by Hengshuang Zhao, Jiaya Jia, and Vladlen Koltun, details are in paper.

This repository is build for the proposed self-attention network (SAN), which contains full training and testing code. The implementation of SA module with optimized CUDA kernels are also included.

1. Requirement:

   • Hardware: tested with 8 x Quadro RTX 6000 (24G).
   • Software: tested with PyTorch 1.4.0, Python3.7, CUDA 10.1, CuPy 10.1, tensorboardX.

2. Clone the repository:

   git clone https://github.com/hszhao/SAN.git

3. Train:

   • Download and prepare the ImageNet dataset (ILSVRC2012) and symlink the path to it as follows (you can alternatively modify the relevant path specified in folder config):

     cd SAN
     mkdir -p dataset
     ln -s /path_to_ILSVRC2012_dataset dataset/ILSVRC2012
     

   • Specify the gpus (usually 8 gpus are adopted) used in config and then do training:

     sh tool/train.sh imagenet san10_pairwise
     

   • If you are using SLURM for nodes manager, uncomment lines in train.sh and then do training:

     sbatch tool/train.sh imagenet san10_pairwise

4. Test:

   • Download trained SAN models and put them under folder specified in config or modify the specified paths, and then do testing:

     sh tool/test.sh imagenet san10_pairwise

5. Visualization:

   • tensorboardX incorporated for better visualization regarding curves:

     tensorboard --logdir=exp/imagenet

6. Other:

   • Resources: GoogleDrive LINK contains shared models.

Train Parameters: train_gpus(8), batch_size(256), epochs(100), base_lr(0.1), lr_scheduler(cosine), label_smoothing(0.1), momentum(0.9), weight_decay(1e-4).

Overall result:

If you find the code or trained models useful, please consider citing:

@inproceedings{zhao2020san,
  title={Exploring Self-attention for Image Recognition},
  author={Zhao, Hengshuang and Jia, Jiaya and Koltun, Vladlen},
  booktitle={CVPR},
  year={2020}
}