Error: Unable to find variable while trying to recognize results using trained network.

2023 1 16
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更新时间：2023 1 30
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I am trying to detect pedestrian using thermal dataset. I was able to train using custome dataset using DarkNet53, using Deep Network Designer script. After that when I am trying to test the results I am facing error. Before it was working fine, now I am trying using new dataset but it should not be the problem. I have attached my .m file and some sample images, if you can check the code and let me know where I need to modify it would be really helpful as this is part of my project.
Thank you very much in advance.
@Image Analyst @Walter Roberson

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Partha Dey
Partha Dey 2023-1-16
编辑：Partha Dey 2023-1-16
I am unable to attach the files as I have exceeded maximum number of attachments for the day. I am giving the full code which was working before and will attach the images once I am able to do it. Thank you very in advance.
% initialization parameters are the parameters of the initial pretrained network.
trainingSetup = load("C:\Users\Neel\Documents\MATLAB\YOLOv3ObjectDetection\DarkNet53\trainednetwork.mat");
%% Import Data
% Import training and validation data.
imdsTrain = imageDatastore("C:\Users\Neel\Documents\MATLAB\YOLOv3ObjectDetection\DarkNet53\Dataset1","IncludeSubfolders",true,"LabelSource","foldernames");
[imdsTrain, imdsValidation] = splitEachLabel(imdsTrain,0.7);
%% Augmentation Settings
imageAugmenter = imageDataAugmenter(...
"RandRotation",[-90 90],...
"RandScale",[1 2],...
"RandXReflection",true);
%Resize the images to match the network input layer.
augimdsTrain = augmentedImageDatastore([256 256 3],imdsTrain,"DataAugmentation",imageAugmenter);
augimdsValidation = augmentedImageDatastore([256 256 3],imdsValidation);
%% Set Training Options
% Specify options to use when training.
opts = trainingOptions("sgdm",...
"ExecutionEnvironment","auto",...
"InitialLearnRate",0.0001,...
"LearnRateDropFactor",0.2,...
"LearnRateDropPeriod",5,...
"MaxEpochs",20,...
"MiniBatchSize",30,...
"Shuffle","every-epoch",...
"ValidationFrequency",13,...
"Plots","training-progress",...
"ValidationData",augimdsValidation);
%% Create Layer Graph
% Create the layer graph variable to contain the network layers.
lgraph = layerGraph();
%% Add Layer Branches
% Add the branches of the network to the layer graph. Each branch is a linear
% array of layers.
tempLayers = [imageInputLayer([256 256 3],"Name","input","Normalization","rescale-zero-one","Max",trainingSetup.input.Max,"Min",trainingSetup.input.Min)
convolution2dLayer([3 3],32,"Name","conv1","Padding","same","Bias",trainingSetup.conv1.Bias,"Weights",trainingSetup.conv1.Weights)
batchNormalizationLayer("Name","batchnorm1","Offset",trainingSetup.batchnorm1.Offset,"Scale",trainingSetup.batchnorm1.Scale,"TrainedMean",trainingSetup.batchnorm1.TrainedMean,"TrainedVariance",trainingSetup.batchnorm1.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu1")
convolution2dLayer([3 3],64,"Name","conv2","Padding",[1 0 1 0],"Stride",[2 2],"Bias",trainingSetup.conv2.Bias,"Weights",trainingSetup.conv2.Weights)
batchNormalizationLayer("Name","batchnorm2","Offset",trainingSetup.batchnorm2.Offset,"Scale",trainingSetup.batchnorm2.Scale,"TrainedMean",trainingSetup.batchnorm2.TrainedMean,"TrainedVariance",trainingSetup.batchnorm2.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],32,"Name","conv3","Padding","same","Bias",trainingSetup.conv3.Bias,"Weights",trainingSetup.conv3.Weights)
batchNormalizationLayer("Name","batchnorm3","Offset",trainingSetup.batchnorm3.Offset,"Scale",trainingSetup.batchnorm3.Scale,"TrainedMean",trainingSetup.batchnorm3.TrainedMean,"TrainedVariance",trainingSetup.batchnorm3.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu3")
convolution2dLayer([3 3],64,"Name","conv4","Padding","same","Bias",trainingSetup.conv4.Bias,"Weights",trainingSetup.conv4.Weights)
batchNormalizationLayer("Name","batchnorm4","Offset",trainingSetup.batchnorm4.Offset,"Scale",trainingSetup.batchnorm4.Scale,"TrainedMean",trainingSetup.batchnorm4.TrainedMean,"TrainedVariance",trainingSetup.batchnorm4.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu4")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
additionLayer(2,"Name","res1")
convolution2dLayer([3 3],128,"Name","conv5","Padding",[1 0 1 0],"Stride",[2 2],"Bias",trainingSetup.conv5.Bias,"Weights",trainingSetup.conv5.Weights)
batchNormalizationLayer("Name","batchnorm5","Offset",trainingSetup.batchnorm5.Offset,"Scale",trainingSetup.batchnorm5.Scale,"TrainedMean",trainingSetup.batchnorm5.TrainedMean,"TrainedVariance",trainingSetup.batchnorm5.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu5")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],64,"Name","conv6","Padding","same","Bias",trainingSetup.conv6.Bias,"Weights",trainingSetup.conv6.Weights)
batchNormalizationLayer("Name","batchnorm6","Offset",trainingSetup.batchnorm6.Offset,"Scale",trainingSetup.batchnorm6.Scale,"TrainedMean",trainingSetup.batchnorm6.TrainedMean,"TrainedVariance",trainingSetup.batchnorm6.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu6")
convolution2dLayer([3 3],128,"Name","conv7","Padding","same","Bias",trainingSetup.conv7.Bias,"Weights",trainingSetup.conv7.Weights)
batchNormalizationLayer("Name","batchnorm7","Offset",trainingSetup.batchnorm7.Offset,"Scale",trainingSetup.batchnorm7.Scale,"TrainedMean",trainingSetup.batchnorm7.TrainedMean,"TrainedVariance",trainingSetup.batchnorm7.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu7")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res2");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],64,"Name","conv8","Padding","same","Bias",trainingSetup.conv8.Bias,"Weights",trainingSetup.conv8.Weights)
batchNormalizationLayer("Name","batchnorm8","Offset",trainingSetup.batchnorm8.Offset,"Scale",trainingSetup.batchnorm8.Scale,"TrainedMean",trainingSetup.batchnorm8.TrainedMean,"TrainedVariance",trainingSetup.batchnorm8.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu8")
convolution2dLayer([3 3],128,"Name","conv9","Padding","same","Bias",trainingSetup.conv9.Bias,"Weights",trainingSetup.conv9.Weights)
batchNormalizationLayer("Name","batchnorm9","Offset",trainingSetup.batchnorm9.Offset,"Scale",trainingSetup.batchnorm9.Scale,"TrainedMean",trainingSetup.batchnorm9.TrainedMean,"TrainedVariance",trainingSetup.batchnorm9.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu9")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
additionLayer(2,"Name","res3")
convolution2dLayer([3 3],256,"Name","conv10","Padding",[1 0 1 0],"Stride",[2 2],"Bias",trainingSetup.conv10.Bias,"Weights",trainingSetup.conv10.Weights)
batchNormalizationLayer("Name","batchnorm10","Offset",trainingSetup.batchnorm10.Offset,"Scale",trainingSetup.batchnorm10.Scale,"TrainedMean",trainingSetup.batchnorm10.TrainedMean,"TrainedVariance",trainingSetup.batchnorm10.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu10")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv11","Padding","same","Bias",trainingSetup.conv11.Bias,"Weights",trainingSetup.conv11.Weights)
batchNormalizationLayer("Name","batchnorm11","Offset",trainingSetup.batchnorm11.Offset,"Scale",trainingSetup.batchnorm11.Scale,"TrainedMean",trainingSetup.batchnorm11.TrainedMean,"TrainedVariance",trainingSetup.batchnorm11.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu11")
convolution2dLayer([3 3],256,"Name","conv12","Padding","same","Bias",trainingSetup.conv12.Bias,"Weights",trainingSetup.conv12.Weights)
batchNormalizationLayer("Name","batchnorm12","Offset",trainingSetup.batchnorm12.Offset,"Scale",trainingSetup.batchnorm12.Scale,"TrainedMean",trainingSetup.batchnorm12.TrainedMean,"TrainedVariance",trainingSetup.batchnorm12.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu12")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res4");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv13","Padding","same","Bias",trainingSetup.conv13.Bias,"Weights",trainingSetup.conv13.Weights)
batchNormalizationLayer("Name","batchnorm13","Offset",trainingSetup.batchnorm13.Offset,"Scale",trainingSetup.batchnorm13.Scale,"TrainedMean",trainingSetup.batchnorm13.TrainedMean,"TrainedVariance",trainingSetup.batchnorm13.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu13")
convolution2dLayer([3 3],256,"Name","conv14","Padding","same","Bias",trainingSetup.conv14.Bias,"Weights",trainingSetup.conv14.Weights)
batchNormalizationLayer("Name","batchnorm14","Offset",trainingSetup.batchnorm14.Offset,"Scale",trainingSetup.batchnorm14.Scale,"TrainedMean",trainingSetup.batchnorm14.TrainedMean,"TrainedVariance",trainingSetup.batchnorm14.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu14")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res5");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv15","Padding","same","Bias",trainingSetup.conv15.Bias,"Weights",trainingSetup.conv15.Weights)
batchNormalizationLayer("Name","batchnorm15","Offset",trainingSetup.batchnorm15.Offset,"Scale",trainingSetup.batchnorm15.Scale,"TrainedMean",trainingSetup.batchnorm15.TrainedMean,"TrainedVariance",trainingSetup.batchnorm15.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu15")
convolution2dLayer([3 3],256,"Name","conv16","Padding","same","Bias",trainingSetup.conv16.Bias,"Weights",trainingSetup.conv16.Weights)
batchNormalizationLayer("Name","batchnorm16","Offset",trainingSetup.batchnorm16.Offset,"Scale",trainingSetup.batchnorm16.Scale,"TrainedMean",trainingSetup.batchnorm16.TrainedMean,"TrainedVariance",trainingSetup.batchnorm16.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu16")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res6");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv17","Padding","same","Bias",trainingSetup.conv17.Bias,"Weights",trainingSetup.conv17.Weights)
batchNormalizationLayer("Name","batchnorm17","Offset",trainingSetup.batchnorm17.Offset,"Scale",trainingSetup.batchnorm17.Scale,"TrainedMean",trainingSetup.batchnorm17.TrainedMean,"TrainedVariance",trainingSetup.batchnorm17.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu17")
convolution2dLayer([3 3],256,"Name","conv18","Padding","same","Bias",trainingSetup.conv18.Bias,"Weights",trainingSetup.conv18.Weights)
batchNormalizationLayer("Name","batchnorm18","Offset",trainingSetup.batchnorm18.Offset,"Scale",trainingSetup.batchnorm18.Scale,"TrainedMean",trainingSetup.batchnorm18.TrainedMean,"TrainedVariance",trainingSetup.batchnorm18.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu18")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res7");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv19","Padding","same","Bias",trainingSetup.conv19.Bias,"Weights",trainingSetup.conv19.Weights)
batchNormalizationLayer("Name","batchnorm19","Offset",trainingSetup.batchnorm19.Offset,"Scale",trainingSetup.batchnorm19.Scale,"TrainedMean",trainingSetup.batchnorm19.TrainedMean,"TrainedVariance",trainingSetup.batchnorm19.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu19")
convolution2dLayer([3 3],256,"Name","conv20","Padding","same","Bias",trainingSetup.conv20.Bias,"Weights",trainingSetup.conv20.Weights)
batchNormalizationLayer("Name","batchnorm20","Offset",trainingSetup.batchnorm20.Offset,"Scale",trainingSetup.batchnorm20.Scale,"TrainedMean",trainingSetup.batchnorm20.TrainedMean,"TrainedVariance",trainingSetup.batchnorm20.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu20")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res8");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv21","Padding","same","Bias",trainingSetup.conv21.Bias,"Weights",trainingSetup.conv21.Weights)
batchNormalizationLayer("Name","batchnorm21","Offset",trainingSetup.batchnorm21.Offset,"Scale",trainingSetup.batchnorm21.Scale,"TrainedMean",trainingSetup.batchnorm21.TrainedMean,"TrainedVariance",trainingSetup.batchnorm21.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu21")
convolution2dLayer([3 3],256,"Name","conv22","Padding","same","Bias",trainingSetup.conv22.Bias,"Weights",trainingSetup.conv22.Weights)
batchNormalizationLayer("Name","batchnorm22","Offset",trainingSetup.batchnorm22.Offset,"Scale",trainingSetup.batchnorm22.Scale,"TrainedMean",trainingSetup.batchnorm22.TrainedMean,"TrainedVariance",trainingSetup.batchnorm22.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu22")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res9");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv23","Padding","same","Bias",trainingSetup.conv23.Bias,"Weights",trainingSetup.conv23.Weights)
batchNormalizationLayer("Name","batchnorm23","Offset",trainingSetup.batchnorm23.Offset,"Scale",trainingSetup.batchnorm23.Scale,"TrainedMean",trainingSetup.batchnorm23.TrainedMean,"TrainedVariance",trainingSetup.batchnorm23.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu23")
convolution2dLayer([3 3],256,"Name","conv24","Padding","same","Bias",trainingSetup.conv24.Bias,"Weights",trainingSetup.conv24.Weights)
batchNormalizationLayer("Name","batchnorm24","Offset",trainingSetup.batchnorm24.Offset,"Scale",trainingSetup.batchnorm24.Scale,"TrainedMean",trainingSetup.batchnorm24.TrainedMean,"TrainedVariance",trainingSetup.batchnorm24.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu24")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res10");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],128,"Name","conv25","Padding","same","Bias",trainingSetup.conv25.Bias,"Weights",trainingSetup.conv25.Weights)
batchNormalizationLayer("Name","batchnorm25","Offset",trainingSetup.batchnorm25.Offset,"Scale",trainingSetup.batchnorm25.Scale,"TrainedMean",trainingSetup.batchnorm25.TrainedMean,"TrainedVariance",trainingSetup.batchnorm25.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu25")
convolution2dLayer([3 3],256,"Name","conv26","Padding","same","Bias",trainingSetup.conv26.Bias,"Weights",trainingSetup.conv26.Weights)
batchNormalizationLayer("Name","batchnorm26","Offset",trainingSetup.batchnorm26.Offset,"Scale",trainingSetup.batchnorm26.Scale,"TrainedMean",trainingSetup.batchnorm26.TrainedMean,"TrainedVariance",trainingSetup.batchnorm26.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu26")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
additionLayer(2,"Name","res11")
convolution2dLayer([3 3],512,"Name","conv27","Padding",[1 0 1 0],"Stride",[2 2],"Bias",trainingSetup.conv27.Bias,"Weights",trainingSetup.conv27.Weights)
batchNormalizationLayer("Name","batchnorm27","Offset",trainingSetup.batchnorm27.Offset,"Scale",trainingSetup.batchnorm27.Scale,"TrainedMean",trainingSetup.batchnorm27.TrainedMean,"TrainedVariance",trainingSetup.batchnorm27.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu27")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv28","Padding","same","Bias",trainingSetup.conv28.Bias,"Weights",trainingSetup.conv28.Weights)
batchNormalizationLayer("Name","batchnorm28","Offset",trainingSetup.batchnorm28.Offset,"Scale",trainingSetup.batchnorm28.Scale,"TrainedMean",trainingSetup.batchnorm28.TrainedMean,"TrainedVariance",trainingSetup.batchnorm28.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu28")
convolution2dLayer([3 3],512,"Name","conv29","Padding","same","Bias",trainingSetup.conv29.Bias,"Weights",trainingSetup.conv29.Weights)
batchNormalizationLayer("Name","batchnorm29","Offset",trainingSetup.batchnorm29.Offset,"Scale",trainingSetup.batchnorm29.Scale,"TrainedMean",trainingSetup.batchnorm29.TrainedMean,"TrainedVariance",trainingSetup.batchnorm29.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu29")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res12");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv30","Padding","same","Bias",trainingSetup.conv30.Bias,"Weights",trainingSetup.conv30.Weights)
batchNormalizationLayer("Name","batchnorm30","Offset",trainingSetup.batchnorm30.Offset,"Scale",trainingSetup.batchnorm30.Scale,"TrainedMean",trainingSetup.batchnorm30.TrainedMean,"TrainedVariance",trainingSetup.batchnorm30.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu30")
convolution2dLayer([3 3],512,"Name","conv31","Padding","same","Bias",trainingSetup.conv31.Bias,"Weights",trainingSetup.conv31.Weights)
batchNormalizationLayer("Name","batchnorm31","Offset",trainingSetup.batchnorm31.Offset,"Scale",trainingSetup.batchnorm31.Scale,"TrainedMean",trainingSetup.batchnorm31.TrainedMean,"TrainedVariance",trainingSetup.batchnorm31.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu31")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res13");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv32","Padding","same","Bias",trainingSetup.conv32.Bias,"Weights",trainingSetup.conv32.Weights)
batchNormalizationLayer("Name","batchnorm32","Offset",trainingSetup.batchnorm32.Offset,"Scale",trainingSetup.batchnorm32.Scale,"TrainedMean",trainingSetup.batchnorm32.TrainedMean,"TrainedVariance",trainingSetup.batchnorm32.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu32")
convolution2dLayer([3 3],512,"Name","conv33","Padding","same","Bias",trainingSetup.conv33.Bias,"Weights",trainingSetup.conv33.Weights)
batchNormalizationLayer("Name","batchnorm33","Offset",trainingSetup.batchnorm33.Offset,"Scale",trainingSetup.batchnorm33.Scale,"TrainedMean",trainingSetup.batchnorm33.TrainedMean,"TrainedVariance",trainingSetup.batchnorm33.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu33")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res14");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv34","Padding","same","Bias",trainingSetup.conv34.Bias,"Weights",trainingSetup.conv34.Weights)
batchNormalizationLayer("Name","batchnorm34","Offset",trainingSetup.batchnorm34.Offset,"Scale",trainingSetup.batchnorm34.Scale,"TrainedMean",trainingSetup.batchnorm34.TrainedMean,"TrainedVariance",trainingSetup.batchnorm34.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu34")
convolution2dLayer([3 3],512,"Name","conv35","Padding","same","Bias",trainingSetup.conv35.Bias,"Weights",trainingSetup.conv35.Weights)
batchNormalizationLayer("Name","batchnorm35","Offset",trainingSetup.batchnorm35.Offset,"Scale",trainingSetup.batchnorm35.Scale,"TrainedMean",trainingSetup.batchnorm35.TrainedMean,"TrainedVariance",trainingSetup.batchnorm35.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu35")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res15");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv36","Padding","same","Bias",trainingSetup.conv36.Bias,"Weights",trainingSetup.conv36.Weights)
batchNormalizationLayer("Name","batchnorm36","Offset",trainingSetup.batchnorm36.Offset,"Scale",trainingSetup.batchnorm36.Scale,"TrainedMean",trainingSetup.batchnorm36.TrainedMean,"TrainedVariance",trainingSetup.batchnorm36.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu36")
convolution2dLayer([3 3],512,"Name","conv37","Padding","same","Bias",trainingSetup.conv37.Bias,"Weights",trainingSetup.conv37.Weights)
batchNormalizationLayer("Name","batchnorm37","Offset",trainingSetup.batchnorm37.Offset,"Scale",trainingSetup.batchnorm37.Scale,"TrainedMean",trainingSetup.batchnorm37.TrainedMean,"TrainedVariance",trainingSetup.batchnorm37.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu37")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res16");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv38","Padding","same","Bias",trainingSetup.conv38.Bias,"Weights",trainingSetup.conv38.Weights)
batchNormalizationLayer("Name","batchnorm38","Offset",trainingSetup.batchnorm38.Offset,"Scale",trainingSetup.batchnorm38.Scale,"TrainedMean",trainingSetup.batchnorm38.TrainedMean,"TrainedVariance",trainingSetup.batchnorm38.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu38")
convolution2dLayer([3 3],512,"Name","conv39","Padding","same","Bias",trainingSetup.conv39.Bias,"Weights",trainingSetup.conv39.Weights)
batchNormalizationLayer("Name","batchnorm39","Offset",trainingSetup.batchnorm39.Offset,"Scale",trainingSetup.batchnorm39.Scale,"TrainedMean",trainingSetup.batchnorm39.TrainedMean,"TrainedVariance",trainingSetup.batchnorm39.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu39")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res17");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv40","Padding","same","Bias",trainingSetup.conv40.Bias,"Weights",trainingSetup.conv40.Weights)
batchNormalizationLayer("Name","batchnorm40","Offset",trainingSetup.batchnorm40.Offset,"Scale",trainingSetup.batchnorm40.Scale,"TrainedMean",trainingSetup.batchnorm40.TrainedMean,"TrainedVariance",trainingSetup.batchnorm40.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu40")
convolution2dLayer([3 3],512,"Name","conv41","Padding","same","Bias",trainingSetup.conv41.Bias,"Weights",trainingSetup.conv41.Weights)
batchNormalizationLayer("Name","batchnorm41","Offset",trainingSetup.batchnorm41.Offset,"Scale",trainingSetup.batchnorm41.Scale,"TrainedMean",trainingSetup.batchnorm41.TrainedMean,"TrainedVariance",trainingSetup.batchnorm41.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu41")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res18");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],256,"Name","conv42","Padding","same","Bias",trainingSetup.conv42.Bias,"Weights",trainingSetup.conv42.Weights)
batchNormalizationLayer("Name","batchnorm42","Offset",trainingSetup.batchnorm42.Offset,"Scale",trainingSetup.batchnorm42.Scale,"TrainedMean",trainingSetup.batchnorm42.TrainedMean,"TrainedVariance",trainingSetup.batchnorm42.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu42")
convolution2dLayer([3 3],512,"Name","conv43","Padding","same","Bias",trainingSetup.conv43.Bias,"Weights",trainingSetup.conv43.Weights)
batchNormalizationLayer("Name","batchnorm43","Offset",trainingSetup.batchnorm43.Offset,"Scale",trainingSetup.batchnorm43.Scale,"TrainedMean",trainingSetup.batchnorm43.TrainedMean,"TrainedVariance",trainingSetup.batchnorm43.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu43")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
additionLayer(2,"Name","res19")
convolution2dLayer([3 3],1024,"Name","conv44","Padding",[1 0 1 0],"Stride",[2 2],"Bias",trainingSetup.conv44.Bias,"Weights",trainingSetup.conv44.Weights)
batchNormalizationLayer("Name","batchnorm44","Offset",trainingSetup.batchnorm44.Offset,"Scale",trainingSetup.batchnorm44.Scale,"TrainedMean",trainingSetup.batchnorm44.TrainedMean,"TrainedVariance",trainingSetup.batchnorm44.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu44")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],512,"Name","conv45","Padding","same","Bias",trainingSetup.conv45.Bias,"Weights",trainingSetup.conv45.Weights)
batchNormalizationLayer("Name","batchnorm45","Offset",trainingSetup.batchnorm45.Offset,"Scale",trainingSetup.batchnorm45.Scale,"TrainedMean",trainingSetup.batchnorm45.TrainedMean,"TrainedVariance",trainingSetup.batchnorm45.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu45")
convolution2dLayer([3 3],1024,"Name","conv46","Padding","same","Bias",trainingSetup.conv46.Bias,"Weights",trainingSetup.conv46.Weights)
batchNormalizationLayer("Name","batchnorm46","Offset",trainingSetup.batchnorm46.Offset,"Scale",trainingSetup.batchnorm46.Scale,"TrainedMean",trainingSetup.batchnorm46.TrainedMean,"TrainedVariance",trainingSetup.batchnorm46.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu46")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res20");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],512,"Name","conv47","Padding","same","Bias",trainingSetup.conv47.Bias,"Weights",trainingSetup.conv47.Weights)
batchNormalizationLayer("Name","batchnorm47","Offset",trainingSetup.batchnorm47.Offset,"Scale",trainingSetup.batchnorm47.Scale,"TrainedMean",trainingSetup.batchnorm47.TrainedMean,"TrainedVariance",trainingSetup.batchnorm47.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu47")
convolution2dLayer([3 3],1024,"Name","conv48","Padding","same","Bias",trainingSetup.conv48.Bias,"Weights",trainingSetup.conv48.Weights)
batchNormalizationLayer("Name","batchnorm48","Offset",trainingSetup.batchnorm48.Offset,"Scale",trainingSetup.batchnorm48.Scale,"TrainedMean",trainingSetup.batchnorm48.TrainedMean,"TrainedVariance",trainingSetup.batchnorm48.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu48")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res21");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],512,"Name","conv49","Padding","same","Bias",trainingSetup.conv49.Bias,"Weights",trainingSetup.conv49.Weights)
batchNormalizationLayer("Name","batchnorm49","Offset",trainingSetup.batchnorm49.Offset,"Scale",trainingSetup.batchnorm49.Scale,"TrainedMean",trainingSetup.batchnorm49.TrainedMean,"TrainedVariance",trainingSetup.batchnorm49.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu49")
convolution2dLayer([3 3],1024,"Name","conv50","Padding","same","Bias",trainingSetup.conv50.Bias,"Weights",trainingSetup.conv50.Weights)
batchNormalizationLayer("Name","batchnorm50","Offset",trainingSetup.batchnorm50.Offset,"Scale",trainingSetup.batchnorm50.Scale,"TrainedMean",trainingSetup.batchnorm50.TrainedMean,"TrainedVariance",trainingSetup.batchnorm50.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu50")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = additionLayer(2,"Name","res22");
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
convolution2dLayer([1 1],512,"Name","conv51","Padding","same","Bias",trainingSetup.conv51.Bias,"Weights",trainingSetup.conv51.Weights)
batchNormalizationLayer("Name","batchnorm51","Offset",trainingSetup.batchnorm51.Offset,"Scale",trainingSetup.batchnorm51.Scale,"TrainedMean",trainingSetup.batchnorm51.TrainedMean,"TrainedVariance",trainingSetup.batchnorm51.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu51")
convolution2dLayer([3 3],1024,"Name","conv52","Padding","same","Bias",trainingSetup.conv52.Bias,"Weights",trainingSetup.conv52.Weights)
batchNormalizationLayer("Name","batchnorm52","Offset",trainingSetup.batchnorm52.Offset,"Scale",trainingSetup.batchnorm52.Scale,"TrainedMean",trainingSetup.batchnorm52.TrainedMean,"TrainedVariance",trainingSetup.batchnorm52.TrainedVariance)
leakyReluLayer(0.1,"Name","leakyrelu52")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
additionLayer(2,"Name","res23")
globalAveragePooling2dLayer("Name","avg1")
convolution2dLayer([1 1],4,"Name","conv53","BiasLearnRateFactor",10,"Padding","same","WeightLearnRateFactor",10)
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")];
lgraph = addLayers(lgraph,tempLayers);
% clean up helper variable
clear tempLayers;
%% Connect Layer Branches
% Connect all the branches of the network to create the network graph.
lgraph = connectLayers(lgraph,"leakyrelu2","conv3");
lgraph = connectLayers(lgraph,"leakyrelu2","res1/in2");
lgraph = connectLayers(lgraph,"leakyrelu4","res1/in1");
lgraph = connectLayers(lgraph,"leakyrelu5","conv6");
lgraph = connectLayers(lgraph,"leakyrelu5","res2/in2");
lgraph = connectLayers(lgraph,"leakyrelu7","res2/in1");
lgraph = connectLayers(lgraph,"res2","conv8");
lgraph = connectLayers(lgraph,"res2","res3/in2");
lgraph = connectLayers(lgraph,"leakyrelu9","res3/in1");
lgraph = connectLayers(lgraph,"leakyrelu10","conv11");
lgraph = connectLayers(lgraph,"leakyrelu10","res4/in2");
lgraph = connectLayers(lgraph,"leakyrelu12","res4/in1");
lgraph = connectLayers(lgraph,"res4","conv13");
lgraph = connectLayers(lgraph,"res4","res5/in2");
lgraph = connectLayers(lgraph,"leakyrelu14","res5/in1");
lgraph = connectLayers(lgraph,"res5","conv15");
lgraph = connectLayers(lgraph,"res5","res6/in2");
lgraph = connectLayers(lgraph,"leakyrelu16","res6/in1");
lgraph = connectLayers(lgraph,"res6","conv17");
lgraph = connectLayers(lgraph,"res6","res7/in2");
lgraph = connectLayers(lgraph,"leakyrelu18","res7/in1");
lgraph = connectLayers(lgraph,"res7","conv19");
lgraph = connectLayers(lgraph,"res7","res8/in2");
lgraph = connectLayers(lgraph,"leakyrelu20","res8/in1");
lgraph = connectLayers(lgraph,"res8","conv21");
lgraph = connectLayers(lgraph,"res8","res9/in2");
lgraph = connectLayers(lgraph,"leakyrelu22","res9/in1");
lgraph = connectLayers(lgraph,"res9","conv23");
lgraph = connectLayers(lgraph,"res9","res10/in2");
lgraph = connectLayers(lgraph,"leakyrelu24","res10/in1");
lgraph = connectLayers(lgraph,"res10","conv25");
lgraph = connectLayers(lgraph,"res10","res11/in2");
lgraph = connectLayers(lgraph,"leakyrelu26","res11/in1");
lgraph = connectLayers(lgraph,"leakyrelu27","conv28");
lgraph = connectLayers(lgraph,"leakyrelu27","res12/in2");
lgraph = connectLayers(lgraph,"leakyrelu29","res12/in1");
lgraph = connectLayers(lgraph,"res12","conv30");
lgraph = connectLayers(lgraph,"res12","res13/in2");
lgraph = connectLayers(lgraph,"leakyrelu31","res13/in1");
lgraph = connectLayers(lgraph,"res13","conv32");
lgraph = connectLayers(lgraph,"res13","res14/in2");
lgraph = connectLayers(lgraph,"leakyrelu33","res14/in1");
lgraph = connectLayers(lgraph,"res14","conv34");
lgraph = connectLayers(lgraph,"res14","res15/in2");
lgraph = connectLayers(lgraph,"leakyrelu35","res15/in1");
lgraph = connectLayers(lgraph,"res15","conv36");
lgraph = connectLayers(lgraph,"res15","res16/in2");
lgraph = connectLayers(lgraph,"leakyrelu37","res16/in1");
lgraph = connectLayers(lgraph,"res16","conv38");
lgraph = connectLayers(lgraph,"res16","res17/in2");
lgraph = connectLayers(lgraph,"leakyrelu39","res17/in1");
lgraph = connectLayers(lgraph,"res17","conv40");
lgraph = connectLayers(lgraph,"res17","res18/in2");
lgraph = connectLayers(lgraph,"leakyrelu41","res18/in1");
lgraph = connectLayers(lgraph,"res18","conv42");
lgraph = connectLayers(lgraph,"res18","res19/in2");
lgraph = connectLayers(lgraph,"leakyrelu43","res19/in1");
lgraph = connectLayers(lgraph,"leakyrelu44","conv45");
lgraph = connectLayers(lgraph,"leakyrelu44","res20/in2");
lgraph = connectLayers(lgraph,"leakyrelu46","res20/in1");
lgraph = connectLayers(lgraph,"res20","conv47");
lgraph = connectLayers(lgraph,"res20","res21/in2");
lgraph = connectLayers(lgraph,"leakyrelu48","res21/in1");
lgraph = connectLayers(lgraph,"res21","conv49");
lgraph = connectLayers(lgraph,"res21","res22/in2");
lgraph = connectLayers(lgraph,"leakyrelu50","res22/in1");
lgraph = connectLayers(lgraph,"res22","conv51");
lgraph = connectLayers(lgraph,"res22","res23/in2");
lgraph = connectLayers(lgraph,"leakyrelu52","res23/in1");
%% Train Network
% Train the network using the specified options and training data.
[net, traininfo] = trainNetwork(augimdsTrain,lgraph,opts);
%% Test Trained Network
%Select a new image to classify using the trained network.
I = imread('NEWFLIRPERSON1.jpg');
%Resize the test image to match the network input size.
%scale = [256 256];
I = imresize(I, [256 256]);
[YPred,probs] = classify(net,I);
gray = rgb2gray(I);
%imshow(gray)
img = imbinarize(gray,0.5);
%imshow(img)
img = bwareaopen(img,100);
%img = ~img_bin;
[bwLabel, num] = bwlabel(img);
bboxes = regionprops(bwLabel,'BoundingBox');
imshow(I)
label = YPred;
title(string(label) + ", " + num2str(100*max(probs),3) + "%");
hold on
for k = 1 : length(bboxes)
CurrBB = bboxes(k).BoundingBox;
rectangle('Position',[CurrBB(1),CurrBB(2),CurrBB(3),CurrBB(4)],'Edgecolor','y','Linewidth',2);
end
%rectangle('Position',bboxes.BoundingBox,'Edgecolor','y','Linewidth',2);
hold off
%}
Walter Roberson
Walter Roberson 2023-1-17
trainingSetup = load("C:\Users\Neel\Documents\MATLAB\YOLOv3ObjectDetection\DarkNet53\trainednetwork.mat");
%% Import Data
% Import training and validation data.
imdsTrain = imageDatastore("C:\Users\Neel\Documents\MATLAB\YOLOv3ObjectDetection\DarkNet53\Dataset1","IncludeSubfolders",true,"LabelSource","foldernames");
We do not have that .mat file or those training directories.
Partha Dey
Partha Dey 2023-1-18
Hello Sir.
This is .mat file which I trained using Deep Network Designer using Darknet53 and after the training I had exported it for the detection using this file.
For Deep Network Designer it will generate a live script after the training, that code I took and used again for my dataset, which worked before but now I am getting this function error while trying to test images for detection.
Some parameters or something I am missing with the function. Kindly check and assist me on this.
Thank you.
Walter Roberson
Walter Roberson 2023-1-18
I ran your code on my computer. It told me that it could not find the mat file on my computer.
Without those files I cannot test the code on my computer and debug the problem.
I can make guesses about what is wrong but they might be on the wrong track completely.
Partha Dey
Partha Dey 2023-1-18
Alright Sir..
I will compress the .mat file and create one web link for that. I will post it here once it is done..
Thank you..
Partha Dey
Partha Dey 2023-1-27
Dear Sir, (@Walter Roberson, @Image Analyst)
Any further update on my request please?
Thank you very much in advance.
Walter Roberson
Walter Roberson 2023-1-29
trainingSetup = load("C:\Users\Neel\Documents\MATLAB\YOLOv3ObjectDetection\DarkNet53\trainednetwork.mat");
The result of load() of a .mat file, is a struct that has one field for each variable loaded from the file.
That particular file only has one variable, trainedNetwork_2
tempLayers = [imageInputLayer([256 256 3],"Name","input","Normalization","rescale-zero-one","Max",trainingSetup.input.Max,"Min",trainingSetup.input.Min)
convolution2dLayer([3 3],32,"Name","conv1","Padding","same","Bias",trainingSetup.conv1.Bias,"Weights",trainingSetup.conv1.Weights)
batchNormalizationLayer("Name","batchnorm1","Offset",trainingSetup.batchnorm1.Offset,"Scale",trainingSetup.batchnorm1.Scale,"TrainedMean",trainingSetup.batchnorm1.TrainedMean,"TrainedVariance",trainingSetup.batchnorm1.TrainedVariance)
The .mat file does not contain any variable named input or conv1 or batchnorm1 .
The DAGNetwork object trainedNetwork_2 does not contain fields or properties with those names either.
Partha Dey
Partha Dey 2023-1-30
Alright Sir. Because after generating the live script using Deep Network Designer it was able to run and detect.
Can you suggest how to fix this code please? Thank you very much in advance.

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