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--- |
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license: mit |
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tags: |
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- yolo |
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- object-detection |
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- pose-estimation |
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- volleyball |
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- sports |
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- computer-vision |
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- pytorch |
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datasets: |
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- volleyball-court-keypoints |
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- volleyball-detection |
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language: |
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- en |
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pipeline_tag: object-detection |
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--- |
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# VOLLEY-REF AI Models |
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AI-powered volleyball referee system for automatic IN/OUT line call detection. |
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## Models Included |
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### 1. Court Keypoints Model (`yolo_court_keypoints.pt`) |
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- **Architecture**: YOLOv11n-pose |
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- **Task**: Detect 14 keypoints of a volleyball court |
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- **Training**: 100 epochs on volleyball-court-keypoints dataset |
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- **Performance**: 99% box mAP@50, 29% pose mAP@50 |
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### 2. Ball Detection Model (`yolo_volleyball_ball.pt`) |
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- **Architecture**: YOLOv11s |
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- **Task**: Detect volleyball in video frames |
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- **Training**: 57 epochs on volleyball_detection dataset |
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- **Performance**: 98.8% mAP@50 |
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## Usage |
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### Download Models |
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```python |
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from huggingface_hub import hf_hub_download |
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# Download court model |
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court_model = hf_hub_download( |
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repo_id="David-dsv/volley-ref-ai", |
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filename="yolo_court_keypoints.pt" |
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) |
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# Download ball model |
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ball_model = hf_hub_download( |
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repo_id="David-dsv/volley-ref-ai", |
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filename="yolo_volleyball_ball.pt" |
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) |
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``` |
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### Inference with Ultralytics |
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```python |
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from ultralytics import YOLO |
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# Court keypoints detection |
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court_model = YOLO("yolo_court_keypoints.pt") |
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results = court_model("volleyball_frame.jpg") |
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# Ball detection |
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ball_model = YOLO("yolo_volleyball_ball.pt") |
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results = ball_model("volleyball_frame.jpg", conf=0.7) |
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``` |
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### Full Pipeline |
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See the [GitHub repository](https://github.com/David-dsv/volley-ref-ai) for the complete VOLLEY-REF AI pipeline that combines both models for automatic IN/OUT detection. |
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## Training Details |
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### Court Model |
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- Base: `yolo11n-pose.pt` |
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- Dataset: volleyball-court-keypoints (495 images) |
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- Epochs: 100 |
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- Image size: 640 |
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- Augmentation: Default YOLO augmentations |
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### Ball Model |
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- Base: `yolo11s.pt` |
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- Dataset: volleyball_detection (1091 images) |
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- Epochs: 57 (early stopped from 150) |
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- Image size: 640 |
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- Augmentation: Default YOLO augmentations |
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## Limitations |
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- Trained primarily on indoor volleyball footage |
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- Performance may vary with different camera angles |
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- Ball detection works best with clear visibility (no motion blur) |
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- Court detection requires visible court lines |
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## License |
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MIT License |
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## Citation |
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```bibtex |
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@software{volley_ref_ai_2025, |
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author = {Vuong}, |
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title = {VOLLEY-REF AI: AI-Powered Volleyball Referee System}, |
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year = {2025}, |
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url = {https://github.com/David-dsv/volley-ref-ai} |
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} |
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``` |
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## Acknowledgments |
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- [Ultralytics](https://github.com/ultralytics/ultralytics) for YOLOv11 |
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- [Roboflow](https://roboflow.com/) for the training datasets |
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