I have a custom dataset with 4 classes and I'm using the example given in MATLAB documentation for YOLov4 object detection. I am not able to obtain the plots of precision.

data=load("train.mat");
defectDataset= data.gTruth;
rng("default");
shuffledIndices = randperm(height(defectDataset));
idx = floor(0.6 * length(shuffledIndices) );

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

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

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

defectDataset.imageFilename = fullfile(pwd,defectDataset.imageFilename);

imdsTrain = imageDatastore(trainingDataTbl{:,"imageFilename"});
bldsTrain = boxLabelDatastore(trainingDataTbl(:,["hole","slub","stain","knot"]));

imdsValidation = imageDatastore(validationDataTbl{:,"imageFilename"});
bldsValidation = boxLabelDatastore(validationDataTbl(:,["hole","slub","stain","knot"]));

imdsTest = imageDatastore(testDataTbl{:,"imageFilename"});
bldsTest = boxLabelDatastore(testDataTbl(:,["hole","slub","stain","knot"]));

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

data = read(trainingData);
I = data{1};
bbox = data{2};
labels = data{3};
annotatedImage = insertObjectAnnotation(I,"Rectangle",bbox,labels);
annotatedImage = imresize(annotatedImage,2);
figure
imshow(annotatedImage)

inputSize = [608 608 3];
className = ["hole", "slub", "stain", "knot"];
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);

augmentedData = cell(4,1);
for k = 1:4
    data = read(augmentedTrainingData);
    augmentedData{k} = insertShape(data{1},"rectangle",data{2});
    reset(augmentedTrainingData);
end
figure
montage(augmentedData,BorderSize=10)
options = trainingOptions("adam",...
    GradientDecayFactor=0.9,...
    SquaredGradientDecayFactor=0.999,...
    InitialLearnRate=0.001,...
    LearnRateSchedule="none",...
    MiniBatchSize=8,...
    L2Regularization=0.0005,...
    MaxEpochs=20,...
    BatchNormalizationStatistics="moving",...
    DispatchInBackground=true,...
    ResetInputNormalization=false,...
    Shuffle="every-epoch",...
    VerboseFrequency=20,...
    ValidationFrequency=1000,...
    CheckpointPath=tempdir,...
    ValidationData=validationData);
doTraining = false;
if doTraining       
    % Train the YOLO v4 detector.
    [detector,info] = trainYOLOv4ObjectDetector(augmentedTrainingData,detector,options);
else
    % Load pretrained detector for the example.
    detector = downloadPretrainedYOLOv4Detector();
end

% Detect objects in the test data using the trained or pretrained detector.
detectionResults = detect(detector,testData);

% Evaluate the detection performance.
[ap,recall,precision] = evaluateDetectionPrecision(detectionResults,testData);

% Plot the precision-recall curve.
figure
plot(recall,precision)
xlabel("Recall")
ylabel("Precision")
grid on
title(sprintf("Average Precision = %.2f",ap))

% Show detection results on test images
resultsDir = fullfile(pwd, 'detection_results');
mkdir(resultsDir);
for i = 1:numel(testDataTbl.imageFilename)
    I = imread(testDataTbl.imageFilename{i});
    I = insertObjectAnnotation(I, 'rectangle', detectionResults(i).Boxes, detectionResults(i).Scores);
    imwrite(I, fullfile(resultsDir, [num2str(i) '.jpg']));
end



function data = preprocessData(data,targetSize)
% Resize the images and scale the pixels to between 0 and 1. Also scale the
% corresponding bounding boxes.

for ii = 1:size(data,1)
    I = data{ii,1};
    imgSize = size(I);
    
    bboxes = data{ii,2};

    I = im2single(imresize(I,targetSize(1:2)));
    scale = targetSize(1:2)./imgSize(1:2);
    bboxes = bboxresize(bboxes,scale);
    
    data(ii,1:2) = {I,bboxes};
end
end



function detector = downloadPretrainedYOLOv4Detector()
% Download a pretrained yolov4 detector.
if ~exist("yolov4CSPDarknet53VehicleExample_22a.mat", "file")
    if ~exist("yolov4CSPDarknet53VehicleExample_22a.zip", "file")
        disp("Downloading pretrained detector...");
        pretrainedURL = "https://ssd.mathworks.com/supportfiles/vision/data/yolov4CSPDarknet53VehicleExample_22a.zip";
        websave("yolov4CSPDarknet53VehicleExample_22a.zip", pretrainedURL);
    end
    unzip("yolov4CSPDarknet53VehicleExample_22a.zip");
end
pretrained = load("yolov4CSPDarknet53VehicleExample_22a.mat");
detector = pretrained.detector;
end

function data = augmentData(A)
% Apply random horizontal flipping, and random X/Y scaling. Boxes that get
% scaled outside the bounds are clipped if the overlap is above 0.25. Also,
% jitter image color.

data = cell(size(A));
for ii = 1:size(A,1)
    I = A{ii,1};
    bboxes = A{ii,2};
    labels = A{ii,3};
    sz = size(I);

    if numel(sz) == 3 && sz(3) == 3
        I = jitterColorHSV(I,...
            contrast=0.0,...
            Hue=0.1,...
            Saturation=0.2,...
            Brightness=0.2);
    end
    
    % Randomly flip image.
    tform = randomAffine2d(XReflection=true,Scale=[1 1.1]);
    rout = affineOutputView(sz,tform,BoundsStyle="centerOutput");
    I = imwarp(I,tform,OutputView=rout);
    
    % Apply same transform to boxes.
    [bboxes,indices] = bboxwarp(bboxes,tform,rout,OverlapThreshold=0.25);
    labels = labels(indices);
    
    % Return original data only when all boxes are removed by warping.
    if isempty(indices)
        data(ii,:) = A(ii,:);
        continue;
    end
    
    data(ii,:) = {I, bboxes, labels};
end
end

Unrecognized field name "gTruth".

Answer 1

[Shubham]

Hi Atishay,

To obtain the precision plot in MATLAB for your custom YOLOv4 object detection model, you can follow these steps:

1. First, you need to train your YOLOv4 model on your custom dataset. You can use the MATLAB example code as a starting point and modify it according to your dataset.
2. After training the model, you can use the evaluateDetectionPrecision function to obtain the precision metrics. This function takes the ground truth data and the detected data as inputs and returns the precision metrics.
3. To obtain the precision plot, you can use the plotDetectionPrecision function. This function takes the output of the evaluateDetectionPrecision function as input and plots the precision metrics.

Here's an example code snippet that shows how to obtain the precision plot:

% Load the ground truth data and detected data
load('groundTruthData.mat');
load('detectedData.mat');

% Evaluate the detection precision
metrics = evaluateDetectionPrecision(detectedData, groundTruthData);

% Plot the precision
plotDetectionPrecision(metrics);

Note that you need to replace groundTruthData.mat and detectedData.mat with the file names of your ground truth and detected data files, respectively.