Evaluating semantic segmentation results error

I used the documentation to evaluate my results but I get this error, can someone tell me what's wrong please , my input is a .mat file

Evaluating semantic segmentation results

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* Selected metrics: global accuracy, class accuracy, IoU, weighted IoU, BF score.

* Processed 0 images.Error using semanticSegmentationMetrics>iAssertCategoricalsHaveSameCategories

The categorical data returned by dsResults and dsTruth must have the same

categories.

my code is below

dataSetDir = fullfile('LungTS','preprocessedDataset');

testImagesDir = fullfile(dataSetDir,'imagesTest');

imdReader = @(x) matRead(x);

imds = imageDatastore(testImagesDir, ...

'FileExtensions','.mat','ReadFcn',imdReader);

%imds = imageDatastore(testImagesDir);

classNames = ["nodule" "background"];

labelIDs = [255 0];

labelReader = @(x) matRead(x);

testLabelsDir = fullfile(dataSetDir,'labelsTest');

pxdsTruth = pixelLabelDatastore(testLabelsDir,classNames,labelIDs, ...

'FileExtensions','.mat','ReadFcn',labelReader);

net = load('trained3DUNet-16-Sep-2022-14-34-13-Epoch-250.mat');

net = net.net;

pxdsResults = semanticseg(imds,net,"WriteLocation",tempdir);

metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth);

metrics.ClassMetrics

metrics.ConfusionMatrix

cm = confusionchart(metrics.ConfusionMatrix.Variables, ...

classNames, Normalization ='row-normalized');

cm.Title = 'Normalized Confusion Matrix (%)';

imageIoU = metrics.ImageMetrics.MeanIoU;

figure

histogram(imageIoU)

title('Image Mean IoU')

[minIoU, worstImageIndex] = min(imageIoU);

minIoU = minIoU(1);

worstImageIndex = worstImageIndex(1);

worstTestImage = readimage(imds,worstImageIndex);

worstTrueLabels = readimage(pxdsTruth,worstImageIndex);

worstPredictedLabels = readimage(pxdsResults,worstImageIndex);

worstTrueLabelImage = im2uint8(worstTrueLabels == classNames(1));

worstPredictedLabelImage = im2uint8(worstPredictedLabels == classNames(1));

worstMontage = cat(4,worstTestImage,worstTrueLabelImage,worstPredictedLabelImage);

worstMontage = imresize(worstMontage,4,"nearest");

figure

montage(worstMontage,'Size',[1 3])

title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(minIoU)])

[minIoU, worstImageIndex] = min(imageIoU);

minIoU = minIoU(1);

worstImageIndex = worstImageIndex(1);

worstTestImage = readimage(imds,worstImageIndex);

worstTrueLabels = readimage(pxdsTruth,worstImageIndex);

worstPredictedLabels = readimage(pxdsResults,worstImageIndex);

worstTrueLabelImage = im2uint8(worstTrueLabels == classNames(1));

worstPredictedLabelImage = im2uint8(worstPredictedLabels == classNames(1));

worstMontage = cat(4,worstTestImage,worstTrueLabelImage,worstPredictedLabelImage);

worstMontage = imresize(worstMontage,4,"nearest");

figure

montage(worstMontage,'Size',[1 3])

title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(minIoU)])

[maxIoU, bestImageIndex] = max(imageIoU);

maxIoU = maxIoU(1);

bestImageIndex = bestImageIndex(1);

bestTestImage = readimage(imds,bestImageIndex);

bestTrueLabels = readimage(pxdsTruth,bestImageIndex);

bestPredictedLabels = readimage(pxdsResults,bestImageIndex);

bestTrueLabelImage = im2uint8(bestTrueLabels == classNames(1));

bestPredictedLabelImage = im2uint8(bestPredictedLabels == classNames(1));

bestMontage = cat(4,bestTestImage,bestTrueLabelImage,bestPredictedLabelImage);

bestMontage = imresize(bestMontage,4,"nearest");

figure

montage(bestMontage,'Size',[1 3])

title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(maxIoU)])

evaluationMetrics = ["accuracy" "iou"];

metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth,"Metrics",evaluationMetrics);

metrics.ClassMetrics