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ashpika13
/
MambaVision-T-1K-FreqMix

Image Classification
Transformers
Safetensors
mambavision
image-feature-extraction
custom_code
Model card Files Files and versions
xet
Community

Instructions to use ashpika13/MambaVision-T-1K-FreqMix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ashpika13/MambaVision-T-1K-FreqMix with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="ashpika13/MambaVision-T-1K-FreqMix", trust_remote_code=True)
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModelForImageClassification
    model = AutoModelForImageClassification.from_pretrained("ashpika13/MambaVision-T-1K-FreqMix", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle

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  • __pycache__
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  • .gitattributes
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    initial commit 4 months ago
  • LICENSE
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  • README.md
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  • config.json
    36.4 kB
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  • configuration_mambavision.py
    625 Bytes
    Refactor: thread use_channel_split and use_dual_branch_mixer flags through constructor chain; remove monkeypatching 4 months ago
  • model.safetensors
    127 MB
    xet
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  • modeling_mambavision.py
    31.9 kB
    Refactor: thread use_channel_split and use_dual_branch_mixer flags through constructor chain; remove monkeypatching 4 months ago