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Qdrant
/
clip-ViT-B-32-vision

Image Classification
Transformers
ONNX
clip_vision_model
Model card Files Files and versions
xet
Community
1

Instructions to use Qdrant/clip-ViT-B-32-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Qdrant/clip-ViT-B-32-vision with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="Qdrant/clip-ViT-B-32-vision")
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Qdrant/clip-ViT-B-32-vision", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
clip-ViT-B-32-vision
352 MB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 6 commits
jmzzomg's picture
jmzzomg
Update README.md
e0c24ed verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    747 Bytes
    Update README.md over 1 year ago
  • config.json
    482 Bytes
    Upload config.json with huggingface_hub about 2 years ago
  • model.onnx
    352 MB
    xet
    Upload model.onnx with huggingface_hub about 2 years ago
  • preprocessor_config.json
    780 Bytes
    Upload preprocessor_config.json with huggingface_hub about 2 years ago