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
- Xet hash:
- 741608a5dbfc78a3167bdf5a8ee89fce66273852be167d6bdd4200cf3d553407
- Size of remote file:
- 4.79 kB
- SHA256:
- f0fa6160d92a99d193faea72b2c9e54c3a35ac04b040cb7bfdf9e8377262ad96
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