA pairs were generated using **Qwen3.7-Plus** (multimodal) via OpenRouter API,
+with ground-truth YOLO annotations as guidance. Each response was validated for:
+- Valid JSON structure
+- Minimum 3 QA pairs per image
+- Required fields (question, thinking, answer)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("yunus-emre/uav-drone-reasoning-vqa")
+
+# Access a sample
+sample = ds["train"][0]
+image = sample["image"]  # PIL Image
+qa_pairs = json.loads(sample["conversations"])
+gt_boxes = json.loads(sample["ground_truth"])
+
+for qa in qa_pairs:
+    print(f"Q: {qa['question']}")
+    print(f"T: {qa['thinking']}")
+    print(f"A: {qa['answer']}")
+    print()
+```
+
+## License
+Apache 2.0