Non-official implement of Paper：CBAM: Convolutional Block Attention Module
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
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.
- Python3
- PyTorch 0.4.1
- tensorboardX (optional)
- torchnet
- pretrainedmodels (optional)
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.
| Models | validation(Top-1) | validation(Top-5) |
|---|---|---|
| ResNet50 | 74.26 | 91.91 |
| ResNet50-CBAM | 75.45 | 92.55 |
