Matconvnet工具箱在Matlab中的安装

安装

 

• 编译前确保Matab已绑定C++编译器，否则使用命令>>mex -setup 进行绑定编译器。

• 将Matalb的工作路径切换到Matconvnet目录下，../matconvnet-1.0-beta23。

• 编译工具箱，>>run matlab/vl_compilenn ;

• 安装工具箱，>>run matlab/vl_setupnn ;

 

测试

 

• 在Matlab工作空间输入一下代码，并运行；成功显示图片说明安装成功。

 

 

% Download a pre-trained CNN from the web (needed once).
urlwrite(\'http://www.vlfeat.org/matconvnet/models/imagenet-vgg-f.mat\',\'imagenet-vgg-f.mat\') ;
% Load a model and upgrade it to MatConvNet current version.
net = load(\'imagenet-vgg-f.mat\') ;
net = vl_simplenn_tidy(net) ;
% Obtain and preprocess an image.
im = imread(\'peppers.png\') ;
im_ = single(im) ; % note: 255 range
im_ = imresize(im_, net.meta.normalization.imageSize(1:2)) ;
im_ = im_ - net.meta.normalization.averageImage ;
% Run the CNN.
res = vl_simplenn(net, im_) ;
% Show the classification result.
scores = squeeze(gather(res(end).x)) ;
[bestScore, best] = max(scores) ;figure(1) ; clf ; imagesc(im) ;
title(sprintf(\'%s (%d), score %.3f\', net.meta.classes.description{best}, best, bestScore)) ;

 

 

Using DAG models

The example above exemplifies using a model using the SimpleNN wrapper. More complex models use the DagNN wrapper instead. For example, to run GoogLeNet use:

% setup MatConvNet
run  matlab/vl_setupnn

% download a pre-trained CNN from the web (needed once)
urlwrite(...
  \'http://www.vlfeat.org/matconvnet/models/imagenet-googlenet-dag.mat\', ...
  \'imagenet-googlenet-dag.mat\') ;

% load the pre-trained CNN
net = dagnn.DagNN.loadobj(load(\'imagenet-googlenet-dag.mat\')) ;
net.mode = \'test\' ;

% load and preprocess an image
im = imread(\'peppers.png\') ;
im_ = single(im) ; % note: 0-255 range
im_ = imresize(im_, net.meta.normalization.imageSize(1:2)) ;
im_ = bsxfun(@minus, im_, net.meta.normalization.averageImage) ;

% run the CNN
net.eval({\'data\', im_}) ;

% obtain the CNN otuput
scores = net.vars(net.getVarIndex(\'prob\')).value ;
scores = squeeze(gather(scores)) ;

% show the classification results
[bestScore, best] = max(scores) ;
figure(1) ; clf ; imagesc(im) ;
title(sprintf(\'%s (%d), score %.3f\',...
net.meta.classes.description{best}, best, bestScore)) ;

http://www.vlfeat.org/matconvnet/quick/