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.
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 |