Update README.md

README.md

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-# YOLOv11 Tennis Detection Model
+---
+language: en
+license: mit
+tags:
+  - yolo
+  - yolov11
+  - object-detection
+  - tennis
+  - racket
+  - tennis-ball
+  - court-detection
+  - sports
+  - computer-vision
+  - pytorch
+  - ultralytics
+  - courtside
+datasets:
+  - roboflow/tennis-ball-detection
+  - roboflow/tennis-court-detection
+  - roboflow/racket-detection
+metrics:
+  - precision
+  - recall
+  - mAP
+library_name: ultralytics
+pipeline_tag: object-detection
+model-index:
+- name: CourtSide Computer Vision v1
+  results:
+  - task:
+      type: object-detection
+    metrics:
+    - type: mAP@50
+      value: 92.13
+    - type: mAP@50-95
+      value: 85.49
+    - type: precision
+      value: 92.9
+    - type: recall
+      value: 92.0
+---
 
-Fine-tuned YOLOv11 model for comprehensive tennis analysis with multi-class object detection.
+# CourtSide Computer Vision v1 - Complete Tennis Detection
 
-## Model Performance
+Fine-tuned YOLOv11n model for comprehensive tennis analysis with **10-class detection**: rackets, balls, and court zones. The most complete model in the CourtSide Computer Vision suite.
 
-| Metric | Score |
+## Model Details
+
+- **Model Name**: CourtSide Computer Vision v1
+- **Model ID**: `Davidsv/CourtSide-Computer-Vision-v1`
+- **Model Type**: Object Detection
+- **Architecture**: YOLOv11 Nano (n)
+- **Framework**: Ultralytics YOLOv11
+- **Parameters**: 2.6M
+- **Input Size**: 640x640
+- **Classes**: 10
+
+## Classes Detected
+
+| ID | Class | Description |
+|----|-------|-------------|
+| 0 | `racket` | Tennis rackets |
+| 1 | `tennis_ball` | Tennis balls |
+| 2 | `bottom-dead-zone` | Bottom baseline area |
+| 3 | `court` | Full court area |
+| 4 | `left-doubles-alley` | Left doubles alley |
+| 5 | `left-service-box` | Left service box |
+| 6 | `net` | Tennis net |
+| 7 | `right-doubles-alley` | Right doubles alley |
+| 8 | `right-service-box` | Right service box |
+| 9 | `top-dead-zone` | Top baseline area |
+
+## Performance Metrics
+
+| Metric | Value |
 |--------|-------|
-| mAP@50 | **92.13%** |
-| mAP@50-95 | **85.49%** |
-| Precision | 92.9% |
-| Recall | 92.0% |
-
-## Classes Detected (10)
-
-1. **racket** - Tennis rackets
-2. **tennis_ball** - Tennis balls
-3. **court** - Full court area
-4. **net** - Tennis net
-5. **left-service-box** - Left service box
-6. **right-service-box** - Right service box
-7. **left-doubles-alley** - Left doubles alley
-8. **right-doubles-alley** - Right doubles alley
-9. **top-dead-zone** - Top baseline area
-10. **bottom-dead-zone** - Bottom baseline area
+| **mAP@50** | **92.13%** |
+| **mAP@50-95** | **85.49%** |
+| **Precision** | 92.9% |
+| **Recall** | 92.0% |
 
 ## Training Details
 
-- **Base Model**: YOLOv11n
-- **Epochs**: 150
-- **Image Size**: 640x640
-- **Optimizer**: AdamW
-- **Learning Rate**: 0.001 (cosine decay to 0.01)
-- **Device**: MPS (Apple Silicon)
+### Datasets
+
+This model was trained on **3 combined datasets**:
+
+1. **Tennis Ball Dataset** - Ball detection
+2. **Tennis Racket Dataset** - Racket detection
+3. **Tennis Court Dataset** - Court zones and net detection
+
+### Training Configuration
+```yaml
+Model: YOLOv11n (nano)
+Epochs: 150
+Batch size: 16
+Image size: 640x640
+Device: Apple Silicon (MPS)
+Optimizer: AdamW
+Learning rate: 0.001 → 0.01
+Patience: 50 (early stopping)
+```
+
+### Augmentation
+- HSV color jitter (h=0.015, s=0.7, v=0.4)
+- Random horizontal flip (p=0.5)
+- Translation (±10%)
+- Scaling (±50%)
+- Mosaic augmentation
+
+### Loss Weights
+- Box loss: 7.5
+- Class loss: 0.5
+- DFL loss: 1.5
 
 ## Usage
 
+### Installation
+```bash
+pip install ultralytics
+```
+
+### Python API
 ```python
 from ultralytics import YOLO
 
-# Load the model
-model = YOLO('training/runs_combined/combined_final/weights/best.pt')
+# Load CourtSide Computer Vision v1 model
+model = YOLO('Davidsv/CourtSide-Computer-Vision-v1')
 
-# Run inference
+# Predict on image
 results = model.predict('tennis_match.jpg', conf=0.25)
 
-# Process results
-for r in results:
-    boxes = r.boxes
-    for box in boxes:
-        cls = int(box.cls[0])
-        conf = float(box.conf[0])
-        print(f"Detected: {model.names[cls]} ({conf:.2f})")
+# Display results
+results[0].show()
+
+# Get detections by class
+for box in results[0].boxes:
+    cls = int(box.cls[0])
+    conf = float(box.conf[0])
+    class_name = model.names[cls]
+    print(f"{class_name}: {conf:.2%}")
 ```
 
-## Training Curves
+### Video Processing
+```python
+from ultralytics import YOLO
+
+model = YOLO('Davidsv/CourtSide-Computer-Vision-v1')
 
-The model converged smoothly over 150 epochs with consistent improvement in both precision and recall metrics.
+# Process video with tracking
+results = model.track(
+    source='tennis_match.mp4',
+    conf=0.25,
+    tracker='bytetrack.yaml',
+    save=True
+)
+```
+
+### Command Line
+```bash
+# Predict on image
+yolo detect predict model=Davidsv/CourtSide-Computer-Vision-v1 source=image.jpg conf=0.25
+
+# Predict on video
+yolo detect predict model=Davidsv/CourtSide-Computer-Vision-v1 source=video.mp4 conf=0.25 save=True
+
+# Track objects in video
+yolo detect track model=Davidsv/CourtSide-Computer-Vision-v1 source=video.mp4 conf=0.25
+```
 
-## Applications
+## Recommended Hyperparameters
+
+### Inference Settings
+```python
+# Balanced (recommended)
+conf_threshold = 0.25  # Confidence threshold
+iou_threshold = 0.45   # NMS IoU threshold
+
+# High precision (fewer false positives)
+conf_threshold = 0.40
+iou_threshold = 0.45
+
+# High recall (detect more objects)
+conf_threshold = 0.15
+iou_threshold = 0.40
+```
+
+## Use Cases
 
 - Real-time tennis match analysis
-- Player tracking and movement analysis
+- Player position and movement tracking
 - Ball trajectory prediction
 - Court zone occupancy analysis
 - Automated highlight generation
+- Swing detection and technique analysis
+- Sports analytics dashboards
+- Training video analysis
+
+## CourtSide Computer Vision Suite
+
+| Version | Description | mAP@50 |
+|---------|-------------|--------|
+| v0.1 | Tennis Ball Detection | 85.6% |
+| v0.2 | Tennis Racket Detection | 66.7% |
+| **v1** | **Complete Tennis Detection (10 classes)** | **92.1%** |
+
+## Model Card Authors
+
+- **Developed by**: Davidsv (Vuong)
+- **Model date**: November 2024
+- **Model version**: v1
+- **Model type**: Object Detection (YOLOv11)
+- **Part of**: CourtSide Computer Vision Suite
+
+## Citations
+
+### This Model
+```bibtex
+@misc{courtsidecv_v1_2024,
+  title={CourtSide Computer Vision v1: Complete Tennis Detection with YOLOv11},
+  author={Vuong},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v1}}
+}
+```
+
+### Ultralytics YOLOv11
+```bibtex
+@software{yolov11_ultralytics,
+  author = {Glenn Jocher and Jing Qiu},
+  title = {Ultralytics YOLO11},
+  version = {11.0.0},
+  year = {2024},
+  url = {https://github.com/ultralytics/ultralytics},
+  license = {AGPL-3.0}
+}
+```
 
 ## License
 
-MIT License
+MIT License - Free for commercial and academic use.
+
+## Acknowledgments
+
+- Built with [Ultralytics YOLOv11](https://github.com/ultralytics/ultralytics)
+- Training datasets from [Roboflow Universe](https://universe.roboflow.com)
+- Part of the CourtSide Computer Vision project for tennis analysis
+
+## Contact & Support
+
+- Hugging Face: [@Davidsv](https://huggingface.co/Davidsv)
+
+---
+
+**Model Size**: ~5.4 MB
+**Supported Formats**: PyTorch (.pt), ONNX, TensorRT, CoreML
+**Model Hub**: [Davidsv/CourtSide-Computer-Vision-v1](https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v1)