CourtSide-Computer-Vision-v0.1

CourtSide Computer Vision v0.1 🎾

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
• Parameters: 2.6M
• Input Size: 640x640
• Classes: 1 (tennis_ball)

Performance Metrics

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

Training Configuration

Model: YOLOv11n (nano)
Epochs: 100
Batch size: 16
Image size: 640x640
Device: Apple M4 Pro (MPS)
Optimizer: AdamW
Learning rate: 0.001 → 0.01
Training time: ~23 minutes

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

pip install ultralytics

Python API

from ultralytics import YOLO
from PIL import Image

# Load CourtSide Computer Vision model
model = YOLO('courtsidecv_v0.1.pt')

# Predict on image
results = model.predict('tennis_match.jpg', conf=0.3)

# Display results
results[0].show()

# Get bounding boxes
for box in results[0].boxes:
    x1, y1, x2, y2 = box.xyxy[0]
    confidence = box.conf[0]
    print(f"Ball detected at [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] with {confidence:.2%} confidence")

Video Processing

from ultralytics import YOLO

model = YOLO('courtsidecv_v0.1.pt')

# Process video
results = model.predict(
    source='tennis_match.mp4',
    conf=0.3,
    save=True,
    save_txt=True
)

Command Line

# Predict on image
yolo detect predict model=courtsidecv_v0.1.pt source=image.jpg conf=0.3

# Predict on video
yolo detect predict model=courtsidecv_v0.1.pt source=video.mp4 conf=0.3 save=True

# Validate model
yolo detect val model=courtsidecv_v0.1.pt data=dataset.yaml

Recommended Hyperparameters

Inference Settings

# Balanced (recommended)
conf_threshold = 0.30  # Confidence threshold
iou_threshold = 0.45   # NMS IoU threshold
max_det = 50           # Maximum detections per image

# High precision (fewer false positives)
conf_threshold = 0.50
iou_threshold = 0.45
max_det = 30

# High recall (detect more balls, more false positives)
conf_threshold = 0.20
iou_threshold = 0.40
max_det = 100

Limitations

• Small objects: Performance may degrade for tennis balls that are very far from the camera (< 20px)
• Motion blur: Fast-moving balls may be harder to detect
• Occlusion: Partially hidden balls may not be detected
• Similar objects: May occasionally detect other small round objects
• Lighting: Optimized for outdoor tennis lighting conditions

Model Biases

• Trained primarily on standard yellow tennis balls
• Dataset includes various court types but may have dataset-specific biases
• Better performance on professional match footage vs amateur recordings

Use Cases

✅ Recommended:

• Tennis match analysis and statistics
• Automated highlight generation
• Player training and coaching tools
• Ball trajectory tracking
• Sports analytics dashboards
• Action recognition pipelines

⚠️ Not Recommended:

• Real-time umpiring decisions (use as assistance only)
• Safety-critical applications
• Detection of non-yellow tennis balls without fine-tuning

Example Results

Sample Detections

Precision: 84.3% - When the model detects a ball, it's correct 84% of the time Recall: 59.5% - The model detects approximately 6 out of 10 tennis balls

Confidence Interpretation

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: v0.1
• Model type: Object Detection (YOLOv11)

Citations

This Model

If you use this model, please cite:

@misc{courtsidecv_v0.1_2024,
  title={CourtSide Computer Vision v0.1: Tennis Ball Detection with YOLOv11},
  author={Vuong},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/...}}
}

Dataset

This model was trained using the Tennis Ball Detection dataset. Please cite:

@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.

Acknowledgments

• Built with Ultralytics YOLOv11
• Dataset by Viren Dhanwani via Roboflow Universe
• Part of the CourtSide Computer Vision project for tennis analysis

Contact & Support

For questions, issues, or collaboration:

• GitHub Issues: tennis_analysis/issues
• Model Updates: Check for newer versions on Hugging Face

Related Models

Coming soon in the CourtSide Computer Vision suite:

• CourtSide CV - Player Detection
• CourtSide CV - Racket Detection
• CourtSide CV - Court Segmentation

Model Changelog

v0.1 (2024-11-20)

• Initial release
• YOLOv11n architecture
• mAP@50: 67.87%
• 520 training images from Roboflow dataset
• Optimized for tennis ball detection in match footage

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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!