Instructions to use venuv62/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use venuv62/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="venuv62/vit-base-patch16-224") 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("venuv62/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("venuv62/vit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from venuv62/vit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/venuv62/vit-base-patch16-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://venuv62/vit-base-patch16-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/venuv62/vit-base-patch16-224/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 2d6da2b7eded01a1624bb6869583a16a706a77dbd14a319b4ca80eef418aaa3b
- Size of remote file:
- 343 MB
- SHA256:
- ce3886e6fa597b2d45d0d57921bb1d5dacfff19dcd03361f2f143c7901c58e95
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