Awesome
PyTorch Implementation of CutMix
Usage
$ python train.py --depth 20 --use_cutmix --outdir results
Results on CIFAR-10
| Model | Test Error (median of 3 runs) | Training Time |
|---|
| WRN-20-4 | 4.56 | 1h22m |
| WRN-20-4, CutMix (alpha=1) | 3.62 | 1h22m |
- These models were trained for 300 epochs with batch size 128, initial learning rate 0.2, and cosine annealing.
- Test errors reported above are of the last epoch.
- These experiments were done using Tesla V100.
w/o CutMix
$ python -u train.py --depth 20 --base_channels 64 --base_lr 0.2 --scheduler cosine --seed 7 --outdir results/wo_cutmix/00
w/ CutMix
$ python -u train.py --depth 20 --base_channels 64 --base_lr 0.2 --scheduler cosine --seed 7 --use_cutmix --cutmix_alpha 1.0 --outdir results/w_cutmix/00
References
- Yun, Sangdoo, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. "CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features." arXiv preprint arXiv:1905.04899 (2019). arXiv:1905.04899