Instructions to use comdet/liver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use comdet/liver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="comdet/liver") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("comdet/liver") model = AutoModelForImageClassification.from_pretrained("comdet/liver") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c505457852f1b8c0499b6da1c99711a2db0104ff088aacbcb77e1ccf3956d88b
- Size of remote file:
- 343 MB
- SHA256:
- ae844aaa664d879af7e96b8d6cab2da34a819d94e721369fe31ba629c9c838b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.