文件列表:

FSRCNN\data_aug.m (727, 2016-09-09)
FSRCNN\FSRCNN-s_mat.prototxt (2565, 2016-09-09)
FSRCNN\FSRCNN-s_net.prototxt (2791, 2016-09-06)
FSRCNN\FSRCNN-s_solver.prototxt (577, 2016-09-09)
FSRCNN\FSRCNN_mat.prototxt (4052, 2016-09-09)
FSRCNN\FSRCNN_net.prototxt (4278, 2016-09-06)
FSRCNN\FSRCNN_solver.prototxt (578, 2016-09-09)
FSRCNN\generate_test.m (1962, 2016-09-09)
FSRCNN\generate_train.m (2040, 2016-09-09)
FSRCNN\modcrop.m (279, 2015-03-17)
FSRCNN\plot_filterpattern.m (841, 2016-09-09)
FSRCNN\saveFilters.m (2391, 2016-09-09)
FSRCNN\store2hdf5.m (2897, 2015-06-05)
FSRCNN\test.txt (24, 2016-09-06)
FSRCNN\train.txt (25, 2016-09-06)
FSRCNN (0, 2018-11-02)

*********************************************************************************************************** Training code for "Accelerating the Super-Resolution Convolutional Neural Networks" (ECCV 2016) by Chao Dong (dongchao@sensetime.com) *********************************************************************************************************** Usage: 1. Place the "FSRCNN" folder into "($Caffe_Dir)/examples/" 2. Open MATLAB and direct to ($Caffe_Dir)/example/FSRCNN, run "generate_train.m" and "generate_test.m" to generate training and test data. You can also run data_aug.m to do data augmentation first. 3. To train our FSRCNN, run ./build/tools/caffe train --solver examples/FSRCNN/FSRCNN_solver.prototxt (For FSRCNN) or ./build/tools/caffe train --solver examples/FSRCNN/FSRCNN-s_solver.prototxt (For FSRCNN-s) 4. After training, you can extract parameters from the caffe model and save them in the format that can be used in our test package (FSRCNN_test). To do this, you need to install mat-caffe first, then open MATLAB and direct to ($Caffe_Dir) and run "saveFilters.m". The "($Caffe_Dir)/examples/FSRCNN/FSRCNN.mat" will be there for you.

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