Instructions to use SeyedAli/Distilled-Melanoma-Classification-ResNet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SeyedAli/Distilled-Melanoma-Classification-ResNet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SeyedAli/Distilled-Melanoma-Classification-ResNet50") 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-ResNet50") model = AutoModelForImageClassification.from_pretrained("SeyedAli/Distilled-Melanoma-Classification-ResNet50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f305b41af754efa0984bc9b90330fb4941234f256c6f9ad073d22b1b7d448b9
- Size of remote file:
- 4.79 kB
- SHA256:
- 5367ea2cfd26f868af758497aa386d16a636d56cefc1bf14b908b78cdc7766d5
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