Object Detection
ultralytics
PyTorch
English
yolo
yolov11
tennis
racket
tennis-ball
court-detection
sports
computer-vision
courtside
Eval Results (legacy)
Instructions to use Davidsv/CourtSide-Computer-Vision-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Davidsv/CourtSide-Computer-Vision-v1 with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("Davidsv/CourtSide-Computer-Vision-v1") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 434 Bytes
af5d451 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | #!/usr/bin/env python
# Example usage for tennis-detection-yolov11
from ultralytics import YOLO
# Load model from local file
model = YOLO('model.pt')
# Or download from Hugging Face (after upload)
# model = YOLO('hf://YOUR_USERNAME/tennis-detection-yolov11/model.pt')
# Predict on image
results = model.predict('image.jpg', conf=0.3)
results[0].show()
# Predict on video
results = model.predict('video.mp4', conf=0.3, save=True)
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