Instructions to use google/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/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("google/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/vit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/google/vit-base-patch16-224/resolve/f20e8ddae12d9a22e353f2095c04b3348009a76e/pytorch_model.bin
- Command line
-
hf download hf://google/vit-base-patch16-224@f20e8ddae12d9a22e353f2095c04b3348009a76e/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/vit-base-patch16-224/resolve/f20e8ddae12d9a22e353f2095c04b3348009a76e/pytorch_model.bin
346 MB
- Xet hash:
- 04c5a02c5e26a07ab5b5918501f8899ce4580b6cfc3fd8aac45f17de6fe61411
- Size of remote file:
- 346 MB
- SHA256:
- 556534b1c1f236a442f9e7c61d1870c1b51afb1f527cebc4cc6363698042ef98
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