Instructions to use dqnguyen/Diabetic_Foot_Ulcer_Image_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dqnguyen/Diabetic_Foot_Ulcer_Image_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dqnguyen/Diabetic_Foot_Ulcer_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("dqnguyen/Diabetic_Foot_Ulcer_Image_Classification") model = AutoModelForImageClassification.from_pretrained("dqnguyen/Diabetic_Foot_Ulcer_Image_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4bf0ba9edac875f8c9c909988e2966ad30b1a134ec29334f769e8205787eeb8
- Size of remote file:
- 343 MB
- SHA256:
- a13c84e081fb8288fb69e236f47256ffb1ff20e4f725cc9c7b141d15bd5961ee
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