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redlessone
/
DermLIP_ViT-B-16

Zero-Shot Image Classification
Transformers
OpenCLIP
Safetensors
English
vision
image-text-to-text
medical
dermatology
multimodal
clip
zero-shot-classification
image-classification
Model card Files Files and versions
xet
Community
1

Instructions to use redlessone/DermLIP_ViT-B-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use redlessone/DermLIP_ViT-B-16 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-image-classification", model="redlessone/DermLIP_ViT-B-16")
    pipe(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png",
        candidate_labels=["animals", "humans", "landscape"],
    )
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("redlessone/DermLIP_ViT-B-16", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
DermLIP_ViT-B-16
1.2 GB
Ctrl+K
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  • 1 contributor
History: 5 commits
redlessone's picture
redlessone
Update README.md
b7d4273 verified 7 months ago
  • .gitattributes
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    initial commit 11 months ago
  • README.md
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  • merges.txt
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  • open_clip_config.json
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  • open_clip_model.safetensors
    599 MB
    xet
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  • open_clip_pytorch_model.bin
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • vocab.json
    862 kB
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