NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
SE-ResNeXt101-32x4d pretrained weights (PyTorch, AMP, ImageNet)
Model
NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
SE-ResNeXt101-32x4d pretrained weights (PyTorch, AMP, ImageNet)

SE-ResNeXt101-32x4d ImageNet pretrained weights

  • Model Overview

    ResNeXt with Squeeze-and-Excitation module added.

    Model Architecture

    SEArch

    Image source: Squeeze-and-Excitation Networks

    Image shows the architecture of SE block and where is it placed in ResNet bottleneck block.

    Training

    This model was trained using script available on NGC and in GitHub repo

    Dataset

    The following datasets were used to train this model:

    • ImageNet - Image database organized according to the WordNet hierarchy, in which each noun is depicted by hundreds and thousands of images.

    Performance

    Performance numbers for this model are available in NGC

    References

    License

    This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.

    Publisher
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    Latest Version20.06.0
    UpdatedApril 4, 2023 UTC
    Compressed Size173.26 MB

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