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Slep
/
CondViT-B16-cat

Feature Extraction
Transformers
Safetensors
condvit
lrvsf-benchmark
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Slep/CondViT-B16-cat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Slep/CondViT-B16-cat with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Slep/CondViT-B16-cat", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Slep/CondViT-B16-cat", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
CondViT-B16-cat
345 MB
Ctrl+K
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  • 2 contributors
History: 12 commits
Slep's picture
Slep
Update README.md
2095254 verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    4.07 kB
    Update README.md almost 2 years ago
  • config.json
    431 Bytes
    Upload CondViTForEmbedding about 2 years ago
  • hf_model.py
    1.31 kB
    Upload CondViTForEmbedding about 2 years ago
  • model.safetensors
    345 MB
    xet
    Upload CondViTForEmbedding about 2 years ago
  • module.py
    4.78 kB
    Upload CondViTForEmbedding about 2 years ago
  • preprocessor_config.json
    526 Bytes
    Upload processor about 2 years ago
  • processor.py
    2.97 kB
    Update processor __call__ for single image/category about 2 years ago