Instructions to use google/vit-base-patch32-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch32-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-base-patch32-384") 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-patch32-384") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch32-384", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/vit-base-patch32-384: direct link, hf CLI and curl.
- Browser
- Download file 353 MB
-
https://huggingface.co/google/vit-base-patch32-384/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/vit-base-patch32-384/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/vit-base-patch32-384/resolve/main/pytorch_model.bin
353 MB
- Xet hash:
- 106c1ef570ddf12492a5d38eb7a19064cb6fff0c092034dc51af705040e9eb72
- Size of remote file:
- 353 MB
- SHA256:
- b72d8c538cca7091192311e138ce7444d958cf27c4588daca398d30b2be0b9b5
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