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 huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("Davidsv/CourtSide-Computer-Vision-v1", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download example.py from Davidsv/CourtSide-Computer-Vision-v1: direct link, hf CLI and curl.
- Browser
- Download file 434 Bytes
-
https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v1/resolve/main/example.py
- Command line
-
hf download hf://Davidsv/CourtSide-Computer-Vision-v1/example.py
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curl -L -o example.py https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v1/resolve/main/example.py
434 Bytes
| #!/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) | |