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https://github.com/Jongchan/attention-module
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
https://github.com/Jongchan/attention-module
Last synced: 2 months ago
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Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
- Host: GitHub
- URL: https://github.com/Jongchan/attention-module
- Owner: Jongchan
- License: mit
- Created: 2018-10-07T06:25:26.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-09T03:46:12.000Z (almost 2 years ago)
- Last Synced: 2024-08-04T01:14:23.525Z (6 months ago)
- Language: Python
- Size: 20.5 KB
- Stars: 2,025
- Watchers: 19
- Forks: 400
- Open Issues: 42
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - official-pytorch : https://github.com/Jongchan/attention-module
README
# BAM and CBAM
Official PyTorch code for "[BAM: Bottleneck Attention Module (BMVC2018)](http://bmvc2018.org/contents/papers/0092.pdf)" and "[CBAM: Convolutional Block Attention Module (ECCV2018)](http://openaccess.thecvf.com/content_ECCV_2018/html/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.html)"
### Updates & Notices
- 2018-10-08: ~~Currently, only CBAM test code is validated. **There may be minor errors in the training code**. Will be fixed in a few days.~~
- 2018-10-11: Training code validated. RESNET50+BAM pretrained weight added.
### Requirement
The code is validated under below environment:
- Ubuntu 16.04, 4*GTX 1080 Ti, Docker (PyTorch 0.4.1, CUDA 9.0 + CuDNN 7.0, Python 3.6)
### How to use
ResNet50 based examples are included. Example scripts are included under ```./scripts/``` directory.
ImageNet data should be included under ```./data/ImageNet/``` with foler named ```train``` and ```val```.
```
# To train with BAM (ResNet50 backbone)
python train_imagenet.py --ngpu 4 --workers 20 --arch resnet --depth 50 --epochs 100 --batch-size 256 --lr 0.1 --att-type BAM --prefix RESNET50_IMAGENET_BAM ./data/ImageNet
# To train with CBAM (ResNet50 backbone)
python train_imagenet.py --ngpu 4 --workers 20 --arch resnet --depth 50 --epochs 100 --batch-size 256 --lr 0.1 --att-type CBAM --prefix RESNET50_IMAGENET_CBAM ./data/ImageNet
```
### Resume with checkpoints
- ResNet50+CBAM (trained for 100 epochs) checkpoint is provided in this [link](https://drive.google.com/file/d/1mvAVvhLR_2XY_bPYxh-SEz4vDmGzSArO/view?usp=sharing). ACC@1=77.622 ACC@5=93.948
- ResNet50+BAM (trained for 90 epochs) checkpoint is provided in this [link](https://drive.google.com/file/d/1auVf70gfL0ol40bvaX5rlbpn9cKIxhAL/view?usp=sharing). ACC@1=76.860 ACC@5=93.416
For validation, please use the script as follows
```
python train_imagenet.py --ngpu 4 --workers 20 --arch resnet --depth 50 --att-type CBAM --prefix EVAL --resume $CHECKPOINT_PATH$ --evaluate ./data/ImageNet
```
### Other implementations
- [MXNet implementation of CBAM with several modifications](https://github.com/bruinxiong/Modified-CBAMnet.mxnet) by [bruinxiong](https://github.com/bruinxiong)