Instructions to use Vombit/yolov10x_cs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use Vombit/yolov10x_cs2 with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10x_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - ultralytics
How to use Vombit/yolov10x_cs2 with ultralytics:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10x_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b55fd19b99631572f7f5b3cc895f0944d0e49ff9560021aca800fa16664dfd78
- Size of remote file:
- 118 MB
- SHA256:
- 9e4b42394bea24aaaa211c696b3b48e81d1413d307fcae90666c3987308d5465
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.