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suinleelab
/
monet

Zero-Shot Image Classification
Transformers
Safetensors
clip
vision
dermatology
medical
Model card Files Files and versions
xet
Community

Instructions to use suinleelab/monet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use suinleelab/monet with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-image-classification", model="suinleelab/monet")
    pipe(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png",
        candidate_labels=["animals", "humans", "landscape"],
    )
    # Load model directly
    from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
    
    processor = AutoProcessor.from_pretrained("suinleelab/monet")
    model = AutoModelForZeroShotImageClassification.from_pretrained("suinleelab/monet")
  • Notebooks
  • Google Colab
  • Kaggle
monet
1.71 GB
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  • 1 contributor
History: 10 commits
chanwkim's picture
chanwkim
update bibtex
1d1efd0 about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    4.21 kB
    update bibtex about 2 years ago
  • config.json
    4.52 kB
    first commit about 2 years ago
  • merges.txt
    525 kB
    first commit about 2 years ago
  • model.safetensors
    1.71 GB
    xet
    first commit about 2 years ago
  • preprocessor_config.json
    286 Bytes
    update README and norm mean about 2 years ago
  • special_tokens_map.json
    389 Bytes
    first commit about 2 years ago
  • tokenizer.json
    2.22 MB
    first commit about 2 years ago
  • tokenizer_config.json
    905 Bytes
    first commit about 2 years ago
  • vocab.json
    961 kB
    first commit about 2 years ago