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_detection.png from Davidsv/CourtSide-Computer-Vision-v1: direct link, hf CLI and curl.
- Browser
- Download file 4.82 MB
-
https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v1/resolve/main/example_detection.png
- Command line
-
hf download hf://Davidsv/CourtSide-Computer-Vision-v1/example_detection.png
-
curl -L -o example_detection.png https://huggingface.co/Davidsv/CourtSide-Computer-Vision-v1/resolve/main/example_detection.png
4.82 MB

- Xet hash:
- 7ea2ef63024260dc2059463725a1c6d49a4f8b004669aacf6a875ce795f0ac59
- Size of remote file:
- 4.82 MB
- SHA256:
- 95c370145381418a933631a1ab967723f2bef18e6a45fef889f9b2218774d516
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