Instructions to use ahishamm/vit-base-isic-patch-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahishamm/vit-base-isic-patch-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahishamm/vit-base-isic-patch-16") 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("ahishamm/vit-base-isic-patch-16") model = AutoModelForImageClassification.from_pretrained("ahishamm/vit-base-isic-patch-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ahishamm/vit-base-isic-patch-16: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ahishamm/vit-base-isic-patch-16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ahishamm/vit-base-isic-patch-16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ahishamm/vit-base-isic-patch-16/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 535441013d2ec65fe8863a98dca66e5fb5d22e97edea8e6374abd2085056492a
- Size of remote file:
- 343 MB
- SHA256:
- 326a68fd29ea4f94a2086d29d5cd66789ff42ab0abbed98caede0b100de2fee6
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