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grelade
/
mmx-feature-extraction

Image Classification
Transformers
PyTorch
google-tensorflow TensorFlow
Safetensors
resnet
image-feature-extraction
vision
Model card Files Files and versions
xet
Community

Instructions to use grelade/mmx-feature-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use grelade/mmx-feature-extraction with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="grelade/mmx-feature-extraction")
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoImageProcessor, AutoModel
    
    processor = AutoImageProcessor.from_pretrained("grelade/mmx-feature-extraction")
    model = AutoModel.from_pretrained("grelade/mmx-feature-extraction", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
mmx-feature-extraction
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  • 1 contributor
History: 2 commits
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grelade
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  • model.safetensors
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  • preprocessor_config.json
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  • pytorch_model.bin
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