Instructions to use SeyedAli/Distilled-Melanoma-Classification-EfficientNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SeyedAli/Distilled-Melanoma-Classification-EfficientNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SeyedAli/Distilled-Melanoma-Classification-EfficientNet") 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("SeyedAli/Distilled-Melanoma-Classification-EfficientNet") model = AutoModelForImageClassification.from_pretrained("SeyedAli/Distilled-Melanoma-Classification-EfficientNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from SeyedAli/Distilled-Melanoma-Classification-EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 511 MB
-
https://huggingface.co/SeyedAli/Distilled-Melanoma-Classification-EfficientNet/resolve/main/optimizer.pt
- Command line
-
hf download hf://SeyedAli/Distilled-Melanoma-Classification-EfficientNet/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SeyedAli/Distilled-Melanoma-Classification-EfficientNet/resolve/main/optimizer.pt
511 MB
- Xet hash:
- 250e77f8a9f96a940b86f588a0517d4ad4ded47c85a281e4e7b755a46085dc9c
- Size of remote file:
- 511 MB
- SHA256:
- 6d963fc8c26e686b48a8bef54d7768b6ae6a19ba5759f6b7017488101c17d8e5
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