Instructions to use pk3388/vit-base-patch16-224-Rado_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pk3388/vit-base-patch16-224-Rado_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pk3388/vit-base-patch16-224-Rado_5") 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("pk3388/vit-base-patch16-224-Rado_5") model = AutoModelForImageClassification.from_pretrained("pk3388/vit-base-patch16-224-Rado_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from pk3388/vit-base-patch16-224-Rado_5: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/pk3388/vit-base-patch16-224-Rado_5/resolve/main/model.safetensors
- Command line
-
hf download hf://pk3388/vit-base-patch16-224-Rado_5/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/pk3388/vit-base-patch16-224-Rado_5/resolve/main/model.safetensors
343 MB
- Xet hash:
- 237dccc1197f4960dec734ad3393de0d27124d44f058f7d11bc73e41d6f88152
- Size of remote file:
- 343 MB
- SHA256:
- cd25ae2d3cb632a4d21418980c17101bc2849a999963df925dd5cecf285eaef7
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