Instructions to use WinKawaks/vit-small-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WinKawaks/vit-small-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="WinKawaks/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("WinKawaks/vit-small-patch16-224") model = AutoModelForImageClassification.from_pretrained("WinKawaks/vit-small-patch16-224") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f06edfda6e83d7391e391a90bd2010964bda902ad012bd1df8149fc6a289924c
- Size of remote file:
- 131 MB
- SHA256:
- fb2ce3b3cb6352c95301765d63602d7126c09e93a70b0fad0b6e2ca430667432
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