Writer : Hiroshi Fukui

Maintainer : Tsubasa Hirakawa

This repository contains the source code of Attention Branch Network for image classification. The attention branch network is designed to extend the top-down visual explanation model by introducing a branch structure with an attention mechanism. ABN improves CNN’s performance and visual explanation at the same time by gnerating attention maps in the forward pass.

[CVF open access] [arXiv paper]

If you find this repository is useful. Please cite the following references.

@article{fukui2018cvpr,
    author = {Hiroshi Fukui and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi},
    title = {Attention Branch Network: Learning of Attention Mechanism for Visual Explanation},
    journal = {Computer Vision and Pattern Recognition},
    year = {2019},
    pages = {10705-10714}
}

@article{fukui2018arxiv,
    author = {Hiroshi Fukui and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi},
    title = {Attention Branch Network: Learning of Attention Mechanism for Visual Explanation},
    journal = {arXiv preprint arXiv:1812.10025},
    year = {2018}
}  

Our source code is based on https://github.com/bearpaw/pytorch-classification/ implemented with PyTorch. We are grateful for the author! Requirements of PyTorch version are as follows:

• PyTorch : 0.4.0
• PyTorch vision : 0.2.1

We prepared Docker environments for ABN. You quickly start to use Docker and run scripts. For more details, please see docker/README.md.

Example of run command is as follows:

python3 cifar.py -a resnet --dataset cifar100 --depth 110 --epochs 300 --schedule 150 225 --gamma 0.1 --wd 1e-4 --checkpoint checkpoints/cifar100/resnet-110 --gpu-id 0,1

python3 imagenet.py -a resnet152 --data ../../dataset/imagenet_data/ --epochs 90 --schedule 31 61 --gamma 0.1 -c checkpoints/imagenet/resnet152 --gpu-id 4,5,6,7 --test-batch 100

python3 cifar.py -a resnet --dataset cifar100 --depth 110 --epochs 300 --schedule 150 225 --gamma 0.1 --wd 1e-4 --checkpoint checkpoints/cifar100/resnet-110 --gpu-id 0,1 --evaluate --resume checkpoints/cifar100/resnet-110/model_best.pth.tar

python3 imagenet.py -a resnet152 --data ../../../../dataset/imagenet_data/ --epochs 90 --schedule 31 61 --gamma 0.1 -c checkpoints/imagenet/resnet152 --gpu-id 4,5,6 --test-batch 10 --evaluate --resume checkpoints/imagenet/resnet152/model_best.pth.tar

If you try the other models or ImageNet, please see TRAINING.md.

We have published the model files of ABN, which are ResNet family models on CIFAR100 and ImageNet2012 dataset.

• ResNet110
• DenseNet
• Wide ResNet
• ResNeXt

• ResNet50
• ResNet101
• ResNet152