Matlab Error using semanticSe​gmentation​Metrics The categorical data returned by dsResults and dsTruth must have the same categories.

I used the matlab documentation to evaluate my results from a previous trained networkbut I get this error, can someone fix it and get it to work and output results please. What I specifically need is to evaluate segmentation results from the trained network and save the image results from segmentation to add to my report. Here's the code I used Evaluating semantic segmentation results ---------------------------------------- * Selected metrics: global accuracy, class accuracy, IoU, weighted IoU, BF score. * Processed 0 [login to view URL] 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('[login to view URL]'); net = [login to view URL]; pxdsResults = semanticseg(imds,net,"WriteLocation",tempdir); metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth); [login to view URL] [login to view URL] cm = confusionchart([login to view URL], ... classNames, Normalization ='row-normalized'); [login to view URL] = 'Normalized Confusion Matrix (%)'; imageIoU = [login to view URL]; 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); [login to view URL]