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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
Last synced: about 1 month ago
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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)
- Host: GitHub
- URL: https://github.com/nutszebra/resnet_in_resnet
- Owner: nutszebra
- License: mit
- Created: 2016-12-17T14:05:54.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-01-19T02:20:54.000Z (almost 8 years ago)
- Last Synced: 2024-05-10T09:32:41.836Z (7 months ago)
- Language: Python
- Homepage:
- Size: 111 KB
- Stars: 3
- Watchers: 2
- Forks: 3
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-chainer : https://github.com/nutszebra/resnet_in_resnet
- awesome-image-classification - unofficial-chainer : https://github.com/nutszebra/resnet_in_resnet
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 |
# Reference
Resnet in Resnet: Generalizing Residual Architectures [[1]][Paper][paper]: https://arxiv.org/abs/1603.08029 "Paper"