Instructions to use DataScienceProject/Vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataScienceProject/Vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DataScienceProject/Vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScienceProject/Vit", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b825c5145b7f5040f5c3e09d5ccc15b8080ab736db8626590dafa8c9ee006193
- Size of remote file:
- 4.13 MB
- SHA256:
- a88630c27b8fcdaf16e2d5d4e91613b89a8dd79e07486b31b89921236357ed91
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