Instructions to use Hemg/Wound-Image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Wound-Image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemg/Wound-Image-classification") 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("Hemg/Wound-Image-classification") model = AutoModelForImageClassification.from_pretrained("Hemg/Wound-Image-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26476c3ab37ca6f2c44f86836b89deda04a188e4a1e31a2d409a95b913ac2532
- Size of remote file:
- 179 MB
- SHA256:
- 2ae92162a2a58454b320a83014fe2b505a2607842f9bf2e406a6d768b1be78df
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