Awesome

What's this

Implementation of Residual Networks In Residual Networks by chainer

Dependencies

git clone https://github.com/nutszebra/resnet_in_resnet.git
cd resnet_in_resnet
git submodule init
git submodule update

How to run

python main.py -p ./ -g 0 

Details about my implementation

All hyperparameters and network architecture are the same as in [1] except for some parts.

• Data augmentation
  Train: Pictures are randomly resized in the range of [32, 36], then 32x32 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.
  Test: Pictures are resized to 32x32, then they are normalized locally. Single image test is used to calculate total accuracy.

• Learning rate
  Initial learning rate is 0.1. Learning rate is divided by 5 at [150, 225] and I totally run 300 epochs.

Cifar10 result

Reference

Resnet in Resnet: Generalizing Residual Architectures [1]