How to plot confusion matrix?

Hello guys, I want to plot confusion matrix after training an object detector ... Here is my code ... How to plot confusion matrix ?

data = load("letisko_labels_new.mat");
LabelData = data.gTruth.LabelData;

LabelData.imageFilename = fullfile(LabelData.imageFilename);

rng("default");
shuffledIndices = randperm(height(LabelData));
idx = floor(0.6 * length(shuffledIndices) );
trainingIdx = 1:idx;
trainingDataTbl = LabelData(shuffledIndices(trainingIdx),:);
validationIdx = idx+1 : idx + 1 + floor(0.1 * length(shuffledIndices) );
validationDataTbl = LabelData(shuffledIndices(validationIdx),:);
testIdx = validationIdx(end)+1 : length(shuffledIndices);
testDataTbl = LabelData(shuffledIndices(testIdx),:);

imdsTrain = imageDatastore(trainingDataTbl{:,"imageFilename"});
bldsTrain = boxLabelDatastore(trainingDataTbl(:,2:6));
imdsValidation = imageDatastore(validationDataTbl{:,"imageFilename"});
bldsValidation = boxLabelDatastore(validationDataTbl(:,2:6));
imdsTest = imageDatastore(testDataTbl{:,"imageFilename"});
bldsTest = boxLabelDatastore(testDataTbl(:,2:6));

trainingData = combine(imdsTrain,bldsTrain);
validationData = combine(imdsValidation,bldsValidation);
testData = combine(imdsTest,bldsTest);

validateInputData(trainingData);
validateInputData(validationData);
validateInputData(testData);

inputSize = [256 256 3];

className = ["kamera","lietadlo","satelit","stlp","veza"];

rng("default")
trainingDataForEstimation = transform(trainingData,@(data)preprocessData(data,inputSize));
numAnchors = 9;
[anchors,meanIoU] = estimateAnchorBoxes(trainingDataForEstimation,numAnchors);
area = anchors(:, 1).*anchors(:,2);
[~,idx] = sort(area,"descend");
anchors = anchors(idx,:);
anchorBoxes = {anchors(1:3,:)
    anchors(4:6,:)
    anchors(7:9,:)
    };

detector = yolov4ObjectDetector("csp-darknet53-coco",className,anchorBoxes,InputSize=inputSize);

augmentedTrainingData = transform(trainingData,@augmentData);

options = trainingOptions("adam",...
    GradientDecayFactor=0.9,...
    SquaredGradientDecayFactor=0.999,...
    InitialLearnRate=0.001,...
    LearnRateSchedule="none",...
    MiniBatchSize=4,...
    L2Regularization=0.0005,...
    MaxEpochs=50,...
    BatchNormalizationStatistics="moving",...
    DispatchInBackground=true,...
    ResetInputNormalization=false,...
    Shuffle="every-epoch",...
    VerboseFrequency=20,...
    ValidationFrequency=1000,...
    Plots="training-progress",...
    CheckpointPath='C:\BAKALARKA\checkpointYOLO',...
    ValidationData=validationData);

doTraining = true;
if doTraining       
    % Train the YOLO v4 detector.
     [detector,info] = trainYOLOv4ObjectDetector(augmentedTrainingData,detector,options);
else
    % Load pretrained detector for the example.
    detector = downloadPretrainedYOLOv4Detector();
end


detectionResults = detect(detector,testData,'MiniBatchSize',4);

[ap,recall,precision] = evaluateDetectionPrecision(detectionResults,testData);

recallv = cell2mat(recall);
precisionv = cell2mat(precision);
[r,index] = sort(recallv);
p = precisionv(index);
figure
plot(r,p)
xlabel("Recall")
ylabel("Precision")
grid on
title(sprintf("Average Precision = %.2f",mean(ap)))

Answer 1

[Venkat Siddarth]

Hi @Adrian Kleffler

I understand that you are looking to plot confusion matrix for the model. Here I am assuming that you want to plot the confusion matrix for the labels column in detectionResults,which can be achieved by using a function called confusionmat. This function takes two vectors as inputs, the true labels and the predicted labels and produces a confusion matrix.

y_true=[1 0 1 1 1 1 0 0];
y_pred=[1 1 1 1 0 0 1 1];
C=confusionmat(y_true,y_pred)

C = 2×2

0 3 2 3

After generating the confusion matrix you can plot the confusion matrix using the function confusionchart

confusionchart(C)

To know more about these functions, check out the following documentation

https://www.mathworks.com/help/stats/confusionmat.html

I hope this resolves the issue,

Regards

Venkat Siddarth V.