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Object Detection Using YOLO v3 Deep Learning

This example shows how to train a YOLO v3 object detector.

Deep learning is a powerful machine learning technique that you can use to train robust object detectors. Several techniques for object detection exist, including Faster R-CNN, you only look once (YOLO) v2, and single shot detector (SSD). This example shows how to train a YOLO v3 object detector. YOLO v3 improves upon YOLO v2 by adding detection at multiple scales to help detect smaller objects. Moreover, the loss function used for training is separated into mean squared error for bounding box regression and binary cross-entropy for object classification to help improve detection accuracy.

Download Pretrained Network

Download a pretrained network using the helper function downloadPretrainedYOLOv3Detector to avoid having to wait for training to complete. If you want to train the network, set the doTraining variable to true.

doTraining = false;

if ~doTraining
    net = downloadPretrainedYOLOv3Detector();    

Load Data

This example uses a small labeled data set that contains 295 images. Each image contains one or two labeled instances of a vehicle. A small data set is useful for exploring the YOLO v3 training procedure, but in practice, more labeled images are needed to train a robust network.

Unzip the vehicle images and load the vehicle ground truth data.

data = load('vehicleDatasetGroundTruth.mat');
vehicleDataset = data.vehicleDataset;

% Add the full path to the local vehicle data folder.
vehicleDataset.imageFilename = fullfile(pwd, vehicleDataset.imageFilename);

Note: In case of multiple classes, the data can also organized as three columns where the first column contains the image file names with paths, the second column contains the bounding boxes and the third column must be a cell vector that contains the label names corresponding to each bounding box. For more information on how to arrange the bounding boxes and labels, see boxLabelDatastore.

All the bounding boxes must be in the form [x y width height]. This vector specifies the upper left corner and the size of the bounding box in pixels.

Split the data set into a training set for training the network, and a test set for evaluating the network. Use 90% of the data for training set and the rest for the test set.

shuffledIndices = randperm(height(vehicleDataset));
idx = floor(0.6 * length(shuffledIndices));
trainingDataTbl = vehicleDataset(shuffledIndices(1:idx), :);
testDataTbl = vehicleDataset(shuffledIndices(idx+1:end), :);

Create an image datastore for loading the images.

imdsTrain = imageDatastore(trainingDataTbl.imageFilename);
imdsTest = imageDatastore(testDataTbl.imageFilename);

Create a datastore for the ground truth bounding boxes.

bldsTrain = boxLabelDatastore(trainingDataTbl(:, 2:end));
bldsTest = boxLabelDatastore(testDataTbl(:, 2:end));

Combine the image and box label datastores.

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

Use validateInputData to detect invalid images, bounding boxes or labels i.e.,

  • Samples with invalid image format or containing NaNs

  • Bounding boxes containing zeros/NaNs/Infs/empty

  • Missing/non-categorical labels.

The values of the bounding boxes should be finite, positive, non-fractional, non-NaN and should be within the image boundary with a positive height and width. Any invalid samples must either be discarded or fixed for proper training.


Data Augmentation

Data augmentation is used to improve network accuracy by randomly transforming the original data during training. By using data augmentation, you can add more variety to the training data without actually having to increase the number of labeled training samples.

Use transform function to apply custom data augmentations to the training data. The augmentData helper function, listed at the end of the example, applies the following augmentations to the input data.

  • Color jitter augmentation in HSV space

  • Random horizontal flip

  • Random scaling by 10 percent

augmentedTrainingData = transform(trainingData, @augmentData);

Read the same image four times and display the augmented training data.

% Visualize the augmented images.
augmentedData = cell(4,1);
for k = 1:4
    data = read(augmentedTrainingData);
    augmentedData{k} = insertShape(data{1,1}, 'Rectangle', data{1,2});
montage(augmentedData, 'BorderSize', 10)

Preprocess Training Data

Specify the network input size. When choosing the network input size, consider the minimum size required to run the network itself, the size of the training images, and the computational cost incurred by processing data at the selected size. When feasible, choose a network input size that is close to the size of the training image and larger than the input size required for the network. To reduce the computational cost of running the example, specify a network input size of [227 227 3].

networkInputSize = [227 227 3];

Preprocess the augmented training data to prepare for training. The preprocessData helper function, listed at the end of the example, applies the following preprocessing operations to the input data.

  • Resize the images to the network input size

  • Scale the image pixels in the range [0 1].

preprocessedTrainingData = transform(augmentedTrainingData, @(data)preprocessData(data, networkInputSize));

Read the preprocessed training data.

data = read(preprocessedTrainingData);

Display the image with the bounding boxes.

I = data{1,1};
bbox = data{1,2};
annotatedImage = insertShape(I, 'Rectangle', bbox);
annotatedImage = imresize(annotatedImage,2);

Reset the datastore.


Define YOLO v3 Network

The YOLO v3 network in this example is based on SqueezeNet, and uses the feature extraction network in SqueezeNet with the addition of two detection heads at the end. The second detection head is twice the size of the first detection head, so it is better able to detect small objects. Note that you can specify any number of detection heads of different sizes based on the size of the objects that you want to detect. The YOLO v3 network uses anchor boxes estimated using training data to have better initial priors corresponding to the type of data set and to help the network learn to predict the boxes accurately. For information about anchor boxes, see Anchor Boxes for Object Detection.

The YOLO v3 network in this example is illustrated in the following diagram.

You can use Deep Network Designer (Deep Learning Toolbox) to create the network shown in the diagram.

First, use transform to preprocess the training data for computing the anchor boxes, as the training images used in this example are bigger than 227-by-227 and vary in size. Specify the number of anchors as 6 to achieve a good tradeoff between number of anchors and mean IoU. Use the estimateAnchorBoxes function to estimate the anchor boxes. For details on estimating anchor boxes, see Estimate Anchor Boxes From Training Data. In case of using a pretrained YOLOv3 object detector, the anchor boxes calculated on that particular training dataset need to be specified. Note that the estimation process is not deterministic. To prevent the estimated anchor boxes from changing while tuning other hyperparameters set the random seed prior to estimation using rng.

trainingDataForEstimation = transform(trainingData, @(data)preprocessData(data, networkInputSize));
numAnchors = 6;
[anchorBoxes, meanIoU] = estimateAnchorBoxes(trainingDataForEstimation, numAnchors)
anchorBoxes = 6×2

    41    34
   163   130
    98    93
   144   125
    33    24
    69    66

meanIoU = 0.8507

Specify anchorBoxMasks to select anchor boxes to use in both the detection heads. anchorBoxMasks is a cell array of [Mx1], where M denotes the number of detection heads. Each detection head consists of a [1xN] array of row index of anchors in anchorBoxes, where N is the number of anchor boxes to use. Select anchor boxes for each detection head based on size—use larger anchor boxes at lower scale and smaller anchor boxes at higher scale. To do so, sort the anchor boxes with the larger anchor boxes first and assign the first three to the first detection head and the next three to the second detection head.

area = anchorBoxes(:, 1).*anchorBoxes(:, 2);
[~, idx] = sort(area, 'descend');
anchorBoxes = anchorBoxes(idx, :);
anchorBoxMasks = {[1,2,3]

Load the SqueezeNet network pretrained on Imagenet data set. You can also choose to load a different pretrained network such as MobileNet-v2 or ResNet-18. YOLO v3 performs better and trains faster when you use a pretrained network.

Next, create the feature extraction network. Choosing the optimal feature extraction layer requires trial and error, and you can use analyzeNetwork to find the names of potential feature extraction layers within a network. For this example, use the squeezenetFeatureExtractor helper function, listed at the end of this example, to remove the layers after the feature extraction layer 'fire9-concat'. The layers after this layer are specific to classification tasks and do not help with object detection.

baseNetwork = squeezenet;
lgraph = squeezenetFeatureExtractor(baseNetwork, networkInputSize);

Specify the names of the object classes, number of object classes to detect, and number of prediction elements per anchor box. The number of predictions per anchor box is set to 5 plus the number of object classes. "5" denoted the 4 bounding box attributes and 1 object confidence. If you use a pretrained YOLOv3 network, specify the class names in the same order they are specified for training the network.

classNames = {'vehicle'};
numClasses = size(classNames, 2);
numPredictorsPerAnchor = 5 + numClasses;

Add the detection heads to the feature extraction network. Each detection head predicts the bounding box coordinates (x, y, width, height), object confidence, and class probabilities for the respective anchor box masks. Therefore, for each detection head, the number of output filters in the last convolution layer is the number of anchor box mask times the number of prediction elements per anchor box. Use the supporting functions addFirstDetectionHead and addSecondDetectionHead to add the detection heads to the feature extraction network.

lgraph = addFirstDetectionHead(lgraph, anchorBoxMasks{1}, numPredictorsPerAnchor);
lgraph = addSecondDetectionHead(lgraph, anchorBoxMasks{2}, numPredictorsPerAnchor);

Finally, connect the detection heads by connecting the first detection head to the feature extraction layer and the second detection head to the output of the first detection head. In addition, merge the upsampled features in the second detection head with features from the 'fire5-concat' layer to get more meaningful semantic information in the second detection head.

lgraph = connectLayers(lgraph, 'fire9-concat', 'conv1Detection1');
lgraph = connectLayers(lgraph, 'relu1Detection1', 'upsample1Detection2');
lgraph = connectLayers(lgraph, 'fire5-concat', 'depthConcat1Detection2/in2');

The detection heads comprise the output layer of the network. To extract output features, specify the names of detection heads using an array of form [Mx1]. M is the number of detection heads. Specify the names of detection heads in the order in which it occurs in the network.

networkOutputs = ["conv2Detection1"

Alternatively, instead of the network created above using SqueezeNet, other pretrained YOLOv3 architectures trained using larger datasets like MS-COCO can be used to transfer learn the detector on custom object detection task. Transfer learning can be realized either by changing the value of number of filters of the last convolution layer or by creating new detection heads as described above, where in the latter case refer to the squeezenetFeatureExtractor for extracting the relevant layers. Transfer learning workflow is recommended if the class of the custom object detection is present either as one of the class or subclass of classes trained in the pretrained network.

Specify Training Options

Specify these training options.

  • Set the number of epochs to be 70.

  • Set the mini batch size as 8. Stable training can be possible with higher learning rates when higher mini batch size is used. Although, this should be set depending on the available memory.

  • Set the learning rate to 0.001.

  • Set the warmup period as 1000 iterations. This parameter denotes the number of iterations to increase the learning rate exponentially based on the formula learningRate×(iterationwarmupPeriod)4. It helps in stabilizing the gradients at higher learning rates.

  • Set the L2 regularization factor to 0.0005.

  • Specify the penalty threshold as 0.5. Detections that overlap less than 0.5 with the ground truth are penalized.

  • Initialize the velocity of gradient as []. This is used by SGDM to store the velocity of gradients.

numEpochs = 70;
miniBatchSize = 8;
learningRate = 0.001;
warmupPeriod = 1000;
l2Regularization = 0.0005;
penaltyThreshold = 0.5;
velocity = [];

Train Model

Train on a GPU, if one is available. Using a GPU requires Parallel Computing Toolbox™ and a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher.

Use the minibatchqueue function to split the preprocessed training data into batches with the supporting function createBatchData which returns the batched images and bounding boxes combined with the respective class IDs. For faster extraction of the batch data for training, dispatchInBackground should be set to "true" which ensures the usage of parallel pool.

minibatchqueue automatically detects the availability of a GPU. If you do not have a GPU, or do not want to use one for training, set the OutputEnvironment parameter to "cpu".

if canUseParallelPool
   dispatchInBackground = true;
   dispatchInBackground = false;

mbqTrain = minibatchqueue(preprocessedTrainingData, 2,...
        "MiniBatchSize", miniBatchSize,...
        "MiniBatchFcn", @(images, boxes, labels) createBatchData(images, boxes, labels, classNames), ...
        "MiniBatchFormat", ["SSCB", ""],...
        "DispatchInBackground", dispatchInBackground,...
        "OutputCast", ["", "double"]);

To train the network with a custom training loop and enable automatic differentiation, convert the layer graph to a dlnetwork object. Then create the training progress plotter using supporting function configureTrainingProgressPlotter.

Finally, specify the custom training loop. For each iteration:

  • Read data from the minibatchqueue. If it doesn't have any more data, reset the minibatchqueue and shuffle.

  • Evaluate the model gradients using dlfeval and the modelGradients function. The function modelGradients, listed as a supporting function, returns the gradients of the loss with respect to the learnable parameters in net, the corresponding mini-batch loss, and the state of the current batch.

  • Apply a weight decay factor to the gradients to regularization for more robust training.

  • Determine the learning rate based on the iterations using the piecewiseLearningRateWithWarmup supporting function.

  • Update the network parameters using the sgdmupdate function.

  • Update the state parameters of net with the moving average.

  • Display the learning rate, total loss, and the individual losses (box loss, object loss and class loss) for every iteration. These can be used to interpret how the respective losses are changing in each iteration. For example, a sudden spike in the box loss after few iterations implies that there are Inf or NaNs in the predictions.

  • Update the training progress plot.

The training can also be terminated if the loss has saturated for few epochs.

if doTraining
    % Convert layer graph to dlnetwork.
    net = dlnetwork(lgraph);
    % Create subplots for the learning rate and mini-batch loss.
    fig = figure;
    [lossPlotter, learningRatePlotter] = configureTrainingProgressPlotter(fig);

    iteration = 0;
    % Custom training loop.
    for epoch = 1:numEpochs
            iteration = iteration + 1;
            [XTrain, YTrain] = next(mbqTrain);
            % Evaluate the model gradients and loss using dlfeval and the
            % modelGradients function.
            [gradients, state, lossInfo] = dlfeval(@modelGradients, net, XTrain, YTrain, anchorBoxes, anchorBoxMasks, penaltyThreshold, networkOutputs);
            % Apply L2 regularization.
            gradients = dlupdate(@(g,w) g + l2Regularization*w, gradients, net.Learnables);
            % Determine the current learning rate value.
            currentLR = piecewiseLearningRateWithWarmup(iteration, epoch, learningRate, warmupPeriod, numEpochs);
            % Update the network learnable parameters using the SGDM optimizer.
            [net, velocity] = sgdmupdate(net, gradients, velocity, currentLR);
            % Update the state parameters of dlnetwork.
            net.State = state;
            % Display progress.
            displayLossInfo(epoch, iteration, currentLR, lossInfo);  
            % Update training plot with new points.
            updatePlots(lossPlotter, learningRatePlotter, iteration, currentLR, lossInfo.totalLoss);

Evaluate Model

Computer Vision System Toolbox™ provides object detector evaluation functions to measure common metrics such as average precision (evaluateDetectionPrecision) and log-average miss rates (evaluateDetectionMissRate). In this example, the average precision metric is used. The average precision provides a single number that incorporates the ability of the detector to make correct classifications (precision) and the ability of the detector to find all relevant objects (recall).

Following these steps to evaluate the trained dlnetwork object net on test data.

  • Specify the confidence threshold as 0.5 to keep only detections with confidence scores above this value.

  • Specify the overlap threshold as 0.5 to remove overlapping detections.

  • Apply the same preprocessing transform to the test data as for the training data. Note that data augmentation is not applied to the test data. Test data must be representative of the original data and be left unmodified for unbiased evaluation.

  • Collect the detection results by running the detector on preprocessedTestData. Use the supporting function yolov3Detect to get the bounding boxes, object confidence scores, and class labels.

  • Call evaluateDetectionPrecision with predicted results and preprocessedTestData as arguments.

confidenceThreshold = 0.5;
overlapThreshold = 0.5;

% Create the test datastore.
preprocessedTestData = transform(testData, @(data)preprocessData(data, networkInputSize));

% Create a table to hold the bounding boxes, scores, and labels returned by
% the detector. 
numImages = size(testDataTbl, 1);
results = table('Size', [0 3], ...
    'VariableTypes', {'cell','cell','cell'}, ...
    'VariableNames', {'Boxes','Scores','Labels'});

mbqTest = minibatchqueue(preprocessedTestData, 1, ...
    "MiniBatchSize", miniBatchSize, ...
    "MiniBatchFormat", "SSCB");

% Run detector on images in the test set and collect results.
while hasdata(mbqTest)
    % Read the datastore and get the image.
    XTest = next(mbqTest);
    % Run the detector.
    [bboxes, scores, labels] = yolov3Detect(net, XTest, networkOutputs, anchorBoxes, anchorBoxMasks, confidenceThreshold, overlapThreshold, classNames);
    % Collect the results.
    tbl = table(bboxes, scores, labels, 'VariableNames', {'Boxes','Scores','Labels'});
    results = [results; tbl];

% Evaluate the object detector using Average Precision metric.
[ap, recall, precision] = evaluateDetectionPrecision(results, preprocessedTestData);

The precision-recall (PR) curve shows how precise a detector is at varying levels of recall. Ideally, the precision is 1 at all recall levels.

% Plot precision-recall curve.
plot(recall, precision)
grid on
title(sprintf('Average Precision = %.2f', ap))

Detect Objects Using YOLO v3

Use the network for object detection.

  • Read an image.

  • Convert the image to a dlarray and use a GPU if one is available..

  • Use the supporting function yolov3Detect to get the predicted bounding boxes, confidence scores, and class labels.

  • Display the image with bounding boxes and confidence scores.

% Read the datastore.
data = read(preprocessedTestData);

% Get the image.
I = data{1};

% Convert to dlarray.
XTest = dlarray(I, 'SSCB');

executionEnvironment = "auto";

% If GPU is available, then convert data to gpuArray.
if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
    XTest = gpuArray(XTest);

[bboxes, scores, labels] = yolov3Detect(net, XTest, networkOutputs, anchorBoxes, anchorBoxMasks, confidenceThreshold, overlapThreshold, classNames);

% Clear the persistent variables used in the yolov3Detect function to avoid retaining their values in memory.
clear yolov3Detect  

% Display the detections on image.
if ~isempty(scores{1})
    I = insertObjectAnnotation(I, 'rectangle', bboxes{1}, scores{1});

Supporting Functions

Model Gradients Function

The function modelGradients takes as input the dlnetwork object net, a mini-batch of input data XTrain with corresponding ground truth boxes YTrain, anchor boxes, anchor box mask, the specified penalty threshold, and the network output names as input arguments and returns the gradients of the loss with respect to the learnable parameters in net, the corresponding mini-batch loss, and the state of the current batch.

The model gradients function computes the total loss and gradients by performing these operations.

  • Generate predictions from the input batch of images using the supporting function yolov3Forward.

  • Collect predictions on the CPU for postprocessing.

  • Convert the predictions from the YOLO v3 grid cell coordinates to bounding box coordinates to allow easy comparison with the ground truth data by using the supporting functions generateTiledAnchors and applyAnchorBoxOffsets.

  • Generate targets for loss computation by using the converted predictions and ground truth data. These targets are generated for bounding box positions (x, y, width, height), object confidence, and class probabilities. See the supporting function generateTargets.

  • Calculates the mean squared error of the predicted bounding box coordinates with target boxes. See the supporting function bboxOffsetLoss.

  • Determines the binary cross-entropy of the predicted object confidence score with target object confidence score. See the supporting function objectnessLoss.

  • Determines the binary cross-entropy of the predicted class of object with the target. See the supporting function classConfidenceLoss.

  • Computes the total loss as the sum of all losses.

  • Computes the gradients of learnables with respect to the total loss.

function [gradients, state, info] = modelGradients(net, XTrain, YTrain, anchors, mask, penaltyThreshold, networkOutputs)
inputImageSize = size(XTrain,1:2);

% Gather the ground truths in the CPU for post processing
YTrain = gather(extractdata(YTrain));

% Extract the predictions from the network.
[YPredCell, state] = yolov3Forward(net,XTrain,networkOutputs,mask);

% Gather the activations in the CPU for post processing and extract dlarray data. 
gatheredPredictions = cellfun(@ gather, YPredCell(:,1:6),'UniformOutput',false); 
gatheredPredictions = cellfun(@ extractdata, gatheredPredictions, 'UniformOutput', false);

% Convert predictions from grid cell coordinates to box coordinates.
tiledAnchors = generateTiledAnchors(gatheredPredictions(:,2:5),anchors,mask);
gatheredPredictions(:,2:5) = applyAnchorBoxOffsets(tiledAnchors, gatheredPredictions(:,2:5), inputImageSize);

% Generate target for predictions from the ground truth data.
[boxTarget, objectnessTarget, classTarget, objectMaskTarget, boxErrorScale] = generateTargets(gatheredPredictions, YTrain, inputImageSize, anchors, mask, penaltyThreshold);

% Compute the loss.
boxLoss = bboxOffsetLoss(YPredCell(:,[2 3 7 8]),boxTarget,objectMaskTarget,boxErrorScale);
objLoss = objectnessLoss(YPredCell(:,1),objectnessTarget,objectMaskTarget);
clsLoss = classConfidenceLoss(YPredCell(:,6),classTarget,objectMaskTarget);
totalLoss = boxLoss + objLoss + clsLoss;

info.boxLoss = boxLoss;
info.objLoss = objLoss;
info.clsLoss = clsLoss;
info.totalLoss = totalLoss;

% Compute gradients of learnables with regard to loss.
gradients = dlgradient(totalLoss, net.Learnables);

function [YPredCell, state] = yolov3Forward(net, XTrain, networkOutputs, anchorBoxMask)
% Predict the output of network and extract the confidence score, x, y,
% width, height, and class.
YPredictions = cell(size(networkOutputs));
[YPredictions{:}, state] = forward(net, XTrain, 'Outputs', networkOutputs);
YPredCell = extractPredictions(YPredictions, anchorBoxMask);

% Append predicted width and height to the end as they are required
% for computing the loss.
YPredCell(:,7:8) = YPredCell(:,4:5);

% Apply sigmoid and exponential activation.
YPredCell(:,1:6) = applyActivations(YPredCell(:,1:6));

function boxLoss = bboxOffsetLoss(boxPredCell, boxDeltaTarget, boxMaskTarget, boxErrorScaleTarget)
% Mean squared error for bounding box position.
lossX = sum(cellfun(@(a,b,c,d) mse(a.*c.*d,b.*c.*d),boxPredCell(:,1),boxDeltaTarget(:,1),boxMaskTarget(:,1),boxErrorScaleTarget));
lossY = sum(cellfun(@(a,b,c,d) mse(a.*c.*d,b.*c.*d),boxPredCell(:,2),boxDeltaTarget(:,2),boxMaskTarget(:,1),boxErrorScaleTarget));
lossW = sum(cellfun(@(a,b,c,d) mse(a.*c.*d,b.*c.*d),boxPredCell(:,3),boxDeltaTarget(:,3),boxMaskTarget(:,1),boxErrorScaleTarget));
lossH = sum(cellfun(@(a,b,c,d) mse(a.*c.*d,b.*c.*d),boxPredCell(:,4),boxDeltaTarget(:,4),boxMaskTarget(:,1),boxErrorScaleTarget));
boxLoss = lossX+lossY+lossW+lossH;

function objLoss = objectnessLoss(objectnessPredCell, objectnessDeltaTarget, boxMaskTarget)
% Binary cross-entropy loss for objectness score.
objLoss = sum(cellfun(@(a,b,c) crossentropy(a.*c,b.*c,'TargetCategories','independent'),objectnessPredCell,objectnessDeltaTarget,boxMaskTarget(:,2)));

function clsLoss = classConfidenceLoss(classPredCell, classTarget, boxMaskTarget)
% Binary cross-entropy loss for class confidence score.
clsLoss = sum(cellfun(@(a,b,c) crossentropy(a.*c,b.*c,'TargetCategories','independent'),classPredCell,classTarget,boxMaskTarget(:,3)));

Augmentation and Data Processing Functions

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,...
    % 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,:);
        data(ii,:) = {I, bboxes, labels};

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);
    % Convert an input image with single channel to 3 channels.
    if numel(imgSize) < 3 
        I = repmat(I,1,1,3);
    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};

function [XTrain, YTrain] = createBatchData(data, groundTruthBoxes, groundTruthClasses, classNames)
% Returns images combined along the batch dimension in XTrain and
% normalized bounding boxes concatenated with classIDs in YTrain

% Concatenate images along the batch dimension.
XTrain = cat(4, data{:,1});

% Get class IDs from the class names.
classNames = repmat({categorical(classNames')}, size(groundTruthClasses));
[~, classIndices] = cellfun(@(a,b)ismember(a,b), groundTruthClasses, classNames, 'UniformOutput', false);

% Append the label indexes and training image size to scaled bounding boxes
% and create a single cell array of responses.
combinedResponses = cellfun(@(bbox, classid)[bbox, classid], groundTruthBoxes, classIndices, 'UniformOutput', false);
len = max( cellfun(@(x)size(x,1), combinedResponses ) );
paddedBBoxes = cellfun( @(v) padarray(v,[len-size(v,1),0],0,'post'), combinedResponses, 'UniformOutput',false);
YTrain = cat(4, paddedBBoxes{:,1});

Network Creation Functions

function lgraph = squeezenetFeatureExtractor(net, imageInputSize)
% The squeezenetFeatureExtractor function removes the layers after 'fire9-concat'
% in SqueezeNet and also removes any data normalization used by the image input layer.

% Convert to layerGraph.
lgraph = layerGraph(net);

lgraph = removeLayers(lgraph, {'drop9' 'conv10' 'relu_conv10' 'pool10' 'prob' 'ClassificationLayer_predictions'});
inputLayer = imageInputLayer(imageInputSize,'Normalization','none','Name','data');
lgraph = replaceLayer(lgraph,'data',inputLayer);

function lgraph = addFirstDetectionHead(lgraph,anchorBoxMasks,numPredictorsPerAnchor)
% The addFirstDetectionHead function adds the first detection head.

numAnchorsScale1 = size(anchorBoxMasks, 2);
% Compute the number of filters for last convolution layer.
numFilters = numAnchorsScale1*numPredictorsPerAnchor;
firstDetectionSubNetwork = [
lgraph = addLayers(lgraph,firstDetectionSubNetwork);

function lgraph = addSecondDetectionHead(lgraph,anchorBoxMasks,numPredictorsPerAnchor)
% The addSecondDetectionHead function adds the second detection head.

numAnchorsScale2 = size(anchorBoxMasks, 2);
% Compute the number of filters for the last convolution layer.
numFilters = numAnchorsScale2*numPredictorsPerAnchor;
secondDetectionSubNetwork = [
    depthConcatenationLayer(2, 'Name', 'depthConcat1Detection2');
lgraph = addLayers(lgraph,secondDetectionSubNetwork);

Learning Rate Schedule Function

function currentLR = piecewiseLearningRateWithWarmup(iteration, epoch, learningRate, warmupPeriod, numEpochs)
% The piecewiseLearningRateWithWarmup function computes the current
% learning rate based on the iteration number.
persistent warmUpEpoch;

if iteration <= warmupPeriod
    % Increase the learning rate for number of iterations in warmup period.
    currentLR = learningRate * ((iteration/warmupPeriod)^4);
    warmUpEpoch = epoch;
elseif iteration >= warmupPeriod && epoch < warmUpEpoch+floor(0.6*(numEpochs-warmUpEpoch))
    % After warm up period, keep the learning rate constant if the remaining number of epochs is less than 60 percent. 
    currentLR = learningRate;
elseif epoch >= warmUpEpoch + floor(0.6*(numEpochs-warmUpEpoch)) && epoch < warmUpEpoch+floor(0.9*(numEpochs-warmUpEpoch))
    % If the remaining number of epochs is more than 60 percent but less
    % than 90 percent multiply the learning rate by 0.1.
    currentLR = learningRate*0.1;
    % If remaining epochs are more than 90 percent multiply the learning
    % rate by 0.01.
    currentLR = learningRate*0.01;


Predict Functions

function [bboxes,scores,labels] = yolov3Detect(net, XTest, networkOutputs, anchors, anchorBoxMask, confidenceThreshold, overlapThreshold, classes)
% The yolov3Detect function detects the bounding boxes, scores, and labels in an image.

imageSize = size(XTest, [1,2]);

% Find the input image layer and get the network input size. To retain 'networkInputSize' in memory and avoid
% recalculating it, declare it as persistent. 
persistent networkInputSize

if isempty(networkInputSize)
    networkInputIdx = arrayfun( @(x)isa(x,'nnet.cnn.layer.ImageInputLayer'), net.Layers);
    networkInputSize = net.Layers(networkInputIdx).InputSize;  

% Predict and filter the detections based on confidence threshold.
predictions = yolov3Predict(net,XTest,networkOutputs,anchorBoxMask);
predictions = cellfun(@ gather, predictions,'UniformOutput',false);
predictions = cellfun(@ extractdata, predictions, 'UniformOutput', false);
tiledAnchors = generateTiledAnchors(predictions(:,2:5),anchors,anchorBoxMask);
predictions(:,2:5) = applyAnchorBoxOffsets(tiledAnchors, predictions(:,2:5), networkInputSize);

numMiniBatch = size(XTest, 4);

bboxes = cell(numMiniBatch, 1);
scores = cell(numMiniBatch, 1);
labels = cell(numMiniBatch, 1);

for ii = 1:numMiniBatch
    fmap = cellfun(@(x) x(:,:,:,ii), predictions, 'UniformOutput', false);
    [bboxes{ii}, scores{ii}, labels{ii}] = ...
        generateYOLOv3Detections(fmap, confidenceThreshold, overlapThreshold, imageSize, classes);


function YPredCell = yolov3Predict(net,XTrain,networkOutputs,anchorBoxMask)
% Predict the output of network and extract the confidence, x, y,
% width, height, and class.
YPredictions = cell(size(networkOutputs));
[YPredictions{:}] = predict(net, XTrain);
YPredCell = extractPredictions(YPredictions, anchorBoxMask);

% Apply activation to the predicted cell array.
YPredCell = applyActivations(YPredCell);

Utility Functions

function YPredCell = applyActivations(YPredCell)
YPredCell(:,1:3) = cellfun(@ sigmoid, YPredCell(:,1:3), 'UniformOutput', false);
YPredCell(:,4:5) = cellfun(@ exp, YPredCell(:,4:5), 'UniformOutput', false);    
YPredCell(:,6) = cellfun(@ sigmoid, YPredCell(:,6), 'UniformOutput', false);

function predictions = extractPredictions(YPredictions, anchorBoxMask)
predictions = cell(size(YPredictions, 1),6);
for ii = 1:size(YPredictions, 1)
    % Get the required info on feature size.
    numChannelsPred = size(YPredictions{ii},3);
    numAnchors = size(anchorBoxMask{ii},2);
    numPredElemsPerAnchors = numChannelsPred/numAnchors;
    allIds = (1:numChannelsPred);
    stride = numPredElemsPerAnchors;
    endIdx = numChannelsPred;

    % X positions.
    startIdx = 1;
    predictions{ii,2} = YPredictions{ii}(:,:,startIdx:stride:endIdx,:);
    xIds = startIdx:stride:endIdx;
    % Y positions.
    startIdx = 2;
    predictions{ii,3} = YPredictions{ii}(:,:,startIdx:stride:endIdx,:);
    yIds = startIdx:stride:endIdx;
    % Width.
    startIdx = 3;
    predictions{ii,4} = YPredictions{ii}(:,:,startIdx:stride:endIdx,:);
    wIds = startIdx:stride:endIdx;
    % Height.
    startIdx = 4;
    predictions{ii,5} = YPredictions{ii}(:,:,startIdx:stride:endIdx,:);
    hIds = startIdx:stride:endIdx;
    % Confidence scores.
    startIdx = 5;
    predictions{ii,1} = YPredictions{ii}(:,:,startIdx:stride:endIdx,:);
    confIds = startIdx:stride:endIdx;
    % Accummulate all the non-class indexes
    nonClassIds = [xIds yIds wIds hIds confIds];
    % Class probabilities.
    % Get the indexes which do not belong to the nonClassIds
    classIdx = setdiff(allIds,nonClassIds);
    predictions{ii,6} = YPredictions{ii}(:,:,classIdx,:);

function tiledAnchors = generateTiledAnchors(YPredCell,anchorBoxes,anchorBoxMask)
% Generate tiled anchor offset.
tiledAnchors = cell(size(YPredCell));
for i=1:size(YPredCell,1)
    anchors = anchorBoxes(anchorBoxMask{i}, :);
    [h,w,~,n] = size(YPredCell{i,1});
    [tiledAnchors{i,2}, tiledAnchors{i,1}] = ndgrid(0:h-1,0:w-1,1:size(anchors,1),1:n);
    [~,~,tiledAnchors{i,3}] = ndgrid(0:h-1,0:w-1,anchors(:,2),1:n);
    [~,~,tiledAnchors{i,4}] = ndgrid(0:h-1,0:w-1,anchors(:,1),1:n);

function tiledAnchors = applyAnchorBoxOffsets(tiledAnchors,YPredCell,inputImageSize)
% Convert grid cell coordinates to box coordinates.
for i=1:size(YPredCell,1)
    [h,w,~,~] = size(YPredCell{i,1});  
    tiledAnchors{i,1} = (tiledAnchors{i,1}+YPredCell{i,1})./w;
    tiledAnchors{i,2} = (tiledAnchors{i,2}+YPredCell{i,2})./h;
    tiledAnchors{i,3} = (tiledAnchors{i,3}.*YPredCell{i,3})./inputImageSize(2);
    tiledAnchors{i,4} = (tiledAnchors{i,4}.*YPredCell{i,4})./inputImageSize(1);

function [lossPlotter, learningRatePlotter] = configureTrainingProgressPlotter(f)
% Create the subplots to display the loss and learning rate.
ylabel('Learning Rate');
learningRatePlotter = animatedline;
ylabel('Total Loss');
lossPlotter = animatedline;

function displayLossInfo(epoch, iteration, currentLR, lossInfo)
% Display loss information for each iteration.
disp("Epoch : " + epoch + " | Iteration : " + iteration + " | Learning Rate : " + currentLR + ...
   " | Total Loss : " + double(gather(extractdata(lossInfo.totalLoss))) + ...
   " | Box Loss : " + double(gather(extractdata(lossInfo.boxLoss))) + ...
   " | Object Loss : " + double(gather(extractdata(lossInfo.objLoss))) + ...
   " | Class Loss : " + double(gather(extractdata(lossInfo.clsLoss))));

function updatePlots(lossPlotter, learningRatePlotter, iteration, currentLR, totalLoss)
% Update loss and learning rate plots.
addpoints(lossPlotter, iteration, double(extractdata(gather(totalLoss))));
addpoints(learningRatePlotter, iteration, currentLR);

function net = downloadPretrainedYOLOv3Detector()
% Download a pretrained yolov3 detector.
if ~exist('yolov3SqueezeNetVehicleExample_20b.mat', 'file')
    if ~exist('', 'file')
        disp('Downloading pretrained detector (8.9 MB)...');
        pretrainedURL = '';
        websave('', pretrainedURL);
pretrained = load("yolov3SqueezeNetVehicleExample_20b.mat");
net =;


1. Redmon, Joseph, and Ali Farhadi. “YOLOv3: An Incremental Improvement.” Preprint, submitted April 8, 2018.