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ENOT-AutoDL
/
imagenet-benchmark

Image Classification
Transformers
ONNX
ENOT-AutoDL
Model card Files Files and versions
xet
Community

Instructions to use ENOT-AutoDL/imagenet-benchmark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ENOT-AutoDL/imagenet-benchmark with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-classification", model="ENOT-AutoDL/imagenet-benchmark")
    pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ENOT-AutoDL/imagenet-benchmark", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
imagenet-benchmark / ViT-B-32
112 MB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 2 commits
savchenkoyana's picture
savchenkoyana
add ViT x4.8 ONNX, small fixes in test.py, and allow measuring macs on ONNX
061bac4 over 2 years ago
  • ViT-B-32-ENOT-x4_8.onnx
    72.2 MB
    xet
    add ViT x4.8 ONNX, small fixes in test.py, and allow measuring macs on ONNX over 2 years ago
  • ViT-B-32-ENOT-x9.onnx
    39.4 MB
    xet
    add ViT x4.8 ONNX, small fixes in test.py, and allow measuring macs on ONNX over 2 years ago