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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 |