CBAM-TensorFlow

This is a Tensorflow implementation of "CBAM: Convolutional Block Attention Module". This repository includes the implementation of "Squeeze-and-Excitation Networks" as well, so that you can train and compare among base CNN model, base model with CBAM block and base model with SE block. Base CNN models are ResNext, Inception-V4, and Inception-ResNet-V2 where the implementation is revised from Junho Kim's code: SENet-Tensorflow.

If you want to use more sophisticated implementation and more base models to use, check the repository "CBAM-TensorFlow-Slim" which aims to be compatible on the TensorFlow-Slim image classification model library and support more base models.

CBAM: Convolutional Block Attention Module

CBAM proposes an architectural unit called "Convolutional Block Attention Module" (CBAM) block to improve representation power by using attention mechanism: focusing on important features and supressing unnecessary ones. This research can be considered as a descendant and an improvement of "Squeeze-and-Excitation Networks".

Diagram of a CBAM_block

Diagram of each attention sub-module

Classification results on ImageNet-1K

Prerequisites

• Python 3.x
• TensorFlow 1.x
• tflearn

Prepare Data set

This repository use Cifar10 dataset. When you run the training script, the dataset will be automatically downloaded.

CBAM_block and SE_block Supportive Models

You can train and test base CNN model, base model with CBAM block and base model with SE block. You can run CBAM_block or SE_block added models in the below list by adding one argument --attention_module=cbam_block or --attention_module=se_block when you train a model.

• Inception V4 + CBAM / + SE
• Inception-ResNet-v2 + CBAM / + SE
• ResNeXt + CBAM / + SE

Change Reduction ratio

To change reduction ratio, you can add an argument --reduction_ratio=8.

Train a Model

You can simply run a model by executing following scripts.

• sh train_ResNext.sh
• sh train_inception_resnet_v2.sh
• sh train_inception_v4.sh

Train a model with CBAM_block

Below script gives you an example of training a model with CBAM_block.

CUDA_VISIBLE_DEVICES=0 python ResNeXt.py \
--model_name put_your_model_name \
--attention_module cbam_block  \
--reduction_ratio 8 \
--learning_rate 0.1 \
--weight_decay 0.0005 \
--momentum 0.9 \
--batch_size 128 \
--total_epoch 100 \
--attention_module cbam_block

Train a model with SE_block

Below script gives you an example of training a model with SE_block.

CUDA_VISIBLE_DEVICES=0 python ResNeXt.py \
--model_name put_your_model_name \
--attention_module cbam_block  \
--reduction_ratio 8 \
--learning_rate 0.1 \
--weight_decay 0.0005 \
--momentum 0.9 \
--batch_size 128 \
--total_epoch 100 \
--attention_module se_block

Train a model without attention module

Below script gives you an example of training a model without attention module.

CUDA_VISIBLE_DEVICES=0 python ResNeXt.py \
--model_name put_your_model_name \
--attention_module cbam_block  \
--reduction_ratio 8 \
--learning_rate 0.1 \
--weight_decay 0.0005 \
--momentum 0.9 \
--batch_size 128 \
--total_epoch 100

Related Works

• Blog: CBAM: Convolutional Block Attention Module
• Repository: CBAM-TensorFlow-Slim
• Repository: CBAM-Keras
• Repository: SENet-TensorFlow-Slim

Reference

• Paper: CBAM: Convolutional Block Attention Module
• Paper: Squeeze-and-Excitation Networks
• Repository: TensorFlow-Slim image classification model library
• Repository: SENet-Tensorflow

Author

Byung Soo Ko / [email protected]