Instructions to use ismgar01/vit-base-patch16-imagenette with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ismgar01/vit-base-patch16-imagenette with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ismgar01/vit-base-patch16-imagenette") 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("ismgar01/vit-base-patch16-imagenette") model = AutoModelForImageClassification.from_pretrained("ismgar01/vit-base-patch16-imagenette") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 164f65c1d9a6a9671e16041b79522b480a317efeb5acf8b7307a357e83ea432d
- Size of remote file:
- 343 MB
- SHA256:
- b65a66aae999b465d44c8362dfb0d8836770c97c2a218997c6fcd07d549af16f
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