Official Implementation of TeachAugment in PyTorch.
arXiv: https://arxiv.org/abs/2202.12513
- PyTorch >= 1.9
- Torchvision >= 0.10
Training with single GPU
python main.py --yaml ./config/$DATASET_NAME/$MODEL
Training with single node multi-GPU
python -m torch.distributed.launch --nproc_per_node=$N_GPUS main.py \
--yaml ./config/$DATASET_NAME/$MODEL --dist
Examples
# Training WRN-28-10 on CIFAR-100
python main.py --yaml ./config/CIFAR100/wrn-28-10.yaml
# Training ResNet-50 on ImageNet with 4 GPUs
python -m torch.distributed.launch --nproc_per_node=4 main.py \
--yaml ./config/ImageNet/resnet50.yaml --dist
If the computational resources are limited, please try --save_memory option.
If you find our project useful in your research, please cite it as follows:
@InProceedings{Suzuki_2022_CVPR,
author = {Suzuki, Teppei},
title = {TeachAugment: Data Augmentation Optimization Using Teacher Knowledge},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10904-10914}
}
The files in ./lib/models and the code in ./lib/augmentation/imagenet_augmentation.py are based on the implementation of Fast AutoAugment.