Instructions to use cpnlab/YOLOR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use cpnlab/YOLOR with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("cpnlab/YOLOR") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a2599e0e91166055b97149cb5f2e70c7fb24157cbc893223c426dcb04f63688
- Size of remote file:
- 115 MB
- SHA256:
- 1c6c261959a9cc2a32514c7a1162fc170b0565f695be4cfced60a0b3e152f86c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.