This is the official implementation of DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection in Pytorch.
Clone this directory and cd
into it.
git clone https://github.com/shuguang99/DualAug.git
cd DualAug
Install a fitting PyTorch version for your setup with GPU support, as our implementation only support setups with at least one CUDA device and install our requirements:
pip install -r requirements.txt
# Install a pytorch version, in many setups this has to be done manually, see pytorch.org
Now you should be ready to go. Start a training like so:
python -m Augment.train -c confs/dual_aug_wresnet28x10_cifar100_autoaug.yaml --dataroot data --tag EXPERIMENT_NAME
@article{wang2023dualaug,
title={DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection},
author={Wang, Zehao and Guo, Yiwen and Li, Qizhang and Yang, Guanglei and Zuo, Wangmeng},
journal={arXiv preprint arXiv:2310.08139},
year={2023}
}
This repository uses code from https://github.com/AIoT-MLSys-Lab/DeepAA.git