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")# pip install -U transformers accelerate # 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
Download training_args.bin from suncy13/Foot: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/suncy13/Foot/resolve/main/training_args.bin
- Command line
-
hf download hf://suncy13/Foot/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/suncy13/Foot/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 28a1f62dc455eb6745e642f6af4c42f42574e05ee918c1ae767d0d25b7bed660
- Size of remote file:
- 4.54 kB
- SHA256:
- 27b13f0301416f12f0aa28d068b3380d24de868b4567133e345a0fdb458d0f89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.