Instructions to use Vombit/yolov10l_cs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use Vombit/yolov10l_cs2 with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10l_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - ultralytics
How to use Vombit/yolov10l_cs2 with ultralytics:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10l_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdf69c6924e3f350ffc251b21c5aed20aa47d36d6e9f15ddd9be52046ab702ba
- Size of remote file:
- 97.5 MB
- SHA256:
- 7fd92d2977abd2f922f5a2e46285f3b08c2d08d8fb82579015a1321db6a7edb8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.