NGC | Catalog
Welcome Guest
CatalogModelsSE-ResNeXt101-32x4d pretrained weights (PyTorch, AMP, ImageNet)

SE-ResNeXt101-32x4d pretrained weights (PyTorch, AMP, ImageNet)

For downloads and more information, please view on a desktop device.
Logo for SE-ResNeXt101-32x4d pretrained weights (PyTorch, AMP, ImageNet)

Description

SE-ResNeXt101-32x4d ImageNet pretrained weights

Publisher

NVIDIA Deep Learning Examples

Use Case

Classification

Framework

PyTorch

Latest Version

20.06.0

Modified

October 29, 2021

Size

173.26 MB

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.