Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Official PyTorch implementation of Ensemble of Averages

This repository is built upon the DomainBed repository by FAIR.

Environment:

	Python: 3.6.8
	PyTorch: 1.9.0+cu111
	Torchvision: 0.10.0+cu111
	CUDA: 11.1
	CUDNN: 8005
	NumPy: 1.19.5
	PIL: 8.4.0

Run Commands

Simple Moving Average (SMA):

First we train the models with our SMA protocol.

All experiments below use the ImageNet pre-trained ResNet-50 architecture, specified as resnet50 in the command using the arch hyper-parameter. Other supported options include resnext50_swsl and regnety_16gf corresponding to weakly-supervised pre-trained models ResNeXt-50 32x4d from Yalniz et al, and RegNetY-16GF from Singh et al respectively.

PACS:

python -m domainbed.scripts.sweep launch --data_dir path/to/data --output_dir erm-sma_resnet50/pacs --command_launcher multi_gpu --algorithms ERM_SMA --datasets PACS --n_hparams 3 --n_trials 2 --single_test_envs --hparams '{"arch": "resnet50"}'

VLCS:

python -m domainbed.scripts.sweep launch --data_dir path/to/data --output_dir erm-sma_resnet50/vlcs --command_launcher multi_gpu --algorithms ERM_SMA --datasets VLCS --n_hparams 3 --n_trials 2 --single_test_envs --hparams '{"arch": "resnet50"}'

OfficeHome:

python -m domainbed.scripts.sweep launch --data_dir path/to/data --output_dir erm-sma_resnet50/officehome --command_launcher multi_gpu --algorithms ERM_SMA --datasets OfficeHome --n_hparams 3 --n_trials 2 --single_test_envs --hparams '{"arch": "resnet50"}'

TerraIncognita:

python -m domainbed.scripts.sweep launch --data_dir path/to/data --output_dir erm-sma_resnet50/terra --command_launcher multi_gpu --algorithms ERM_SMA --datasets TerraIncognita --n_hparams 3 --n_trials 2 --single_test_envs --hparams '{"arch": "resnet50"}'

DomainNet (notice that the number of steps is set to 15000 for this dataset following SWAD):

python -m domainbed.scripts.sweep launch --data_dir path/to/data --output_dir erm-sma_resnet50/domainnet --command_launcher multi_gpu --algorithms ERM_SMA --datasets DomainNet --n_hparams 3 --n_trials 2 --single_test_envs --hparams '{"arch": "resnet50"}' --steps 15000

Ensemble of Averages (EoA)

We now use the best SMA models saved from the above runs (using in-domain validation accuracy based early stopping) in an ensemble, that we call EoA since these ensembles contain moving average models.

TIP: Use larger values of num_workers and batch_size for faster runtime.

PACS (note that the output_dir argument is same as the value used above in the SMA experiment for PACS dataset):

python -m domainbed.EoA --data_dir path/to/data --dataset PACS --output_dir erm-sma_resnet50/pacs --hparams '{"num_workers": 1, "batch_size": 128, "arch": "resnet50"}'

Citation

If you use our work, please cite us:

@article{arpit2021ensemble,
  title={Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization},
  author={Arpit, Devansh and Wang, Huan and Zhou, Yingbo and Xiong, Caiming},
  journal={arXiv preprint arXiv:2110.10832},
  year={2021}
}

License

This repository is released under the MIT License. See this for details.

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ensemble-of-averages's Introduction

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Environment:

Run Commands

Simple Moving Average (SMA):

Ensemble of Averages (EoA)

Citation

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