Convolutional Neural Network for traffic signs classification.
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Hello,
I want to create traffic sign classifier with CNN based on dataset from GTSRB. I made my net only for 11 classes. When I try to classify picture of sign which is in my dataset then I have almost everytime 100% accuracy, but when I try to cfassily sign which isn't in my dataset ( for example speed limit 20 km/h image downloaded from google ) the prediction is mostly incorrect.
How can I improve my network ?
Dataset : https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html
Code :
clc;clear;
DatasetPath = fullfile('C:\Users\Pulpit\Splotowe Sieci Neuronowe\GTSRB\fl');
Data = imageDatastore(DatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(Data,0.7,'randomize');
imageAugmenter = imageDataAugmenter( ...
'RandRotation',[-20,20], ...
'RandXTranslation',[-1 1], ...
'RandYTranslation',[-1 1], ...
'RandXReflection', false, ...
'RandYReflection', true ...
);
imageSize = [32 32 3];
augimdsTrain = augmentedImageDatastore(imageSize,imdsTrain,'DataAugmentation',imageAugmenter);
augimdsValidation = augmentedImageDatastore(imageSize(1:2),imdsValidation);
%%
layers = [
imageInputLayer([32 32 3])
convolution2dLayer(3,64,'Stride' ,1,'Padding','same' )
convolution2dLayer(3,64,'Stride' ,1,'Padding','same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(1, 'Stride', 1);
convolution2dLayer(3,64,'Stride' ,1,'Padding','same' )
convolution2dLayer(3,64,'Stride' ,1,'Padding','same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(1, 'Stride', 1);
convolution2dLayer(3,128,'Stride' ,1,'Padding','same' )
convolution2dLayer(3,128,'Stride' ,1,'Padding','same' )
convolution2dLayer(3,128,'Stride' ,1,'Padding','same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(1, 'Stride', 1);
convolution2dLayer(3,256,'Stride' ,1,'Padding','same' )
convolution2dLayer(3,256,'Stride' ,1,'Padding','same' )
convolution2dLayer(3,256,'Stride' ,1,'Padding','same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(1, 'Stride', 1);
fullyConnectedLayer(64)
fullyConnectedLayer(32)
fullyConnectedLayer(11)
softmaxLayer
classificationLayer];
%miniBatchSize = 32;
%valFrequency = floor(numel(imdsValidation.Files)/miniBatchSize);
%%
%{
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',5, ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',valFrequency, ...
'InitialLearnRate',0.001, ...
'MaxEpochs',10, ...
'MiniBatchSize',miniBatchSize, ...
'Plots','training-progress');
%}
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',6, ...
'InitialLearnRate',1e-4, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',3, ...
'Verbose',false, ...
'Plots','training-progress');
%%
convnet = trainNetwork(augimdsTrain,layers,options);
%%
YTest = imdsValidation.Labels;
[YPred,prob] = classify(convnet,augimdsValidation);
accuracy = sum(YPred == YTest)/numel(YTest)
plotconfusion(YTest,YPred)
idx = randperm(numel(imdsValidation.Files),4);
figure
for i = 1:4
subplot(2,2,i)
I = readimage(imdsValidation,idx(i));
imshow(I)
label = YPred(idx(i));
title(string(label) + ", " + num2str(100*max(prob(idx(i),:)),3) + "%");
end
2 Comments
Omran Adnanoglu
on 13 Apr 2020
Hi Krystian,
I am working on the same project, i still in the very beginning and i am facing a problem while reading the data with the given code like this :
Error using imread (line 438)
End of file reached too early.
Error in TrainTrafficSigns (line 28)
Img = imread(ImgFile);
Can you please share me the part of code where you read the data ?
Answers (1)
ZHI-RUI LIN
on 7 Jun 2022
Excuse me, i would like to ask how you loaded the GTSRB data set (ppm file) into matlab, thank you !
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