Update README.md

README.md

@@ -10,8 +10,9 @@ tags:
   - computer-vision
   - pytorch
   - ultralytics
+  - courtside
 datasets:
-  - custom
+  - tennis-ball-detection
 metrics:
   - precision
   - recall
@@ -20,12 +21,13 @@ library_name: ultralytics
 pipeline_tag: object-detection
 ---
 
-# YOLOv11 Tennis Ball Detection 🎾
+# CourtSide Computer Vision v0.1 🎾
 
-Fine-tuned YOLOv11n model for detecting tennis balls in images and videos.
+Fine-tuned YOLOv11n model for detecting tennis balls in images and videos. Part of the CourtSide Computer Vision suite for comprehensive tennis match analysis.
 
 ## Model Details
 
+- **Model Name**: CourtSide Computer Vision v0.1
 - **Model Type**: Object Detection
 - **Architecture**: YOLOv11 Nano (n)
 - **Framework**: Ultralytics YOLOv11
@@ -48,11 +50,15 @@ Evaluated on validation set (62 images):
 ## Training Details
 
 ### Dataset
+
+This model was trained on the **Tennis Ball Detection Dataset** by Viren Dhanwani, available on Roboflow Universe.
+
 - **Training images**: 408
 - **Validation images**: 62
 - **Test images**: 50
 - **Total**: 520 annotated images
 - **Annotation format**: YOLO format (bounding boxes)
+- **Source**: [Roboflow Universe - Tennis Ball Detection](https://universe.roboflow.com/viren-dhanwani/tennis-ball-detection)
 
 ### Training Configuration
 ```yaml
@@ -81,19 +87,17 @@ Training time: ~23 minutes
 ## Usage
 
 ### Installation
-
 ```bash
 pip install ultralytics
 ```
 
 ### Python API
-
 ```python
 from ultralytics import YOLO
 from PIL import Image
 
-# Load model
-model = YOLO('path/to/tennis_ball_subset_best.pt')
+# Load CourtSide Computer Vision model
+model = YOLO('courtsidecv_v0.1.pt')
 
 # Predict on image
 results = model.predict('tennis_match.jpg', conf=0.3)
@@ -109,11 +113,10 @@ for box in results[0].boxes:
 ```
 
 ### Video Processing
-
 ```python
 from ultralytics import YOLO
 
-model = YOLO('path/to/tennis_ball_subset_best.pt')
+model = YOLO('courtsidecv_v0.1.pt')
 
 # Process video
 results = model.predict(
@@ -125,22 +128,20 @@ results = model.predict(
 ```
 
 ### Command Line
-
 ```bash
 # Predict on image
-yolo detect predict model=tennis_ball_subset_best.pt source=image.jpg conf=0.3
+yolo detect predict model=courtsidecv_v0.1.pt source=image.jpg conf=0.3
 
 # Predict on video
-yolo detect predict model=tennis_ball_subset_best.pt source=video.mp4 conf=0.3 save=True
+yolo detect predict model=courtsidecv_v0.1.pt source=video.mp4 conf=0.3 save=True
 
 # Validate model
-yolo detect val model=tennis_ball_subset_best.pt data=dataset.yaml
+yolo detect val model=courtsidecv_v0.1.pt data=dataset.yaml
 ```
 
 ## Recommended Hyperparameters
 
 ### Inference Settings
-
 ```python
 # Balanced (recommended)
 conf_threshold = 0.30  # Confidence threshold
@@ -175,11 +176,12 @@ max_det = 100
 ## Use Cases
 
 ✅ **Recommended:**
-- Tennis match analysis
+- Tennis match analysis and statistics
 - Automated highlight generation
-- Player training and coaching
-- Sports analytics
-- Ball tracking for statistics
+- Player training and coaching tools
+- Ball trajectory tracking
+- Sports analytics dashboards
+- Action recognition pipelines
 
 ⚠️ **Not Recommended:**
 - Real-time umpiring decisions (use as assistance only)
@@ -202,20 +204,29 @@ max_det = 100
 | 0.3 - 0.5 | Low confidence - possible tennis ball |
 | < 0.3 | Very low confidence - likely false positive |
 
+## CourtSide Computer Vision Suite
+
+This model is part of the **CourtSide Computer Vision** project, a comprehensive tennis analysis system featuring:
+- 🎾 Ball detection (this model)
+- 👤 Player detection and tracking
+- 🎯 Action recognition (forehand, backhand, serve, etc.)
+- 📊 Match statistics generation
+
 ## Model Card Authors
 
 - **Developed by**: Vuong
 - **Model date**: November 2024
-- **Model version**: 1.0
+- **Model version**: v0.1
 - **Model type**: Object Detection (YOLOv11)
 
-## Citation
+## Citations
 
-If you use this model, please cite:
+### This Model
 
+If you use this model, please cite:
 ```bibtex
-@misc{yolov11_tennis_ball_2024,
-  title={YOLOv11 Tennis Ball Detection},
+@misc{courtsidecv_v0.1_2024,
+  title={CourtSide Computer Vision v0.1: Tennis Ball Detection with YOLOv11},
   author={Vuong},
   year={2024},
   publisher={Hugging Face},
@@ -223,6 +234,24 @@ If you use this model, please cite:
 }
 ```
 
+### Dataset
+
+This model was trained using the Tennis Ball Detection dataset. Please cite:
+```bibtex
+@misc{tennis-ball-detection_dataset,
+  title = {tennis ball detection Dataset},
+  type = {Open Source Dataset},
+  author = {Viren Dhanwani},
+  howpublished = {\url{https://universe.roboflow.com/viren-dhanwani/tennis-ball-detection}},
+  url = {https://universe.roboflow.com/viren-dhanwani/tennis-ball-detection},
+  journal = {Roboflow Universe},
+  publisher = {Roboflow},
+  year = {2023},
+  month = {feb},
+  note = {visited on 2024-11-20}
+}
+```
+
 ## License
 
 MIT License - Free for commercial and academic use.
@@ -230,8 +259,8 @@ 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 Viren Dhanwani via [Roboflow Universe](https://universe.roboflow.com/viren-dhanwani/tennis-ball-detection)
+- Part of the CourtSide Computer Vision project for tennis analysis
 
 ## Contact & Support
 
@@ -241,20 +270,24 @@ For questions, issues, or collaboration:
 
 ## Related Models
 
-- [YOLOv11 Tennis Racket Detection](https://huggingface.co/...) - Companion model for racket detection
+Coming soon in the CourtSide Computer Vision suite:
+- CourtSide CV - Player Detection
+- CourtSide CV - Racket Detection
+- CourtSide CV - Court Segmentation
 
 ## Model Changelog
 
-### v1.0 (2024-11-20)
+### v0.1 (2024-11-20)
 - Initial release
 - YOLOv11n architecture
 - mAP@50: 67.87%
-- 520 training images
+- 520 training images from Roboflow dataset
+- Optimized for tennis ball detection in match footage
 
 ---
 
-**Model Size**: 5.4 MB
-**Inference Speed**: 10-65ms (device dependent)
+**Model Size**: 5.4 MB  
+**Inference Speed**: 10-65ms (device dependent)  
 **Supported Formats**: PyTorch (.pt), ONNX, TensorRT, CoreML
 
 🎾 Ready for production use in tennis analysis applications!