Ubuntu15.04 + Matlab2014a + MatConvNet install and compile

最新推荐文章于 2022-04-15 21:33:23 发布

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最新推荐文章于 2022-04-15 21:33:23 发布

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分类专栏： Machine Learning Convenet Ubuntu 文章标签： Ubuntu15.04 Convnet

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本文介绍了MatConvNet工具箱的下载与配置过程，并演示了如何利用预训练模型进行图像分类。MatConvNet是一个用于计算机视觉应用的MATLAB工具箱，能够运行和学习先进的卷积神经网络。

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MatConvNet is a MATLAB toolbox implementingConvolutional NeuralNetworks (CNNs) for computer vision applications. It is simple,efficient, and can run and learn state-of-the-art CNNs. Severalexample CNNs are included to classify and encode images.

1. Download MatConvNet

https://github.com/vlfeat/matconvnet

2. Make sure that MATLAB is configured to use your compiler.

3. Compiling the CPU support

Open MATLAB and issue the commands:

> cd <MatConvNet>
> addpath matlab
> vl_compilenn

At this point MatConvNet should start compiling. If all goes well, youare ready to use the library. If not, you can try debugging theproblem by running the complation script again in verbose mode:

> vl_compilenn('verbose', 1)

4. Compiling the GPU support

Assuming that there is only a single copy of the CUDA toolkitinstalled in your system and that it matches MATLAB's version, compilethe library with:

> vl_compilenn('enableGpu', true)

If you have multiple versions of the CUDA toolkit, or if the scriptcannot find the toolkit for any reason, specify the path to the CUDAtoolkit explicitly. For example, on a Mac this may look like:

> vl_compilenn('enableGpu', true, 'cudaRoot', '/Developer/NVIDIA/CUDA-6.0')

5. Examples

% setup MtConvNet in MATLAB
run matlab/vl_setupnn

% download a pre-trained CNN from the web
urlwrite('http://www.vlfeat.org/sandbox-matconvnet/models/imagenet-vgg-f.mat', ...
'imagenet-vgg-f.mat') ;
net = load('imagenet-vgg-f.mat') ;

% obtain and preprocess an image
im = imread('peppers.png') ;
im_ = single(im) ; % note: 255 range
im_ = imresize(im_, net.normalization.imageSize(1:2)) ;
im_ = im_ - net.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.classes.description{best}, best, bestScore)) ;