Instructions to use ahishamm/vit-base-binary-isic-sharpened-patch-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahishamm/vit-base-binary-isic-sharpened-patch-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ahishamm/vit-base-binary-isic-sharpened-patch-32") 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-sharpened-patch-32") model = AutoModelForImageClassification.from_pretrained("ahishamm/vit-base-binary-isic-sharpened-patch-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 3000
Browse files
pytorch_model.bin
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runs/Jul01_13-19-20_293c456ba153/events.out.tfevents.1688217565.293c456ba153.1472.2
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