Instructions to use Batr97/ViT_ordinary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Batr97/ViT_ordinary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Batr97/ViT_ordinary") 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("Batr97/ViT_ordinary") model = AutoModelForImageClassification.from_pretrained("Batr97/ViT_ordinary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Batr97/ViT_ordinary: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/Batr97/ViT_ordinary/resolve/main/optimizer.pt
- Command line
-
hf download hf://Batr97/ViT_ordinary/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Batr97/ViT_ordinary/resolve/main/optimizer.pt
687 MB
- Xet hash:
- 11d48261c1570310346366eb5d7e027bce17cc8bebbca3ad74b0d55818621404
- Size of remote file:
- 687 MB
- SHA256:
- d43282583f4e6291a3d40f788104fb32d3d5fe3a8ad3d69eaac9f5bcbd07f652
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