:x: I have stopped maintaining this repo. For fine-tuning ResNet, I would suggest using Torch version from Facebook repo.
This repository is a Matconvnet re-implementation of "Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. You can train Deep Residual Network on ImageNet from Scratch or fine-tune pre-trained model on your own dataset. This repo is created by Hang Zhang.
The code relies on vlfeat, and matconvnet, which should be downloaded and built before running the experiments. You can use the following commend to download them.
git clone -b v1.0 --recurse-submodules https://github.com/zhanghang1989/ResNet-Matconvnet.git
If you have problem with compiling, please refer to the link.
Cifar. Reproducing Figure 6 from the original paper.
run_cifar_experiments([20 32 44 56 110], 'plain', 'gpus', [1]);
run_cifar_experiments([20 32 44 56 110], 'resnet', 'gpus', [1]);
Cifar Experiments
Reproducing the experiments in Facebook blog. Removing ReLU layer at the end of each residual unit, we observe a small but significant improvement in test performance and the converging progress becomes smoother.
res_cifar(20, 'modelType', 'resnet', 'reLUafterSum', false,...
'expDir', 'data/exp/cifar-resNOrelu-20', 'gpus', [2])
plot_results_mix('data/exp','cifar',[],[],'plots',{'resnet','resNOrelu'})
Imagenet2012. download the dataset to data/ILSVRC2012
and follow the instructions in setup_imdb_imagenet.m
.
run_experiments([50 101 152], 'gpus', [1 2 3 4 5 6 7 8]);
Your own dataset.
run_experiments([18 34],'datasetName', 'minc',...
'datafn', @setup_imdb_minc, 'nClasses', 23, 'gpus', [1 2]);
Download
data/models
: imagenet-resnet-50-dag
, imagenet-resnet-101-dag
, imagenet-resnet-152-dag data/
: Material in Context Database (minc)Fine-tuning
res_finetune('datasetName', 'minc', 'datafn',...
@setup_imdb_minc, 'gpus',[1 2]);
06/21/2016:
05/17/2016:
05/02/2016:
04/27/2016: Re-implementation of Residual Network: