Awesome
OnlineAugment (Accepted at ECCV 2020)
Official OnlineAugment implementation in PyTorch
- More automatic than AutoAugment and related
- Towards fully automatic (STN and VAE, No need to specify the image primitives).
- Broad domains (natural, medical images, etc).
- Diverse tasks (classification, segmentation, etc).
- Easy to use
- One-stage training (user-friendly).
- Simple code (single GPU training, no need for parallel optimization).
- Orthogonal to AutoAugment and related
- Online v.s. Offline (Joint optimization, no expensive offline policy searching).
- State-of-the-art performance (in combination with AutoAugment).
(In this implementation, we disable the meta-gradient for efficient training. The code is also refactored accordingly, achieving comparable performance. Especially for reduced CIFARs, we observe higher accuracy than reported in the paper.)
Visualization on CIFAR-10
A-STN
D-VAE
P-VAE
Run
We conducted experiments in
- python 3.7
- pytorch 1.2, torchvision 0.4.0, cuda10
The searching of policies and training of target model is optimized jointly.
For example, training wide-resnet using STN on reduced CIFAR-10, using the script in r-cifar10-wrn-scripts
./run-aug-stn.sh
Citation
If this code is helpful for your research, please cite:
@article{tang2020onlineaugment,
title={OnlineAugment: Online Data Augmentation with Less Domain Knowledge},
author={Tang, Zhiqiang and Gao, Yunhe and Karlinsky, Leonid and Sattigeri, Prasanna and Feris, Rogerio and Metaxas, Dimitris},
journal={arXiv preprint arXiv:2007.09271},
year={2020}
}