Instructions to use Enayat/ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Enayat/ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enayat/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 AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Enayat/ViT") model = AutoModelForImageClassification.from_pretrained("Enayat/ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Enayat/ViT: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Enayat/ViT/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Enayat/ViT@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Enayat/ViT/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- d69b1796da8699e257edddc5c823ad2e5b955057b86691af7feaafcd97b30c86
- Size of remote file:
- 343 MB
- SHA256:
- 79f434c42930b3782aa00e5779d558a444c1adea1bfa1565dbc6ff4ae8feea3b
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