Add README with dataset documentation

README.md

@@ -16,28 +16,32 @@ A balanced VQA dataset for evaluating camera motion understanding in videos.
 ## 🎯 Task Categories
 
 This dataset covers various camera motion tasks including:
-- **Static**: 37 questions
-- **Move In**: 26 questions
-- **Pan Left**: 21 questions
-- **Roll Clockwise**: 20 questions
-- **Pan Right**: 20 questions
-- **Roll Counterclockwise**: 19 questions
-- **Move Out**: 18 questions
-- **Move Left**: 17 questions
+- **Static**: 39 questions
+- **Move In**: 30 questions
+- **Pan Right**: 23 questions
+- **Pan Left**: 23 questions
+- **Move Out**: 20 questions
+- **Roll Counterclockwise**: 20 questions
+- **Roll Clockwise**: 19 questions
+- **Move Down**: 17 questions
 - **Tilt Up**: 16 questions
-- **Move Down**: 16 questions
-- **Tilt Down**: 15 questions
-- **Move Up**: 14 questions
+- **Move Up**: 16 questions
 - **Move Right**: 14 questions
-- **Zoom In**: 12 questions
-- **Zoom Out**: 10 questions
+- **Move Left**: 14 questions
+- **Tilt Down**: 13 questions
+- **Zoom In**: 13 questions
+- **Zoom Out**: 12 questions
 
 
 ## 📝 Dataset Format
 
-Each record contains:
+The dataset consists of:
+- `videos/`: Directory containing all MP4 video files
+- `metadata.jsonl`: JSONL file with question annotations
+
+Each record in `metadata.jsonl` contains:
 - `video_name`: Original video filename
-- `video`: Video file (MP4 format, original quality)
+- `video_path`: Relative path to video file (e.g., `videos/video.mp4`)
 - `question`: Binary question about camera motion
 - `label`: Answer ("Yes" or "No")
 - `task`: Task category
@@ -54,81 +58,60 @@ Below are animated GIF previews of sample videos from the dataset:
 ### Loading the Dataset
 
 ```python
-from datasets import load_dataset
+import json
+import os
 
-# Load the dataset
-dataset = load_dataset("cambench_binary_eval")
+# Load metadata
+metadata = []
+with open("metadata.jsonl", "r") as f:
+    for line in f:
+        metadata.append(json.loads(line))
 
 # Access a sample
-sample = dataset['train'][0]
+sample = metadata[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']
+print(f"Video path: {sample['video_path']}")
 ```
 
-### 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")
+### Downloading the Dataset
 
-# Download all videos to a local directory
-output_dir = "downloaded_videos"
-os.makedirs(output_dir, exist_ok=True)
+Download the entire dataset using huggingface-cli or git:
 
-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}")
+```bash
+# Using huggingface-cli
+huggingface-cli download tuhink/cambench_binary_eval --repo-type dataset --local-dir ./cambench_data
 
-print(f"All {len(dataset['train'])} videos downloaded to {output_dir}/")
+# Or using git
+git clone https://huggingface.co/datasets/tuhink/cambench_binary_eval
 ```
 
-### Accessing Individual Videos
+This will download all videos and metadata to your local machine.
 
-To download a specific video by name:
+### Loading Videos
 
 ```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}")
+import json
+import cv2
+
+# Load metadata
+with open("metadata.jsonl", "r") as f:
+    metadata = [json.loads(line) for line in f]
+
+# Load a video
+sample = metadata[0]
+video_path = sample['video_path']  # e.g., "videos/video_name.mp4"
+
+# Use OpenCV to read the video
+cap = cv2.VideoCapture(video_path)
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
         break
-else:
-    print(f"Video {target_video} not found in dataset")
+    # Process frame
+    pass
+cap.release()
 ```
 
 ### Batch Processing
@@ -136,15 +119,17 @@ else:
 For evaluation tasks:
 
 ```python
-from datasets import load_dataset
+import json
 
-dataset = load_dataset("cambench_binary_eval")
+# Load all questions
+with open("metadata.jsonl", "r") as f:
+    dataset = [json.loads(line) for line in f]
 
 correct = 0
 total = 0
 
-for sample in dataset['train']:
-    video_path = sample['video']
+for sample in dataset:
+    video_path = sample['video_path']
     question = sample['question']
     ground_truth = sample['label']
     
@@ -159,21 +144,21 @@ for sample in dataset['train']:
 # print(f"Accuracy: {accuracy:.2%}")
 ```
 
-## 📥 Alternative: Download Full Dataset
+### Using with HuggingFace Datasets Library
 
-To download the entire dataset with all videos at once:
+```python
+from datasets import load_dataset
 
-```bash
-# Using huggingface-cli
-huggingface-cli download cambench_binary_eval --repo-type dataset --local-dir ./cambench_data
+# Load the dataset
+dataset = load_dataset("tuhink/cambench_binary_eval")
 
-# Or using Python
-from huggingface_hub import snapshot_download
-snapshot_download(repo_id="cambench_binary_eval", repo_type="dataset", local_dir="./cambench_data")
+# Access samples
+for sample in dataset['train']:
+    print(f"Question: {sample['question']}")
+    print(f"Answer: {sample['label']}")
+    print(f"Video: {sample['video_path']}")
 ```
 
-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:
@@ -196,4 +181,4 @@ For questions or issues, please open an issue on the repository.
 
 ---
 
-**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.
+**Note**: All videos are provided in original MP4 format. The dataset maintains temporal dynamics for accurate camera motion evaluation.