Image Segmentation
ultralytics
TensorBoard
PyTorch
v8
ultralyticsplus
yolov8
yolo
vision
Eval Results (legacy)
Instructions to use fcakyon/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use fcakyon/test-model with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("fcakyon/test-model") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50ff0d7aefba4660781fcc843c04be3dedcec23d4b7d175baeb43f7b1c1d0601
- Size of remote file:
- 1.08 MB
- SHA256:
- a90ee64131278f7d768c75845bb0c0680a8871fa621020084016b72fc893942e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.