Awesome
BAM and CBAM
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
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. ACC@1=77.622 ACC@5=93.948
- ResNet50+BAM (trained for 90 epochs) checkpoint is provided in this link. 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