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JH-C-k
/
clipL336_TTR

Image Feature Extraction
Transformers
PyTorch
custom_clip_with_registers
feature-extraction
clip
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use JH-C-k/clipL336_TTR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use JH-C-k/clipL336_TTR with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="JH-C-k/clipL336_TTR", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("JH-C-k/clipL336_TTR", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
clipL336_TTR / __pycache__
101 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
JH-C-k's picture
JH-C-k
Add files using upload-large-folder tool
f62695c verified 9 months ago
  • imagenet_classes.cpython-310.pyc
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  • misc.cpython-310.pyc
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  • model.cpython-310.pyc
    12.5 kB
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  • modified_resnet.cpython-310.pyc
    6.39 kB
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  • shared.cpython-310.pyc
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  • timm_model.cpython-310.pyc
    4.04 kB
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  • tokenizer.cpython-310.pyc
    8.63 kB
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  • transformer.cpython-310.pyc
    27.4 kB
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