README.md

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" (https://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%

Blog address

zhihu