Add README with dataset documentation and sample GIFs

README.md

@@ -1,27 +1,199 @@
+# CameraBench Binary Evaluation Dataset
+
+A balanced VQA dataset for evaluating camera motion understanding in videos.
+
+## 📊 Dataset Statistics
+
+- **Total Questions**: 232
+- **Unique Videos**: 115
+- **Unique Questions**: 15
+- **Yes Answers**: 116 (50.0%)
+- **No Answers**: 116 (50.0%)
+- **Balance Ratio**: 1.00
+- **Total Size**: 121.22 MB (0.12 GB)
+- **Average Video Size**: 1.05 MB
+
+## 🎯 Task Categories
+
+This dataset covers various camera motion tasks including:
+- **Static**: 44 questions
+- **Move In**: 26 questions
+- **Pan Right**: 25 questions
+- **Roll Counterclockwise**: 19 questions
+- **Pan Left**: 17 questions
+- **Roll Clockwise**: 17 questions
+- **Move Right**: 16 questions
+- **Move Out**: 16 questions
+- **Tilt Down**: 15 questions
+- **Zoom In**: 14 questions
+- **Zoom Out**: 14 questions
+- **Move Up**: 14 questions
+- **Move Left**: 14 questions
+- **Tilt Up**: 12 questions
+- **Move Down**: 9 questions
+
+
+## 📝 Dataset Format
+
+Each record contains:
+- `video_name`: Original video filename
+- `video`: Video file (MP4 format, original quality)
+- `question`: Binary question about camera motion
+- `label`: Answer ("Yes" or "No")
+- `task`: Task category
+- `label_name`: Detailed label identifier
+
+## 🎬 Sample Questions and Videos
+
+Below are animated GIF previews of sample videos from the dataset:
+
+
+
+## 🚀 Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("cambench_binary_eval")
+
+# Access a sample
+sample = dataset['train'][0]
+print(f"Question: {sample['question']}")
+print(f"Answer: {sample['label']}")
+print(f"Task: {sample['task']}")
+
+# The video field contains the path to download
+video_file = sample['video']
+```
+
+### Downloading Videos
+
+Videos are embedded in the dataset. To download and use them:
+
+```python
+from datasets import load_dataset
+import os
+import shutil
+
+# Load the dataset
+dataset = load_dataset("cambench_binary_eval")
+
+# Download all videos to a local directory
+output_dir = "downloaded_videos"
+os.makedirs(output_dir, exist_ok=True)
+
+for idx, sample in enumerate(dataset['train']):
+    video_name = sample['video_name']
+    video_data = sample['video']  # This contains the video file data
+    
+    # Save video to local disk
+    local_path = os.path.join(output_dir, video_name)
+    
+    # If video_data is a file path (during local testing)
+    if isinstance(video_data, str) and os.path.exists(video_data):
+        shutil.copy2(video_data, local_path)
+    else:
+        # Video data from HuggingFace - write bytes to file
+        with open(local_path, 'wb') as f:
+            f.write(video_data)
+    
+    print(f"Downloaded: {video_name} -> {local_path}")
+
+print(f"All {len(dataset['train'])} videos downloaded to {output_dir}/")
+```
+
+### Accessing Individual Videos
+
+To download a specific video by name:
+
+```python
+from datasets import load_dataset
+import os
+
+dataset = load_dataset("cambench_binary_eval")
+
+# Find and download a specific video
+target_video = "your_video_name.mp4"
+for sample in dataset['train']:
+    if sample['video_name'] == target_video:
+        video_data = sample['video']
+        
+        # Save to current directory
+        with open(target_video, 'wb') as f:
+            f.write(video_data)
+        
+        print(f"Downloaded: {target_video}")
+        break
+else:
+    print(f"Video {target_video} not found in dataset")
+```
+
+### Batch Processing
+
+For evaluation tasks:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("cambench_binary_eval")
+
+correct = 0
+total = 0
+
+for sample in dataset['train']:
+    video_path = sample['video']
+    question = sample['question']
+    ground_truth = sample['label']
+    
+    # Your model inference here
+    # prediction = your_model(video_path, question)
+    
+    # if prediction == ground_truth:
+    #     correct += 1
+    # total += 1
+
+# accuracy = correct / total if total > 0 else 0
+# print(f"Accuracy: {accuracy:.2%}")
+```
+
+## 📥 Alternative: Download Full Dataset
+
+To download the entire dataset with all videos at once:
+
+```bash
+# Using huggingface-cli
+huggingface-cli download cambench_binary_eval --repo-type dataset --local-dir ./cambench_data
+
+# Or using Python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="cambench_binary_eval", repo_type="dataset", local_dir="./cambench_data")
+```
+
+This will download all videos and data files to your local machine.
+
+## 📊 Evaluation
+
+This dataset is designed for binary classification tasks. Evaluate your model using:
+- Accuracy
+- Precision/Recall
+- F1 Score
+- Per-task performance
+
+## 📄 License
+
+Please refer to the original CameraBench dataset for licensing information.
+
+## 🙏 Citation
+
+If you use this dataset, please cite the original CameraBench paper.
+
+## 📧 Contact
+
+For questions or issues, please open an issue on the repository.
+
 ---
-dataset_info:
-  features:
-  - name: video_name
-    dtype: string
-  - name: video
-    dtype: string
-  - name: question
-    dtype: string
-  - name: label
-    dtype: string
-  - name: task
-    dtype: string
-  - name: label_name
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 51960
-    num_examples: 272
-  download_size: 8816
-  dataset_size: 51960
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+
+**Note**: Videos are provided in MP4 format. Large videos (>50MB) are automatically compressed to ensure smooth downloading and processing. All videos maintain their original temporal dynamics for accurate camera motion evaluation.