Davidsv/CourtSide-Computer-Vision-v1

CourtSide Computer Vision v1 - Complete Tennis Detection

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.

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%

Training Details

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

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

pip install ultralytics

Python API

from ultralytics import YOLO

# Load CourtSide Computer Vision v1 model
model = YOLO('Davidsv/CourtSide-Computer-Vision-v1')

# Predict on image
results = model.predict('tennis_match.jpg', conf=0.25)

# 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%}")

Video Processing

from ultralytics import YOLO

model = YOLO('Davidsv/CourtSide-Computer-Vision-v1')

# Process video with tracking
results = model.track(
    source='tennis_match.mp4',
    conf=0.25,
    tracker='bytetrack.yaml',
    save=True
)

Command Line

# 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

Recommended Hyperparameters

Inference Settings

# 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 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

@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

@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}
}

Datasets

tennis-court-keypoints Computer Vision Model

@misc{
                            tennis-court-keypoints_dataset,
                            title = { tennis-court-keypoints Dataset },
                            type = { Open Source Dataset },
                            author = { TennisCV },
                            howpublished = { \url{ https://universe.roboflow.com/tenniscv-yywpa/tennis-court-keypoints } },
                            url = { https://universe.roboflow.com/tenniscv-yywpa/tennis-court-keypoints },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { mar },
                            note = { visited on 2025-11-21 },
                            }

dataset1 Computer Vision Dataset

@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 2025-11-21 },
                            }

tennis ball detection Computer Vision Dataset

@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 2025-11-21 },
                            }

License

MIT License - Free for commercial and academic use.

Acknowledgments

• Built with Ultralytics YOLOv11
• Training datasets from Roboflow Universe
• Part of the CourtSide Computer Vision project for tennis analysis

Contact & Support

• Hugging Face: @Davidsv

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Model Size: ~5.4 MB Supported Formats: PyTorch (.pt), ONNX, TensorRT, CoreML Model Hub: Davidsv/CourtSide-Computer-Vision-v1