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/