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apple
/
aimv2-large-patch14-224-lit

Zero-Shot Image Classification
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2
feature-extraction
vision
custom_code
Model card Files Files and versions
xet
Community
6

Instructions to use apple/aimv2-large-patch14-224-lit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use apple/aimv2-large-patch14-224-lit with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-image-classification", model="apple/aimv2-large-patch14-224-lit", trust_remote_code=True)
    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, AutoModel
    
    processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit", trust_remote_code=True)
    model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224-lit", trust_remote_code=True)
  • MLX

    How to use apple/aimv2-large-patch14-224-lit with MLX:

    # Download the model from the Hub
    pip install huggingface_hub[hf_xet]
    
    huggingface-cli download --local-dir aimv2-large-patch14-224-lit apple/aimv2-large-patch14-224-lit
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • LM Studio
aimv2-large-patch14-224-lit
5.25 GB
Ctrl+K
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  • 4 contributors
History: 7 commits
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  • modeling_aimv2.py
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  • preprocessor_config.json
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