Train YOLO v2 Network for Vehicle Detection
Load the training data for vehicle detection into the workspace.
data = load('vehicleTrainingData.mat');
trainingData = data.vehicleTrainingData;Specify the directory in which training samples are stored. Add full path to the file names in training data.
dataDir = fullfile(toolboxdir('vision'),'visiondata'); trainingData.imageFilename = fullfile(dataDir,trainingData.imageFilename);
Randomly shuffle data for training.
rng(0); shuffledIdx = randperm(height(trainingData)); trainingData = trainingData(shuffledIdx,:);
Create an imageDatastore using the files from the table.
imds = imageDatastore(trainingData.imageFilename);
Create a boxLabelDatastore using the label columns from the table.
blds = boxLabelDatastore(trainingData(:,2:end));
Combine the datastores.
ds = combine(imds, blds);
Load a preinitialized YOLO v2 object detection network.
net = load('yolov2VehicleDetector.mat');
lgraph = net.lgraphlgraph =
LayerGraph with properties:
Layers: [25×1 nnet.cnn.layer.Layer]
Connections: [24×2 table]
InputNames: {'input'}
OutputNames: {'yolov2OutputLayer'}
Inspect the layers in the YOLO v2 network and their properties. You can also create the YOLO v2 network by following the steps given in Create YOLO v2 Object Detection Network.
lgraph.Layers
ans =
25x1 Layer array with layers:
1 'input' Image Input 128x128x3 images
2 'conv_1' Convolution 16 3x3 convolutions with stride [1 1] and padding [1 1 1 1]
3 'BN1' Batch Normalization Batch normalization
4 'relu_1' ReLU ReLU
5 'maxpool1' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
6 'conv_2' Convolution 32 3x3 convolutions with stride [1 1] and padding [1 1 1 1]
7 'BN2' Batch Normalization Batch normalization
8 'relu_2' ReLU ReLU
9 'maxpool2' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
10 'conv_3' Convolution 64 3x3 convolutions with stride [1 1] and padding [1 1 1 1]
11 'BN3' Batch Normalization Batch normalization
12 'relu_3' ReLU ReLU
13 'maxpool3' Max Pooling 2x2 max pooling with stride [2 2] and padding [0 0 0 0]
14 'conv_4' Convolution 128 3x3 convolutions with stride [1 1] and padding [1 1 1 1]
15 'BN4' Batch Normalization Batch normalization
16 'relu_4' ReLU ReLU
17 'yolov2Conv1' Convolution 128 3x3 convolutions with stride [1 1] and padding 'same'
18 'yolov2Batch1' Batch Normalization Batch normalization
19 'yolov2Relu1' ReLU ReLU
20 'yolov2Conv2' Convolution 128 3x3 convolutions with stride [1 1] and padding 'same'
21 'yolov2Batch2' Batch Normalization Batch normalization
22 'yolov2Relu2' ReLU ReLU
23 'yolov2ClassConv' Convolution 24 1x1 convolutions with stride [1 1] and padding [0 0 0 0]
24 'yolov2Transform' YOLO v2 Transform Layer. YOLO v2 Transform Layer with 4 anchors.
25 'yolov2OutputLayer' YOLO v2 Output YOLO v2 Output with 4 anchors.
Configure the network training options.
options = trainingOptions('sgdm',... 'InitialLearnRate',0.001,... 'Verbose',true,... 'MiniBatchSize',16,... 'MaxEpochs',30,... 'Shuffle','never',... 'VerboseFrequency',30,... 'CheckpointPath',tempdir);
Train the YOLO v2 network.
[detector,info] = trainYOLOv2ObjectDetector(ds,lgraph,options);
************************************************************************* Training a YOLO v2 Object Detector for the following object classes: * vehicle Training on single CPU. |========================================================================================| | Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning | | | | (hh:mm:ss) | RMSE | Loss | Rate | |========================================================================================| | 1 | 1 | 00:00:01 | 7.13 | 50.8 | 0.0010 | | 2 | 30 | 00:00:14 | 1.35 | 1.8 | 0.0010 | | 4 | 60 | 00:00:27 | 1.13 | 1.3 | 0.0010 | | 5 | 90 | 00:00:39 | 0.64 | 0.4 | 0.0010 | | 7 | 120 | 00:00:51 | 0.65 | 0.4 | 0.0010 | | 9 | 150 | 00:01:04 | 0.72 | 0.5 | 0.0010 | | 10 | 180 | 00:01:16 | 0.52 | 0.3 | 0.0010 | | 12 | 210 | 00:01:28 | 0.45 | 0.2 | 0.0010 | | 14 | 240 | 00:01:41 | 0.61 | 0.4 | 0.0010 | | 15 | 270 | 00:01:52 | 0.43 | 0.2 | 0.0010 | | 17 | 300 | 00:02:05 | 0.42 | 0.2 | 0.0010 | | 19 | 330 | 00:02:17 | 0.52 | 0.3 | 0.0010 | | 20 | 360 | 00:02:29 | 0.43 | 0.2 | 0.0010 | | 22 | 390 | 00:02:42 | 0.43 | 0.2 | 0.0010 | | 24 | 420 | 00:02:54 | 0.59 | 0.4 | 0.0010 | | 25 | 450 | 00:03:06 | 0.61 | 0.4 | 0.0010 | | 27 | 480 | 00:03:18 | 0.65 | 0.4 | 0.0010 | | 29 | 510 | 00:03:31 | 0.48 | 0.2 | 0.0010 | | 30 | 540 | 00:03:42 | 0.34 | 0.1 | 0.0010 | |========================================================================================| Detector training complete. *************************************************************************
Inspect the properties of the detector.
detector
detector =
yolov2ObjectDetector with properties:
ModelName: 'vehicle'
Network: [1×1 DAGNetwork]
TrainingImageSize: [128 128]
AnchorBoxes: [4×2 double]
ClassNames: vehicle
You can verify the training accuracy by inspecting the training loss for each iteration.
figure plot(info.TrainingLoss) grid on xlabel('Number of Iterations') ylabel('Training Loss for Each Iteration')
Read a test image into the workspace.
img = imread('detectcars.png');Run the trained YOLO v2 object detector on the test image for vehicle detection.
[bboxes,scores] = detect(detector,img);
Display the detection results.
if(~isempty(bboxes)) img = insertObjectAnnotation(img,'rectangle',bboxes,scores); end figure imshow(img)
