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SeyedAli
/
Distilled-Melanoma-Classification-ResNet50

Image Classification
Transformers
Safetensors
resnet
Model card Files Files and versions
xet
Community

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")
    # pip install -U transformers accelerate
    # 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
Distilled-Melanoma-Classification-ResNet50
283 MB
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  • 1 contributor
History: 4 commits
SeyedAli's picture
SeyedAli
Upload optimizer.pt
8ee90b3 verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    21 Bytes
    initial commit over 2 years ago
  • config.json
    1.15 kB
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  • model.safetensors
    94.4 MB
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  • optimizer.pt
    188 MB
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  • preprocessor_config.json
    325 Bytes
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  • rng_state.pth
    14.2 kB
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  • scheduler.pt
    1.06 kB
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  • trainer_state.json
    11.1 kB
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  • training_args.bin
    4.79 kB
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