ResNets experiments on cifar10 with caffe
Citation
@article{He2015,
author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {arXiv preprint arXiv:1512.03385},
year = {2015}
}
Introduction
This repository reimplements resnet experiments on cifar10 with caffe according to the paper "Deep Residual Learning for Image Recognition" (http://arxiv.org/abs/1512.03385). The data augmentation means 4 pixels are padded on each side for every images during training. You can make datasets prepared by using the scripts.
Structure
The network structure is here(we only list the network of 20 depth):
ResNet_20
PlainNet_20
Usage
First, you should make sure that your caffe is correctly installed. You can follow this blog's instructions if you use windows.(https://zhuanlan.zhihu.com/p/22129880)
for training
caffe train -solver=solver.prototxt -gpu 0
for testing
caffe test -model=res20_cifar_train_test.prototxt -weights=ResNet_20.caffemodel -iterations=100 -gpu 0
Result
Result with data augmentation:
model | Repeated | Reference |
---|---|---|
20 lyaers | 91.94% | 91.25% |
32 layers | 92.70% | 92.49% |
44 layers | 93.01% | 92.83% |
56 layers | 93.19% | 93.03% |
110 layers | 93.56% | 93.39% |
notice:'Repeated' means reimplementation results and 'Reference' means result in the paper.We got even better results than the original paper
Compare result(without data augmentation):
model | PlainNet | ResNet |
---|---|---|
20 lyaers | 90.10% | 91.74% |
32 layers | 86.96% | 92.23% |
44 layers | 84.45% | 92.67% |
56 layers | 85.26% | 93.09% |
110 layers | X | 93.27% |