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")# 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 rng_state.pth from SeyedAli/Distilled-Melanoma-Classification-EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/SeyedAli/Distilled-Melanoma-Classification-EfficientNet/resolve/main/rng_state.pth
- Command line
-
hf download hf://SeyedAli/Distilled-Melanoma-Classification-EfficientNet/rng_state.pth
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curl -L -o rng_state.pth https://huggingface.co/SeyedAli/Distilled-Melanoma-Classification-EfficientNet/resolve/main/rng_state.pth
14.2 kB
- Xet hash:
- b3fefaf215287e12cc2382a4d9922ec07deedd142f56f0501553578af549ab4f
- Size of remote file:
- 14.2 kB
- SHA256:
- 889de10fb992b121100f458ef9f2f4499ecc7b00accce13dcd308479c6f90e30
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