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Davidsv/CourtSide-Computer-Vision-v1

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Model Card

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.

[image]

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

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 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

python

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

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

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 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}
}

Datasets

tennis-court-keypoints Computer Vision Model

bibtex

@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

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 2025-11-21 },
                            }

tennis ball detection Computer Vision Dataset

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 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

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