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CBAM.PyTorch

Non-official implement of Paper：CBAM: Convolutional Block Attention Module

Introduction

The codes are PyTorch re-implement version for paper: CBAM: Convolutional Block Attention Module

Woo S, Park J, Lee J Y, et al. CBAM: Convolutional Block Attention Module[J]. 2018. ECCV2018

Structure

The overview of CBAM. The module has two sequential sub-modules: channel and spatial. The intermediate feature map is adaptively refined through our module (CBAM) at every convolutional block of deep networks.

1

Requirements

  • Python3
  • PyTorch 0.4.1
  • tensorboardX (optional)
  • torchnet
  • pretrainedmodels (optional)

Results

We just test four models in ImageNet-1K, both train set and val set are scaled to 256(minimal side), only use Mirror and RandomResizeCrop as training data augmentation, during validation, we use center crop to get 224x224 patch.

ImageNet-1K

Models validation(Top-1) validation(Top-5)
ResNet50 74.26 91.91
ResNet50-CBAM 75.45 92.55