Awesome
Shake-Shake regularization
PyTorch implementation of shake-shake regularization.
Dependencies
- python 3.5
- PyTorch 1.0.0
Accuracy
CIFAR-10
| Model | Method | This implementaion | Paper |
|---|---|---|---|
| ResNet26-2x32 | S-S-I | 96.37 | 96.45 |
| ResNet26-2x64 | S-S-I | 97.07 | 97.02 |
CIFAR-100
| Model | Method | This implementaion | Paper |
|---|---|---|---|
| ResNeXt29-2x4x64d | S-S-I | TODO | 84.03 |
Train ResNet26-2x64d for CIFAR-10
python train.py --label 10 --depth 26 --w_base 64 --lr 0.1 --epochs 1800 --batch_size 64
Effects of learning epoch on accuracy
| Model | Method | Epoch | This implementaion | Paper |
|---|---|---|---|---|
| ResNet26-2x64 | S-S-I | 300 | 96.46 | - |
| ResNet26-2x64 | S-S-I | 600 | 96.65 | - |
| ResNet26-2x64 | S-S-I | 900 | 96.78 | - |
| ResNet26-2x64 | S-S-I | 1200 | 96.68 | - |
| ResNet26-2x64 | S-S-I | 1500 | 96.77 | - |
| ResNet26-2x64 | S-S-I | 1800 | 97.07 | 97.02 |
References
Gastaldi, Xavier. "Shake-Shake regularization." arXiv:1705.07485v2
Gastaldi, Xavier. "Shake-Shake regularization of 3-branch residual networks." ICLR2017 Workshop
Author Implementation https://github.com/xgastaldi/shake-shake