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
Adding ONNX file of this model
#3
by ivensamdh - opened
- model.onnx +3 -0
model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:e904d8f1f58f0eead9e910dc5fd6479b87a54e6205cda365ef83e99e7f3dde5b
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size 346533767
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