Instructions to use Hemg/Wound-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Wound-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemg/Wound-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-classification") model = AutoModelForImageClassification.from_pretrained("Hemg/Wound-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eee1d6310361c20e858e262316233ca49cea36fcd55943c01ec4bfe5712d04c
- Size of remote file:
- 4.92 kB
- SHA256:
- 14e86cf3bb658dae59d7d23e201403dc1ef98e845e921cb1701a19e42eeb7592
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