Instructions to use ahishamm/vit-large-binary-isic-patch-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahishamm/vit-large-binary-isic-patch-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahishamm/vit-large-binary-isic-patch-16") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ahishamm/vit-large-binary-isic-patch-16") model = AutoModelForImageClassification.from_pretrained("ahishamm/vit-large-binary-isic-patch-16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f9d3f4687012035590ce9b8fd471ec4fcf1613914b1e7cc3b3160fef3b944e4
- Size of remote file:
- 1.21 GB
- SHA256:
- d862f5a4265bd94dc86538672331478cf6033f313cb68239e9feda87e4ba317c
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