matconvnet 테스트 이미지에 딥러닝모델 적용하고, 피쳐 뽑아 SVM으로 분류하는 예제

Deep Learning/Matconvnet 2017. 5. 15. 22:18

http://www.ninadthakoor.com/2016/07/25/getting-started-with-matconvnet/

Available at: http://www.vlfeat.org/matconvnet/

My goal is to use this toolbox to classify cars into four classes: Sedan, Minivan, SUV and pickup. I already have the data from my prior work (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6558793).

My plan is to first use transfer learning i. e. to use one of the deep networks pre-trained with imagenet data to extract features and use an SVM classifier to do the actual classification. I plan to do this with Matlab 2016a on Windows 10 PC equipped with GeForce GTX 960. I am aware that Matlab also has deep learning support in its Neural Network toolbox, I am going with MatConNet in hopes that it will stay more cutting edge.

The code below is derived from http://www.mathworks.com/company/newsletters/articles/deep-learning-for-computer-vision-with-matlab.html and http://www.vlfeat.org/matconvnet/quick/

clear; close all; clc;
%Load the pre-trained net
net = load('imagenet-vgg-f.mat');
net = vl_simplenn_tidy(net) ;
 
%Remove the last layer (softmax layer)
net.layers = net.layers(1 : end - 1);
 
%% This deals with reading the data and getting the ground truth class labels
 
%All files are are inside the root 
root = 'C:\Users\ninad\Dropbox\Ninad\Lasagne\data\c200x200\';
Files = dir(fullfile(root, '*.bmp'));
 
%Load the map which stores the class information
load MakeModels.mat
for i = 1 : length(Files)
    waitbar (i/ length(Files));
    % Read class from the map
    Q = Files(i).name(end - 18 : end - 4);
    Qout = MakeModels(Q);
    Files(i).class = Qout.Type;
     
    % Preprocess the data and get it ready for the CNN
    im = imread(fullfile(root, Files(i).name));
    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 to compute the features
    feats = vl_simplenn(net, im_) ;
    Files(i).feats = squeeze(feats(end).x);
end
 
%% Classifier training
 
%Select training data fraction
 
trainFraction = 0.5;
randomsort = randperm(length(Files));
trainSamples = randomsort(1 : trainFraction * length(Files));
testSamples = randomsort(trainFraction * length(Files)+1 : end);
 
Labels = [Files.class];
Features = [Files.feats];
 
trainingFeatures = Features( :, trainSamples);
trainingLabels = Labels( :, trainSamples);
 
classifier = fitcecoc(trainingFeatures', trainingLabels);
 
%% Carry out the validation with rest of the data
testFeatures = Features( :, testSamples);
testLabels = Labels( :, testSamples);
 
predictedLabels = predict(classifier, testFeatures');
 
confMat = confusionmat(testLabels, predictedLabels);
 
% Convert confusion matrix into percentage form
confMat = bsxfun(@rdivide,confMat,sum(confMat,2))
 
% Display the mean accuracy
mean(diag(confMat))