Resolve README merge conflict

README.md

@@ -16,24 +16,38 @@ language:
 - en
 size_categories:
 - n<1K
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 version: 0.0.1
+=======
+version: 1.2.0
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 ---
 
 # BJJ Positions & Submissions Dataset
 
 ## Dataset Description
 
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 This dataset contains pose keypoint annotations **and compressed video clips** for Brazilian Jiu-Jitsu (BJJ) combat positions and submissions. It includes 2D keypoint coordinates for up to 2 athletes per image, labeled with specific BJJ positions and submission attempts, as well as short video segments for each position/submission. The videos are optimized for use in video transformer models such as ViViT.
+=======
+This dataset contains pose keypoint annotations for Brazilian Jiu-Jitsu (BJJ) combat positions and submissions. It includes 2D keypoint coordinates for up to 2 athletes per image, labeled with specific BJJ positions and submission attempts.
+>>>>>>> 52c1f32e9ebdc8666e3da854c10f47aa5996ae90
 
 ### Dataset Summary
 
 - **Total samples**: 1
 - **Position classes**: 1 unique BJJ positions
 - **Keypoint format**: MS-COCO (17 keypoints per person)
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 - **Video format**: MP4, H.264, 360p/480p, 15 FPS, compressed for ML
 - **Data format**: [x, y, confidence] for each keypoint, plus associated video
 - **Last updated**: 2025-07-21
 - **Version**: 0.0.1
+=======
+- **Data format**: [x, y, confidence] for each keypoint
+- **Last updated**: 2025-07-21
+- **Version**: 1.2.0
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 ### Supported Tasks
 
@@ -41,7 +55,10 @@ This dataset contains pose keypoint annotations **and compressed video clips** f
 - Submission detection
 - Multi-person pose estimation
 - Combat sports analysis
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 - **Video action recognition (ViViT, etc.)**
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 - Action recognition in grappling
 
 ## Recent Updates
@@ -71,7 +88,10 @@ This dataset contains pose keypoint annotations **and compressed video clips** f
 - `num_people`: Number of people detected (1 or 2)
 - `total_keypoints`: Total visible keypoints across both athletes
 - `date_added`: Date when sample was added to dataset
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 - **`video_path`**: Relative path to the associated compressed video clip (MP4, suitable for ViViT and other video models)
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 ### Keypoint Format
 
@@ -83,11 +103,14 @@ Uses MS-COCO 17-keypoint format:
 
 Each keypoint: [x, y, confidence] where confidence 0.0-1.0
 
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 ### Video Format
 
 - **Format**: MP4 (H.264), 360p or 480p, 15 FPS, compressed for efficient ML training
 - **Usage**: Each sample links to a short video clip showing the position/submission, suitable for direct use in video transformer models (e.g., ViViT)
 
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 ## Usage
 
 ```python
@@ -101,6 +124,7 @@ sample = dataset['train'][0]
 print(f"Position: {sample['position']}")
 print(f"Number of people: {sample['num_people']}")
 print(f"Athlete 1 keypoints: {len(sample['pose1_keypoints'])}")
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 print(f"Video path: {sample['video_path']}")
 
 # Example: Load video for ViViT preprocessing
@@ -114,6 +138,8 @@ while True:
     frames.append(frame)
 cap.release()
 print(f"Loaded {len(frames)} frames for ViViT input.")
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 # Filter by specific positions
 guard_samples = dataset['train'].filter(lambda x: 'guard' in x['position'])
@@ -122,14 +148,22 @@ print(f"Guard positions: {len(guard_samples)} samples")
 
 ## Data Collection Progress
 
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 The dataset is continuously updated with new BJJ position and submission samples, including both pose annotations and video clips. Each position is being captured from multiple angles and with different athletes to improve model generalization and support robust video-based learning.
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+The dataset is continuously updated with new BJJ position and submission samples. Each position is being captured from multiple angles and with different athletes to improve model generalization.
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 ### Collection Goals
 
 - **Target**: 50+ samples per position (900+ total)
 - **Current**: 1 total samples
 - **Coverage**: 1/18+ positions represented
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 - **Focus**: High-quality pose annotations and video clips for training robust BJJ classifiers and video models (ViViT, etc.)
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+- **Focus**: High-quality pose annotations for training robust BJJ classifiers
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 ## Applications
 
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 - **Training Feedback**: Provide real-time feedback on position quality
 - **Competition Analysis**: Automatically score and analyze BJJ matches
 - **Educational Tools**: Interactive learning applications for BJJ students
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 - **Video Action Recognition**: Train ViViT and other video transformer models for grappling action recognition
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 ## Citation
 
@@ -151,7 +188,11 @@ If you use this dataset in your research, please cite:
   title={BJJ Positions and Submissions Dataset},
   author={Carlos J},
   year={2025},
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   version={0.0.1},
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+  version={1.2.0},
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   publisher={Hugging Face},
   url={https://huggingface.co/datasets/carlosj934/BJJ_Positions_Submissions}
 }