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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Hemg/Wound-Image-classification") model = AutoModelForImageClassification.from_pretrained("Hemg/Wound-Image-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tmp-checkpoint-6/optimizer.pt from Hemg/Wound-Image-classification: direct link, hf CLI and curl.
- Browser
- Download file 179 MB
-
https://huggingface.co/Hemg/Wound-Image-classification/resolve/main/tmp-checkpoint-6/optimizer.pt
- Command line
-
hf download hf://Hemg/Wound-Image-classification/tmp-checkpoint-6/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Hemg/Wound-Image-classification/resolve/main/tmp-checkpoint-6/optimizer.pt
179 MB
- Xet hash:
- 26476c3ab37ca6f2c44f86836b89deda04a188e4a1e31a2d409a95b913ac2532
- Size of remote file:
- 179 MB
- SHA256:
- 2ae92162a2a58454b320a83014fe2b505a2607842f9bf2e406a6d768b1be78df
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