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:
- 60f0b18b8e4e0c4f6f2ac83607040f6c20f6b5d811a4e972cd7d95f25785624c
- Size of remote file:
- 343 MB
- SHA256:
- 3975df5a443c080bba56efd8ffe56e10cb2d2ff08649b75892ae1a30d1bb9229
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