Instructions to use suncy13/Foot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suncy13/Foot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="suncy13/Foot") 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("suncy13/Foot") model = AutoModelForImageClassification.from_pretrained("suncy13/Foot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a138f8f070f45be11dbf5e8a7f6eda9557da483fefe569b00cac550eaba32576
- Size of remote file:
- 346 MB
- SHA256:
- 4d5947a32ebdf6c37f894e493e81e8f9252c931c0b75f61d50ddb629c5a19c7c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.