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https://github.com/nutszebra/resnet_in_resnet

Implementation of Residual Networks In Residual Networks by chainer (Resnet in Resnet: Generalizing Residual Architectures: https://arxiv.org/abs/1603.08029)
https://github.com/nutszebra/resnet_in_resnet

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Implementation of Residual Networks In Residual Networks by chainer (Resnet in Resnet: Generalizing Residual Architectures: https://arxiv.org/abs/1603.08029)

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README

        

# 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]][Paper] 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

| network | total accuracy (%) |
|:---------------------|-------------------:|
| 18-layer + wide RiR | 94.99 |
| my implementation | 94.43 |

loss
total accuracy

# Reference
Resnet in Resnet: Generalizing Residual Architectures [[1]][Paper]

[paper]: https://arxiv.org/abs/1603.08029 "Paper"