Hello,
I am working on training a CNN model for pavement distress identification using image classification. I have three categories for defects (Alligator Cracks, Potholes and Rutting). When I run my model it only predicts a single class out of three rendering my model accuracy to 33.33%. I tried changing the training parameters/ options but the result is same. I was hoping if someone can help me figure out why. The code I am using is given below. Thanks in advance.
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rootFolder = fullfile ('CNN Input');
categories = {'Alligator Cracks','Potholes','Rutting'};
imds = imageDatastore (fullfile (rootFolder,categories), 'LabelSource', 'Foldernames');
tbl = countEachLabel (imds);
minSetCount = min(tbl{:,2});
countEachLabel(imds);
AlligatorCracks = find(imds.Labels == 'Alligator Cracks',1);
Potholes = find(imds.Labels == 'Potholes',1);
Rutting = find(imds.Labels == 'Rutting',1);
net = alexnet ();
rng ('default');
net.Layers(1);
net.Layers(end);
numel(net.Layers(end).ClassNames);
[trainingSet, testSet, validationSet] = splitEachLabel(imds,0.75,0.15,'randomize');
imageSize = net.Layers(1).InputSize;
augmentedTrainingSet = augmentedImageDatastore(imageSize,trainingSet,'ColorPreprocessing','gray2rgb');
augmentedTestSet = augmentedImageDatastore(imageSize,testSet,'ColorPreprocessing','gray2rgb');
augmentedValidationSet = augmentedImageDatastore(imageSize,validationSet,'ColorPreprocessing','gray2rgb');
w1 = net.Layers(2).Weights;
w1 = mat2gray(w1);
featurelayer = 'drop7';
trainingFeatures = activations(net, augmentedTrainingSet, featurelayer,'MiniBatchSize', 32,'OutputAs', 'columns');
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',0.2,...
'learnRateDropPeriod',1,...
'MaxEpochs',8,...
'MiniBatchSize',16,...
'Plots','training-progress',...
'Shuffle','once');
numClasses = 3;
layersTransfer = net.Layers(1:end-3);
layers = [
layersTransfer
fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20)
softmaxLayer
classificationLayer];
trainedNet = trainNetwork(augmentedTrainingSet,layers,options);
trainingLabels = trainingSet.Labels;
classifier = fitcecoc(trainingFeatures, trainingLabels, 'Learner', 'Linear', 'Coding', 'onevsall','observationsIn','columns');
testFeatures = activations(trainedNet,augmentedTestSet, featurelayer,'MiniBatchSize', 32,'OutputAs', 'columns');
validationFeatures = activations(trainedNet,augmentedValidationSet, featurelayer,'MiniBatchSize', 32,'OutputAs', 'columns');
predictLabels = predict(classifier,testFeatures,'ObservationsIn','columns');
predictLabelss = predict(classifier,validationFeatures,'ObservationsIn','columns');
testLables = testSet.Labels;
validationLables = validationSet.Labels;
confMat = confusionmat(testLables, predictLabels);
confMatt = confusionmat(validationLables, predictLabelss);
confMat = bsxfun(@rdivide, confMat, sum(confMat,2));
confMatt = bsxfun(@rdivide, confMatt, sum(confMatt,2));
mean(diag(confMat));
mean(diag(confMatt));
