Instructions to use google/vit-base-patch32-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch32-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="google/vit-base-patch32-224-in21k")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-base-patch32-224-in21k") model = AutoModel.from_pretrained("google/vit-base-patch32-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/vit-base-patch32-224-in21k: direct link, hf CLI and curl.
- Browser
- Download file 352 MB
-
https://huggingface.co/google/vit-base-patch32-224-in21k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/vit-base-patch32-224-in21k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/vit-base-patch32-224-in21k/resolve/main/pytorch_model.bin
352 MB
- Xet hash:
- d8344fe6f957b453c46f102e10752764706ccb957cd1fc5c6725812c28bbca1e
- Size of remote file:
- 352 MB
- SHA256:
- d1f5662c99f04647c12757a8b72eb61ae150d819069b05e73fb1064b737bafcb
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