Awesome
PyTorch Implementation of shake-shake
Usage
$ python train.py --depth 26 --base_channels 32 --shake_forward True --shake_backward True --shake_image True --outdir results
Results on CIFAR-10
| Model | Test Error (median of 3 runs) | Test Error (in paper) | Training Time |
|---|---|---|---|
| shake-shake-26 2x32d (S-S-I) | 3.68 | 3.55 (average of 3 runs) | 11h16m |
| shake-shake-26 2x64d (S-S-I) | 2.88 (1 run) | 2.98 (average of 3 runs) | 19h43m |
| shake-shake-26 2x96d (S-S-I) | 2.90 (1 run) | 2.86 (average of 5 runs) | 32h10m |
Notes
- Tesla V100 was used in these experiments.
References
- Gastaldi, Xavier. "Shake-Shake regularization." In International Conference on Learning Representations, 2017. arXiv:1705.07485, Torch implementation