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MVRL
/
rcme-vit-base-patch16

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

Instructions to use MVRL/rcme-vit-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MVRL/rcme-vit-base-patch16 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-image-classification", model="MVRL/rcme-vit-base-patch16")
    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("MVRL/rcme-vit-base-patch16")
    model = AutoModelForZeroShotImageClassification.from_pretrained("MVRL/rcme-vit-base-patch16")
  • Notebooks
  • Google Colab
  • Kaggle
rcme-vit-base-patch16
599 MB
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  • 1 contributor
History: 2 commits
Srikumar26's picture
Srikumar26
Upload model
05e21b7 verified 10 months ago
  • .gitattributes
    1.52 kB
    initial commit 10 months ago
  • README.md
    5.17 kB
    Upload model 10 months ago
  • config.json
    1.21 kB
    Upload model 10 months ago
  • model.safetensors
    599 MB
    xet
    Upload model 10 months ago