Instructions to use ahishamm/vit-base-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-base-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-base-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-base-binary-isic-patch-16") model = AutoModelForImageClassification.from_pretrained("ahishamm/vit-base-binary-isic-patch-16") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
pytorch_model.bin
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runs/Jul01_11-49-46_9e49da66a448/events.out.tfevents.1688212195.9e49da66a448.1494.0
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