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hf-internal-testing
/
tiny-random-ViTMAEModel

Image Feature Extraction
Transformers
PyTorch
google-tensorflow TensorFlow
ONNX
vit_mae
Model card Files Files and versions
xet
Community
4

Instructions to use hf-internal-testing/tiny-random-ViTMAEModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use hf-internal-testing/tiny-random-ViTMAEModel with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-ViTMAEModel")
    # Load model directly
    from transformers import AutoImageProcessor, AutoModel
    
    processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ViTMAEModel")
    model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ViTMAEModel")
  • Notebooks
  • Google Colab
  • Kaggle
tiny-random-ViTMAEModel
741 kB
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  • 2 contributors
History: 3 commits
Xenova's picture
Xenova HF Staff
Upload ONNX weights (#2)
91b7b12 verified over 1 year ago
  • onnx
    Upload ONNX weights (#2) over 1 year ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • config.json
    712 Bytes
    Upload tiny models for ViTMAEModel over 3 years ago
  • preprocessor_config.json
    342 Bytes
    Upload tiny models for ViTMAEModel over 3 years ago
  • pytorch_model.bin
    194 kB
    xet
    Upload tiny models for ViTMAEModel over 3 years ago
  • tf_model.h5
    284 kB
    xet
    Upload tiny models for ViTMAEModel over 3 years ago