Update README.md

README.md

@@ -11,22 +11,37 @@ tags:
   - computer-vision
   - pytorch
   - ultralytics
+  - courtside
 datasets:
-  - custom
+  - dataset1-yx5qr
 metrics:
   - precision
   - recall
   - mAP
 library_name: ultralytics
 pipeline_tag: object-detection
+model-index:
+- name: CourtSide Computer Vision v0.2
+  results:
+  - task:
+      type: object-detection
+    metrics:
+    - type: mAP@50
+      value: 66.67
+    - type: precision
+      value: 71
+    - type: recall
+      value: 44
 ---
 
-# YOLOv11 Tennis Racket Detection 🎾
+# CourtSide Computer Vision v0.2 - Racket Detection 🎾
 
-Fine-tuned YOLOv11n model for detecting tennis rackets in images and videos.
+Fine-tuned YOLOv11n model for detecting tennis rackets in images and videos. Part of the CourtSide Computer Vision suite for comprehensive tennis match analysis.
 
 ## Model Details
 
+- **Model Name**: CourtSide Computer Vision v0.2
+- **Model ID**: `Davidsv/CourtSide-Computer-Vision-v0.2`
 - **Model Type**: Object Detection
 - **Architecture**: YOLOv11 Nano (n)
 - **Framework**: Ultralytics YOLOv11
@@ -42,18 +57,22 @@ Evaluated on validation set (66 images):
 |--------|-------|
 | **mAP@50** | **66.67%** |
 | **mAP@50-95** | 33.33% |
-| **Precision** | ~71% (estimated) |
-| **Recall** | ~44% (estimated) |
+| **Precision** | ~71% |
+| **Recall** | ~44% |
 | **Inference Speed** (M4 Pro) | ~10ms |
 
 ## Training Details
 
 ### Dataset
+
+This model was trained on the **dataset1** by Tesi, available on Roboflow Universe.
+
 - **Training images**: 582
 - **Validation images**: 66
 - **Test images**: 55
 - **Total**: 703 annotated images
 - **Annotation format**: YOLO format (bounding boxes)
+- **Source**: [Roboflow Universe - Dataset1](https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr)
 
 ### Training Configuration
 ```yaml
@@ -82,19 +101,16 @@ Training time: ~26 minutes
 ## Usage
 
 ### Installation
-
 ```bash
 pip install ultralytics
 ```
 
 ### Python API
-
 ```python
 from ultralytics import YOLO
-from PIL import Image
 
-# Load model
-model = YOLO('path/to/raquette_subset_best.pt')
+# Load CourtSide Computer Vision v0.2 model
+model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2')
 
 # Predict on image
 results = model.predict('tennis_match.jpg', conf=0.4)
@@ -110,11 +126,10 @@ for box in results[0].boxes:
 ```
 
 ### Video Processing
-
 ```python
 from ultralytics import YOLO
 
-model = YOLO('path/to/raquette_subset_best.pt')
+model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2')
 
 # Process video
 results = model.predict(
@@ -133,25 +148,23 @@ results = model.track(
 ```
 
 ### Command Line
-
 ```bash
 # Predict on image
-yolo detect predict model=raquette_subset_best.pt source=image.jpg conf=0.4
+yolo detect predict model=Davidsv/CourtSide-Computer-Vision-v0.2 source=image.jpg conf=0.4
 
 # Predict on video
-yolo detect predict model=raquette_subset_best.pt source=video.mp4 conf=0.4 save=True
+yolo detect predict model=Davidsv/CourtSide-Computer-Vision-v0.2 source=video.mp4 conf=0.4 save=True
 
 # Track rackets in video
-yolo detect track model=raquette_subset_best.pt source=video.mp4 conf=0.4
+yolo detect track model=Davidsv/CourtSide-Computer-Vision-v0.2 source=video.mp4 conf=0.4
 
 # Validate model
-yolo detect val model=raquette_subset_best.pt data=dataset.yaml
+yolo detect val model=Davidsv/CourtSide-Computer-Vision-v0.2 data=dataset.yaml
 ```
 
 ## Recommended Hyperparameters
 
 ### Inference Settings
-
 ```python
 # Balanced (recommended)
 conf_threshold = 0.40  # Confidence threshold
@@ -187,12 +200,13 @@ max_det = 15
 ## Use Cases
 
 ✅ **Recommended:**
-- Tennis match analysis
+- Tennis match analysis and statistics
 - Player technique analysis
 - Swing detection and tracking
 - Automated coaching feedback
-- Sports analytics and statistics
+- Sports analytics dashboards
 - Training video analysis
+- Action recognition pipelines (combined with ball detection)
 
 ⚠️ **Not Recommended:**
 - Real-time officiating decisions
@@ -203,8 +217,9 @@ max_det = 15
 
 ### Sample Detections
 
-**mAP@50: 66.67%** - Good detection performance on typical tennis scenes
-**Precision: ~71%** - When detected, about 7 out of 10 detections are correct
+**mAP@50: 66.67%** - Good detection performance on typical tennis scenes  
+**Precision: ~71%** - When detected, about 7 out of 10 detections are correct  
+**Recall: ~44%** - Detects approximately 4-5 out of 10 rackets
 
 ### Confidence Interpretation
 
@@ -215,35 +230,40 @@ max_det = 15
 | 0.4 - 0.5 | Low confidence - possible tennis racket |
 | < 0.4 | Very low confidence - likely false positive |
 
-## Integration with Tennis Ball Detection
+## CourtSide Computer Vision Suite
+
+This model is part of the **CourtSide Computer Vision** project for comprehensive tennis analysis:
 
-This model works well in combination with the Tennis Ball Detection model:
+### Available Models
 
+- **v0.1** - Tennis Ball Detection ([Davidsv/CourtSide-Computer-Vision-v0.1](https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v0.1))
+- **v0.2** - Tennis Racket Detection (this model)
+
+### Combined Usage Example
 ```python
 from ultralytics import YOLO
 
-# Load both models
-model_racket = YOLO('raquette_subset_best.pt')
-model_ball = YOLO('tennis_ball_subset_best.pt')
+# Load both CourtSide CV models
+model_ball = YOLO('Davidsv/CourtSide-Computer-Vision-v0.1')      # Ball detection
+model_racket = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2')    # Racket detection
 
 # Detect both in same image
-racket_results = model_racket.predict('match.jpg', conf=0.4)
 ball_results = model_ball.predict('match.jpg', conf=0.3)
+racket_results = model_racket.predict('match.jpg', conf=0.4)
 
-# Combine detections for analysis
-print(f"Rackets: {len(racket_results[0].boxes)}")
-print(f"Balls: {len(ball_results[0].boxes)}")
+# Combine detections for comprehensive analysis
+print(f"Balls detected: {len(ball_results[0].boxes)}")
+print(f"Rackets detected: {len(racket_results[0].boxes)}")
 ```
 
 ## Advanced Usage
 
-### Detect Player Actions
-
+### Detect and Track Swing Actions
 ```python
 from ultralytics import YOLO
 import cv2
 
-model = YOLO('raquette_subset_best.pt')
+model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2')
 video = cv2.VideoCapture('match.mp4')
 
 frame_count = 0
@@ -257,7 +277,7 @@ while True:
     # Detect rackets
     results = model.predict(frame, conf=0.4, verbose=False)
 
-    # Track racket movement
+    # Track racket movement for swing analysis
     for box in results[0].boxes:
         x1, y1, x2, y2 = box.xyxy[0]
         center_x = (x1 + x2) / 2
@@ -270,24 +290,59 @@ while True:
 print(f"Total racket detections: {len(racket_positions)}")
 ```
 
+### Full Tennis Analysis Pipeline
+```python
+from ultralytics import YOLO
+
+# Load all CourtSide models
+ball_model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.1')
+racket_model = YOLO('Davidsv/CourtSide-Computer-Vision-v0.2')
+
+# Process video with both models
+ball_results = ball_model.track('match.mp4', conf=0.3)
+racket_results = racket_model.track('match.mp4', conf=0.4)
+
+# Combine for action recognition and analytics
+```
+
 ## Model Card Authors
 
-- **Developed by**: Vuong
+- **Developed by**: Davidsv (Vuong)
 - **Model date**: November 2024
-- **Model version**: 1.0
+- **Model version**: v0.2
 - **Model type**: Object Detection (YOLOv11)
+- **Part of**: CourtSide Computer Vision Suite
 
-## Citation
+## Citations
 
-If you use this model, please cite:
+### This Model
 
+If you use this model, please cite:
 ```bibtex
-@misc{yolov11_tennis_racket_2024,
-  title={YOLOv11 Tennis Racket Detection},
+@misc{courtsidecv_v0.2_2024,
+  title={CourtSide Computer Vision v0.2: Tennis Racket Detection with YOLOv11},
   author={Vuong},
   year={2024},
   publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/...}}
+  howpublished={\url{https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v0.2}}
+}
+```
+
+### Dataset
+
+This model was trained using the dataset1 dataset. Please cite:
+```bibtex
+@misc{dataset1-yx5qr_dataset,
+  title = {dataset1 Dataset},
+  type = {Open Source Dataset},
+  author = {Tesi},
+  howpublished = {\url{https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr}},
+  url = {https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr},
+  journal = {Roboflow Universe},
+  publisher = {Roboflow},
+  year = {2023},
+  month = {mar},
+  note = {visited on 2024-11-20}
 }
 ```
 
@@ -298,45 +353,47 @@ MIT License - Free for commercial and academic use.
 ## Acknowledgments
 
 - Built with [Ultralytics YOLOv11](https://github.com/ultralytics/ultralytics)
-- Trained on custom annotated tennis dataset
-- Part of the Tennis Analysis project
+- Dataset by Tesi via [Roboflow Universe](https://universe.roboflow.com/tesi-mpvmr/dataset1-yx5qr)
+- Part of the CourtSide Computer Vision project for tennis analysis
 
 ## Contact & Support
 
 For questions, issues, or collaboration:
-- GitHub Issues: [tennis_analysis/issues](https://github.com/...)
-- Model Updates: Check for newer versions on Hugging Face
-
-## Related Models
-
-- [YOLOv11 Tennis Ball Detection](https://huggingface.co/...) - Companion model for ball detection
+- Hugging Face: [@Davidsv](https://huggingface.co/Davidsv)
+- Model Updates: Check for newer versions in the CourtSide CV series
 
 ## Common Issues & Solutions
 
-### Issue: Low Recall
+### Issue: Low Recall (Missing Rackets)
 **Solution**: Lower confidence threshold to 0.30-0.35
 
 ### Issue: Too Many False Positives
 **Solution**: Increase confidence threshold to 0.50-0.55
 
-### Issue: Missed Rackets in Motion
-**Solution**: Use model tracking (`model.track()`) instead of simple prediction
+### Issue: Missed Rackets in Fast Motion
+**Solution**: Use `model.track()` instead of `model.predict()` for better temporal consistency
 
 ### Issue: Multiple Detections per Racket
 **Solution**: Increase NMS IoU threshold to 0.50-0.55
 
+### Issue: Poor Performance on Unusual Angles
+**Solution**: Consider fine-tuning on your specific camera setup or use data augmentation
+
 ## Model Changelog
 
-### v1.0 (2024-11-20)
-- Initial release
+### v0.2 (2024-11-20)
+- Initial release of racket detection model
 - YOLOv11n architecture
 - mAP@50: 66.67%
-- 703 training images
+- 703 training images from Roboflow dataset
+- Optimized for standard tennis racket detection
+- Part of CourtSide Computer Vision suite
 
 ---
 
-**Model Size**: 5.4 MB
-**Inference Speed**: 10-65ms (device dependent)
-**Supported Formats**: PyTorch (.pt), ONNX, TensorRT, CoreML
+**Model Size**: 5.4 MB  
+**Inference Speed**: 10-65ms (device dependent)  
+**Supported Formats**: PyTorch (.pt), ONNX, TensorRT, CoreML  
+**Model Hub**: [Davidsv/CourtSide-Computer-Vision-v0.2](https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v0.2)
 
 🎾 Ready for production use in tennis analysis applications!