I am trainng U-Net architecture for semantic segmentation but after training the trained network does not segment the test images.
I am using 500 images (400 training, 50 validation and 50 testing) along with labelled mask to train the network. The training process reaches at 90% validation accuracy after 2nd epoch and remains at same level for rest of the epochs. I tried many possible ways including change in learning rate but it does not work. The images are in JPEG format and labeld mask has png format.
load gTruth % load gTruth file of training data
[imdsTrain,pxdsTrain] = pixelLabelTrainingData(gTruth);
pximdsTrain = pixelLabelImageDatastore(imdsTrain,pxdsTrain);
load gTruth % load gTruth file of Validiation data
[imdsVal,pxdsVal] = pixelLabelTrainingData(gTruth);
pximdsVal = pixelLabelImageDatastore(imdsVal,pxdsVal);
load gTruth % load gTruth file of test data
[imdsTest,pxdsTest] = pixelLabelTrainingData(gTruth)
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise',...
'LearnRateDropPeriod',2,...
'LearnRateDropFactor',0.1,...
'Momentum',0.9, ...
'InitialLearnRate',1e-3, ...
'L2Regularization',0.09, ...
'ValidationData',pximdsVal,...
'MaxEpochs',5, ...
'MiniBatchSize',2, ...
'Shuffle','every-epoch', ...
'VerboseFrequency',50,...
'Plots','training-progress',...
'ValidationFrequency',100);
imageSize = [256 256 1];
numClasses = 2;
Network = unetLayers(imageSize,numClasses)
I repalced cross entropy loss with dice los function.
rng(1) %
Trained_net= trainNetwork(pximdsTrain,Network,options);
