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--- |
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language: en |
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license: mit |
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tags: |
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- yolo |
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- yolov11 |
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- object-detection |
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- tennis |
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- racket |
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- sports |
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- computer-vision |
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- pytorch |
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- ultralytics |
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- courtside |
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datasets: |
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- dataset1-yx5qr |
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metrics: |
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- precision |
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- recall |
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- mAP |
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library_name: ultralytics |
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pipeline_tag: object-detection |
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model-index: |
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- name: CourtSide Computer Vision v0.2 |
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results: |
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- task: |
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type: object-detection |
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metrics: |
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- type: mAP@50 |
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value: 66.67 |
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- type: precision |
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value: 71 |
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- type: recall |
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value: 44 |
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--- |
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# CourtSide Computer Vision v0.2 - Racket Detection 🎾 |
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Fine-tuned YOLOv11n model for detecting tennis rackets in images and videos. Part of the CourtSide Computer Vision suite for comprehensive tennis match analysis. |
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## Model Details |
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- **Model Name**: CourtSide Computer Vision v0.2 |
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- **Model ID**: `Davidsv/CourtSide-Computer-Vision-v0.2` |
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- **Model Type**: Object Detection |
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- **Architecture**: YOLOv11 Nano (n) |
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- **Framework**: Ultralytics YOLOv11 |
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- **Parameters**: 2.6M |
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- **Input Size**: 640x640 |
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- **Classes**: 1 (`racket`) |
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## Performance Metrics |
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Evaluated on validation set (66 images): |
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| Metric | Value | |
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|--------|-------| |
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| **mAP@50** | **66.67%** | |
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| **mAP@50-95** | 33.33% | |
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| **Precision** | ~71% | |
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| **Recall** | ~44% | |
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| **Inference Speed** (M4 Pro) | ~10ms | |
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## Training Details |
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### Dataset |
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This model was trained on the **dataset1** by Tesi, available on Roboflow Universe. |
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- **Training images**: 582 |
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- **Validation images**: 66 |
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- **Test images**: 55 |
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- **Total**: 703 annotated images |
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- **Annotation format**: YOLO format (bounding boxes) |
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- **Source**: [Roboflow Universe - Dataset1](https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr) |
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### Training Configuration |
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```yaml |
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Model: YOLOv11n (nano) |
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Epochs: 100 |
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Batch size: 16 |
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Image size: 640x640 |
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Device: Apple M4 Pro (MPS) |
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Optimizer: AdamW |
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Learning rate: 0.001 → 0.01 |
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Training time: ~26 minutes |
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``` |
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### Augmentation |
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- HSV color jitter (h=0.015, s=0.7, v=0.4) |
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- Random horizontal flip (p=0.5) |
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- Translation (±10%) |
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- Scaling (±50%) |
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- Mosaic augmentation |
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### Loss Weights |
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- Box loss: 7.5 |
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- Class loss: 0.5 |
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- DFL loss: 1.5 |
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## Usage |
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### Installation |
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```bash |
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pip install ultralytics |
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``` |
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### Python API |
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```python |
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from ultralytics import YOLO |
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# Load CourtSide Computer Vision v0.2 model |
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model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2') |
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# Predict on image |
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results = model.predict('tennis_match.jpg', conf=0.4) |
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# Display results |
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results[0].show() |
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# Get bounding boxes |
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for box in results[0].boxes: |
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x1, y1, x2, y2 = box.xyxy[0] |
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confidence = box.conf[0] |
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print(f"Racket detected at [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] with {confidence:.2%} confidence") |
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``` |
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### Video Processing |
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```python |
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from ultralytics import YOLO |
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model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2') |
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# Process video |
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results = model.predict( |
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source='tennis_match.mp4', |
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conf=0.4, |
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save=True, |
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save_txt=True |
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) |
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# Track rackets across frames |
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results = model.track( |
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source='tennis_match.mp4', |
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conf=0.4, |
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tracker='bytetrack.yaml' |
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) |
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``` |
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### Command Line |
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```bash |
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# Predict on image |
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yolo detect predict model=Davidsv/CourtSide-Computer-Vision-v0.2 source=image.jpg conf=0.4 |
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# Predict on video |
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yolo detect predict model=Davidsv/CourtSide-Computer-Vision-v0.2 source=video.mp4 conf=0.4 save=True |
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# Track rackets in video |
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yolo detect track model=Davidsv/CourtSide-Computer-Vision-v0.2 source=video.mp4 conf=0.4 |
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# Validate model |
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yolo detect val model=Davidsv/CourtSide-Computer-Vision-v0.2 data=dataset.yaml |
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``` |
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## Recommended Hyperparameters |
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### Inference Settings |
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```python |
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# Balanced (recommended) |
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conf_threshold = 0.40 # Confidence threshold |
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iou_threshold = 0.45 # NMS IoU threshold |
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max_det = 10 # Maximum detections per image (usually 2-4 rackets) |
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# High precision (fewer false positives) |
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conf_threshold = 0.55 |
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iou_threshold = 0.45 |
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max_det = 8 |
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# High recall (detect more rackets, more false positives) |
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conf_threshold = 0.30 |
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iou_threshold = 0.40 |
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max_det = 15 |
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``` |
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## Limitations |
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- **Motion blur**: Rackets in very fast motion may be harder to detect |
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- **Occlusion**: Partially hidden rackets (behind player, net, etc.) may not be detected |
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- **Angles**: Extreme viewing angles may reduce detection accuracy |
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- **Racket types**: Trained on standard tennis rackets, may not generalize to unusual designs |
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- **Similar objects**: May occasionally detect similar elongated objects |
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## Model Biases |
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- Trained on professional and amateur match footage |
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- Better performance on standard racket designs and colors |
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- Dataset may have court-type or player-level biases |
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- Optimized for typical tennis camera angles |
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## Use Cases |
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✅ **Recommended:** |
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- Tennis match analysis and statistics |
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- Player technique analysis |
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- Swing detection and tracking |
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- Automated coaching feedback |
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- Sports analytics dashboards |
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- Training video analysis |
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- Action recognition pipelines (combined with ball detection) |
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⚠️ **Not Recommended:** |
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- Real-time officiating decisions |
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- Safety-critical applications |
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- Detection of non-tennis rackets without fine-tuning |
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## Example Results |
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### Sample Detections |
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**mAP@50: 66.67%** - Good detection performance on typical tennis scenes |
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**Precision: ~71%** - When detected, about 7 out of 10 detections are correct |
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**Recall: ~44%** - Detects approximately 4-5 out of 10 rackets |
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### Confidence Interpretation |
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| Confidence Range | Interpretation | |
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|------------------|----------------| |
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| > 0.7 | High confidence - very likely a tennis racket | |
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| 0.5 - 0.7 | Medium confidence - probably a tennis racket | |
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| 0.4 - 0.5 | Low confidence - possible tennis racket | |
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| < 0.4 | Very low confidence - likely false positive | |
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## CourtSide Computer Vision Suite |
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This model is part of the **CourtSide Computer Vision** project for comprehensive tennis analysis: |
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### Available Models |
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- **v0.1** - Tennis Ball Detection ([Davidsv/CourtSide-Computer-Vision-v0.1](https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v0.1)) |
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- **v0.2** - Tennis Racket Detection (this model) |
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### Combined Usage Example |
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```python |
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from ultralytics import YOLO |
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# Load both CourtSide CV models |
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model_ball = YOLO('Davidsv/CourtSide-Computer-Vision-v0.1') # Ball detection |
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model_racket = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2') # Racket detection |
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# Detect both in same image |
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ball_results = model_ball.predict('match.jpg', conf=0.3) |
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racket_results = model_racket.predict('match.jpg', conf=0.4) |
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# Combine detections for comprehensive analysis |
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print(f"Balls detected: {len(ball_results[0].boxes)}") |
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print(f"Rackets detected: {len(racket_results[0].boxes)}") |
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``` |
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## Advanced Usage |
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### Detect and Track Swing Actions |
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```python |
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from ultralytics import YOLO |
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import cv2 |
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model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2') |
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video = cv2.VideoCapture('match.mp4') |
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frame_count = 0 |
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racket_positions = [] |
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while True: |
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ret, frame = video.read() |
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if not ret: |
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break |
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# Detect rackets |
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results = model.predict(frame, conf=0.4, verbose=False) |
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# Track racket movement for swing analysis |
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for box in results[0].boxes: |
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x1, y1, x2, y2 = box.xyxy[0] |
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center_x = (x1 + x2) / 2 |
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center_y = (y1 + y2) / 2 |
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racket_positions.append((frame_count, center_x, center_y)) |
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frame_count += 1 |
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# Analyze swing patterns |
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print(f"Total racket detections: {len(racket_positions)}") |
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``` |
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### Full Tennis Analysis Pipeline |
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```python |
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from ultralytics import YOLO |
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# Load all CourtSide models |
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ball_model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.1') |
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racket_model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2') |
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# Process video with both models |
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ball_results = ball_model.track('match.mp4', conf=0.3) |
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racket_results = racket_model.track('match.mp4', conf=0.4) |
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# Combine for action recognition and analytics |
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``` |
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## Model Card Authors |
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- **Developed by**: Davidsv (Vuong) |
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- **Model date**: November 2024 |
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- **Model version**: v0.2 |
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- **Model type**: Object Detection (YOLOv11) |
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- **Part of**: CourtSide Computer Vision Suite |
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## Citations |
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### This Model |
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If you use this model, please cite: |
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```bibtex |
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@misc{courtsidecv_v0.2_2024, |
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title={CourtSide Computer Vision v0.2: Tennis Racket Detection with YOLOv11}, |
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author={Vuong}, |
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year={2024}, |
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publisher={Hugging Face}, |
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howpublished={\url{https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v0.2}} |
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} |
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``` |
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### Dataset |
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This model was trained using the dataset1 dataset. Please cite: |
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```bibtex |
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@misc{dataset1-yx5qr_dataset, |
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title = {dataset1 Dataset}, |
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type = {Open Source Dataset}, |
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author = {Tesi}, |
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howpublished = {\url{https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr}}, |
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url = {https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr}, |
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journal = {Roboflow Universe}, |
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publisher = {Roboflow}, |
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year = {2023}, |
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month = {mar}, |
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note = {visited on 2024-11-20} |
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} |
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``` |
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## License |
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MIT License - Free for commercial and academic use. |
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## Acknowledgments |
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- Built with [Ultralytics YOLOv11](https://github.com/ultralytics/ultralytics) |
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- Dataset by Tesi via [Roboflow Universe](https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr) |
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- Part of the CourtSide Computer Vision project for tennis analysis |
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## Contact & Support |
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For questions, issues, or collaboration: |
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- Hugging Face: [@Davidsv](https://huggingface.co/Davidsv) |
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- Model Updates: Check for newer versions in the CourtSide CV series |
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## Common Issues & Solutions |
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### Issue: Low Recall (Missing Rackets) |
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**Solution**: Lower confidence threshold to 0.30-0.35 |
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### Issue: Too Many False Positives |
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**Solution**: Increase confidence threshold to 0.50-0.55 |
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### Issue: Missed Rackets in Fast Motion |
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**Solution**: Use `model.track()` instead of `model.predict()` for better temporal consistency |
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### Issue: Multiple Detections per Racket |
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**Solution**: Increase NMS IoU threshold to 0.50-0.55 |
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### Issue: Poor Performance on Unusual Angles |
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**Solution**: Consider fine-tuning on your specific camera setup or use data augmentation |
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## Model Changelog |
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### v0.2 (2024-11-20) |
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- Initial release of racket detection model |
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- YOLOv11n architecture |
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- mAP@50: 66.67% |
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- 703 training images from Roboflow dataset |
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- Optimized for standard tennis racket detection |
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- Part of CourtSide Computer Vision suite |
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--- |
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**Model Size**: 5.4 MB |
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**Inference Speed**: 10-65ms (device dependent) |
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**Supported Formats**: PyTorch (.pt), ONNX, TensorRT, CoreML |
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**Model Hub**: [Davidsv/CourtSide-Computer-Vision-v0.2](https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v0.2) |
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🎾 Ready for production use in tennis analysis applications! |
