Instructions to use wbxlala/vit-small-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wbxlala/vit-small-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="wbxlala/vit-small-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("wbxlala/vit-small-patch16-224") model = AutoModelForImageClassification.from_pretrained("wbxlala/vit-small-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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version https://git-lfs.github.com/spec/v1
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oid sha256:228fcc84ee1183c61bf262cadd7403cd619af45da6d7b597612a2b38f2f30498
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size 86688624
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