Instructions to use Prot10/vit-base-patch16-224-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/vit-base-patch16-224-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/vit-base-patch16-224-for-pre_evaluation") 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("Prot10/vit-base-patch16-224-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/vit-base-patch16-224-for-pre_evaluation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 831aa52c3bc267888039b7483b26e83882cd8e5e2990913beec0e803b8c842d6
- Size of remote file:
- 343 MB
- SHA256:
- 3cc73c1db631427e15dd1e273cd6c0d1f045a5010fc3040cf6f8f2151b2bb387
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