create a semantic segmentation algorithm using net = inceptionresnetv2 with imdstest = imagedatastore() and pxdstest = pixellabeldatastore() with image size 2242243 in matlab

To create a semantic segmentation algorithm using inceptionresnetv2, imagedatastore(), and pixellabeldatastore() with an image size of 224*224*3, follow these steps:

Load the Inception-ResNet-v2 network using the inceptionresnetv2 function.


main.m
net = inceptionresnetv2;
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Define the input image size and number of classes for semantic segmentation. In this case, we'll use 224x224 resolution and 3 classes: background, foreground, and unknown.


main.m
imageSize = [224 224 3];
numClasses = 3;
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Initialize the training data source using imagedatastore() and pixellabeldatastore().


main.m
imdsTrain = imagedatastore('path/to/training/images');
pxdsTrain = pixellabeldatastore('path/to/training/labels', classes);
imdsTest = imagedatastore('path/to/testing/images');
pxdsTest = pixellabeldatastore('path/to/testing/labels', classes);
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Create a semantic segmentation network using deeplabv3plusLayers() specifying the input and number of classes. Then configure the training options.


main.m
lgraph = deeplabv3plusLayers(imageSize,numClasses,net);
options = trainingOptions('sgdm', ...
                          'Momentum',0.9, ...
                          'InitialLearnRate',1e-3, ...
                          'L2Regularization',1e-3, ...
                          'MaxEpochs',15, ...
                          'MiniBatchSize',8, ...
                          'Shuffle','every-epoch', ...
                          'Verbose',false, ...
                          'Plots','training-progress');
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Train the semantic segmentation network using trainNetwork().


main.m
net = trainNetwork(imdsTrain,pxdsTrain,lgraph,options);
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Evaluate the trained network using semanticseg() and evaluateSemanticSegmentation().


main.m
pxdsResults = semanticseg(imdsTest,net);
metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTest);
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View the results of the evaluation and visualize the semantic segmentation output.


main.m
disp(metrics);
I = read(imdsTest);
C = read(pxdsResults);
B = labeloverlay(I,C,'Colormap',cmap,'Transparency',0.4);
imshowpair(I,B,'montage');
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Make sure to replace 'path/to/training/images', 'path/to/training/labels', 'path/to/testing/images', and 'path/to/testing/labels' with the corresponding paths to your own data. Also, define the classes variable in step 3 based on your own label classes.