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Style-Based Recalibration Module

The official PyTorch implementation of "SRM : A Style-based Recalibration Module for Convolutional Neural Networks" for ImageNet. SRM is a lightweight architectural unit that dynamically recalibrates feature responses based on style importance.

Overview of Results

Training and validation curves on ImageNet with ResNet-50

Top-1 and top-5 accuracy (%) on the ImageNet-1K validation set

Example results of style transfer

Prerequisites

  • PyTorch 0.4.0+
  • Python 3.6
  • CUDA 8.0+

Training Examples

  • Train ResNet-50
python imagenet.py --depth 50 --data /data/imagenet/ILSVRC2012 --gpu-id 0,1,2,3,4,5,6,7 --checkpoint resnet50/baseline
  • Train SRM-ResNet-50
python imagenet.py --depth 50 --data /data/imagenet/ILSVRC2012 --gpu-id 0,1,2,3,4,5,6,7 --checkpoint resnet50/srm --recalibration-type srm
  • Train SE-ResNet-50
python imagenet.py --depth 50 --data /data/imagenet/ILSVRC2012 --gpu-id 0,1,2,3,4,5,6,7 --checkpoint resnet50/se --recalibration-type se
  • Train GE-ResNet-50
python imagenet.py --depth 50 --data /data/imagenet/ILSVRC2012 --gpu-id 0,1,2,3,4,5,6,7 --checkpoint resnet50/ge --recalibration-type ge

Acknowledgment

This code is heavily borrowed from pytorch-classification.

Note

  • 28/05/2019: initial code for ImageNet is released

About

PyTorch code for our paper : "SRM : A Style-based Recalibration Module for Convolutional Neural Networks" (https://arxiv.org/abs/1903.10829)

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