Python: 3.8.6
PyTorch: 1.7.0+cu92
Torchvision: 0.8.1+cu92
CUDA: 9.2
CUDNN: 7603
NumPy: 1.19.4
PIL: 8.0.1

+------------+--------------+---------+---------+---------+---------+
| Selection  | art_painting | cartoon |  photo  |  sketch |   Avg.  |
+------------+--------------+---------+---------+---------+---------+
|   oracle   |   82.245%    | 85.661% | 97.530% | 83.461% | 87.224% |
|    iid     |   87.919%    | 78.891% | 96.482% | 78.435% | 85.432% |
|    last    |   82.306%    | 81.823% | 95.135% | 82.061% | 85.331% |
| last (inD) |   95.807%    | 95.291% | 96.306% | 95.477% | 95.720% |
| iid (inD)  |   97.275%    | 96.619% | 96.696% | 97.253% | 96.961% |
|    SWAD    |   89.750%    | 82.942% | 97.979% | 81.870% | 88.135% |
| SWAD (inD) |   97.713%    | 97.649% | 97.316% | 98.074% | 97.688% |
+------------+--------------+---------+---------+---------+---------+

python train_all.py PACS0 --dataset PACS --deterministic --trial_seed 0 --checkpoint_freq 100 --data_dir /my/datasets/path
python train_all.py PACS1 --dataset PACS --deterministic --trial_seed 1 --checkpoint_freq 100 --data_dir /my/datasets/path
python train_all.py PACS2 --dataset PACS --deterministic --trial_seed 2 --checkpoint_freq 100 --data_dir /my/datasets/path

python train_all.py VLCS0 --dataset VLCS --deterministic --trial_seed 0 --checkpoint_freq 50 --tolerance_ratio 0.2 --data_dir /my/datasets/path
python train_all.py VLCS1 --dataset VLCS --deterministic --trial_seed 1 --checkpoint_freq 50 --tolerance_ratio 0.2 --data_dir /my/datasets/path
python train_all.py VLCS2 --dataset VLCS --deterministic --trial_seed 2 --checkpoint_freq 50 --tolerance_ratio 0.2 --data_dir /my/datasets/path

python train_all.py OH0 --dataset OfficeHome --deterministic --trial_seed 0 --checkpoint_freq 100 --data_dir /my/datasets/path
python train_all.py OH1 --dataset OfficeHome --deterministic --trial_seed 1 --checkpoint_freq 100 --data_dir /my/datasets/path
python train_all.py OH2 --dataset OfficeHome --deterministic --trial_seed 2 --checkpoint_freq 100 --data_dir /my/datasets/path

python train_all.py TR0 --dataset TerraIncognita --deterministic --trial_seed 0 --checkpoint_freq 100 --data_dir /my/datasets/path
python train_all.py TR1 --dataset TerraIncognita --deterministic --trial_seed 1 --checkpoint_freq 100 --data_dir /my/datasets/path
python train_all.py TR2 --dataset TerraIncognita --deterministic --trial_seed 2 --checkpoint_freq 100 --data_dir /my/datasets/path

python train_all.py DN0 --dataset DomainNet --deterministic --trial_seed 0 --checkpoint_freq 500 --data_dir /my/datasets/path
python train_all.py DN1 --dataset DomainNet --deterministic --trial_seed 1 --checkpoint_freq 500 --data_dir /my/datasets/path
python train_all.py DN2 --dataset DomainNet --deterministic --trial_seed 2 --checkpoint_freq 500 --data_dir /my/datasets/path

@inproceedings{cha2021swad,
  title={SWAD: Domain Generalization by Seeking Flat Minima},
  author={Cha, Junbum and Chun, Sanghyuk and Lee, Kyungjae and Cho, Han-Cheol and Park, Seunghyun and Lee, Yunsung and Park, Sungrae},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2021}
}