You can do this with the semanticseg function. Because the prediction results for semantic segmentation can be very large, the semanticseg function will write the outputs to disk. Here is a short example.
% Load a pretrained segmentation network.
data = load('triangleSegmentationNetwork');
net = data.net;
% Specify the location of the test data and load the images.
dataDir = fullfile(toolboxdir('vision'),'visiondata','triangleImages');
testImageDir = fullfile(dataDir,'testImages');
imds = imageDatastore(testImageDir);
% Predict on the test data. The results are returned as a datastore with
% image files stored in a temporary directory.
pxdsResults = semanticseg( imds, net, ...
'MiniBatchSize', 4, ...
'WriteLocation', tempdir );
If you have a the ground truth labels as well, you can use semanticseg with evaluateSemanticSegmentation to compute metrics. For example, you can run this code after the previous code:
% Load the labels for the data. Note that class names and pixel labels
% depend on your data.
testLabelDir = fullfile(dataDir,'testLabels');
classNames = ["triangle" "background"];
pixelLabelID = [255 0];
pxdsTruth = pixelLabelDatastore(testLabelDir,classNames,pixelLabelID);
% Compute metrics on the predictions using the ground truth.
metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth);
