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:
- e5cae5383b2267d65fcc42c7c190386e8e670e0f66f30143abde6a9f48f65b03
- Size of remote file:
- 4.09 kB
- SHA256:
- 7d4e9bc5cce472e87d28f0814c3bdc25d30279d397b94afda8445685fb46a168
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