Awesome
MemNet
[Paper]
Citation
If you find MemNet useful in your research, please consider citing:
@inproceedings{Tai-MemNet-2017,
title={MemNet: A Persistent Memory Network for Image Restoration},
author={Tai, Ying and Yang, Jian and Liu, Xiaoming and Xu, Chunyan},
booktitle={Proceedings of International Conference on Computer Vision},
year={2017}
}
Other implementation
[MemNet-tensorflow] by ly-atdawn
[MemNet-pytorch] by Vandermode
Implement adjustable gradient clipping
modify sgd_solver.cpp in your_caffe_root/src/caffe/solvers/, where we add the following codes in funciton ClipGradients():
Dtype rate = GetLearningRate();
const Dtype clip_gradients = this->param_.clip_gradients()/rate;
Training (Taking Super-resolution task as the example)
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Preparing training/validation data using the files: generate_trainingset_x234/generate_testingset_x234 in "data/SuperResolution" folder. "Train_291" folder contains 291 training images and "Set5" folder is a popular benchmark dataset.
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We release two MemNet architectures: MemNet_M6R6_80C64 and MemNet_M10R10_212C64 in "caffe_files" folder. Choose either one to do training.
$ cd ./caffe_files/MemNet_M6R6_80C64 $ ./train_MemNet_M6R6_80C64.sh
Test (Taking Super-resolution task as the example)
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Remember to compile the matlab wrapper: make matcaffe, since we use matlab to do testing.
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We release two pretrained models: MemNet_M6R6_80C64 and MemNet_M10R10_212C64 in "model" folder. Choose either one to do testing on benchmark Set5.
$ cd ./results/MemNet_M6R6_80C64 $ matlab >> test_MemNet_M6R6_SR
The results are stored in "results" folder, with both reconstructed images and PSNR/SSIMs.