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

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking "Accept All", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on "Manage Settings." By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.