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")# 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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03cbedf1947fac820f1156dc02a1de3b8ead2d73376057aba85c2ad7dbc7bd47
- Size of remote file:
- 346 MB
- SHA256:
- 1cea07110a4a47edc51420b2dda6f3b8b58e7256e8f44b4ea6aa9696162ccb5d
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