Awesome

ShakeDrop

This repository contains the implementation of the paper ShakeDrop regularization. The code is based on fb.resnet.torch and PyramidNet.

Usage

1. Install Torch and fb.resnet.torch and put on all files of this repository into "fb.resnet.torch/models".
2. Change the learning rate schedule in the file train.lua: "decay = epoch >= 122 and 2 or epoch >= 81 and 1 or 0" to "decay = epoch >= 225 and 2 or epoch >= 150 and 1 or 0".
3. Train the network, by running main.lua as below: To train additive PyramidNet-110 (alpha=270) on CIFAR-10 dataset:

CUDA_VISIBLE_DEVICES=0,1,2,3 th main.lua -dataset cifar10 -nEpochs 300 -netType pyramidnet -batchSize 128 -LR 0.5 -shareGradInput true -nGPU 4 -nThreads 8

To train additive PyramidNet-110 (alpha=270) on CIFAR-100 dataset:

CUDA_VISIBLE_DEVICES=0,1,2,3 th main.lua -dataset cifar100 -nEpochs 300 -netType pyramidnet -batchSize 128 -LR 0.5 -shareGradInput true -nGPU 4 -nThreads 8

The "ShakeDrop.lua" is a implementation of pixel level ShakeDrop with memory efficiency. In the paper, PyramidNet-110 (alpha=270) with pixel level ShakeDrop achieved 15.78% error on CIFAR-100.