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