unzip vehicleDatasetImages.zip
data = load("vehicleDatasetGroundTruth.mat");
vehicleDataset = data.vehicleDataset;

rng(0)
shuffledIndices = randperm(height(vehicleDataset));
idx = floor(0.6 * height(vehicleDataset));

trainingIdx = 1:idx;
trainingDataTbl = vehicleDataset(shuffledIndices(trainingIdx),:);

validationIdx = idx+1 : idx + 1 + floor(0.1 * length(shuffledIndices) );
validationDataTbl = vehicleDataset(shuffledIndices(validationIdx),:);

testIdx = validationIdx(end)+1 : length(shuffledIndices);
testDataTbl = vehicleDataset(shuffleIndices(testIdx),:);

imdsTrain = imageDatastore(trainingDatatbl{:,"imageFilename"});
bldsTrain = boxLabelDatastore(trainingDataTbl(:,"vehicle"));

imdsValidation = imageDatastore(validationDataTbl{:,"imageFilename"});
bldsValidation = boxLabelDataStore(validationDatatbl(:,"vehicle"));

imdsTest = imageDatastore(testDataTbl{:"imageFilename"});
bldsTest = boxLabelDatastore(testDataTbl(:,"vehicle"));

// Combine image and box label datastores

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

//Display one of the training images and box labels

data = read(trainingData);
I = data{1};
bbox = data{2};
annotatedImage = insertShape(I"rectangle",bbox);
annotatedImage = imresize(annotatedImage,2);
figure
imshow(annotatedImage)