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 pytorch_model.bin from Batr97/ViT_ordinary: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Batr97/ViT_ordinary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Batr97/ViT_ordinary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Batr97/ViT_ordinary/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 2824b8cd12fe70f8f2e3b7a19da7cb29b84310df888cf951d17994f75913759a
- Size of remote file:
- 343 MB
- SHA256:
- cd1776ccb0275a483d9293c5a4cd5eda00e2efe35feb16677adda2460e74762a
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