https://github.com/luuuyi/CBAM.PyTorch

Non-official implement of Paper：CBAM: Convolutional Block Attention Module
https://github.com/luuuyi/CBAM.PyTorch

Last synced: 3 months ago
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Non-official implement of Paper：CBAM: Convolutional Block Attention Module

• Host: GitHub
• URL: https://github.com/luuuyi/CBAM.PyTorch
• Owner: luuuyi
• Created: 2018-09-11T15:02:39.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-07-12T10:24:30.000Z (over 1 year ago)
• Last Synced: 2024-05-10T12:34:50.184Z (6 months ago)
• Language: Python
• Size: 72.3 KB
• Stars: 1,276
• Watchers: 12
• Forks: 287
• Open Issues: 10
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-image-classification - unofficial-pytorch : https://github.com/luuuyi/CBAM.PyTorch

README

        
# CBAM.PyTorch

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

## Introduction

The codes are [PyTorch](https://pytorch.org/) 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](http://openaccess.thecvf.com/content_ECCV_2018/papers/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.pdf)

## 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](imgs/01.png)

## 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             |