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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScienceProject/Vit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from DataScienceProject/Vit: direct link, hf CLI and curl.
- Browser
- Download file 131 Bytes
-
https://huggingface.co/DataScienceProject/Vit/resolve/main/requirements.txt
- Command line
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hf download hf://DataScienceProject/Vit/requirements.txt
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curl -L -o requirements.txt https://huggingface.co/DataScienceProject/Vit/resolve/main/requirements.txt
131 Bytes
| # Requires Python 3.10.14 | |
| torch==2.3.0+cu121 | |
| torchvision==0.18.0 | |
| transformers==4.40.2 | |
| Pillow | |
| pandas | |
| scikit-learn | |
| matplotlib | |
| seaborn |