Hi all, how to create image datasets. I need them to train neural networks. I have about 15 to 20 images and I need to turn these images into an image dataset. Please.

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Nurul Farhana Mohd Fadzli
Nurul Farhana Mohd Fadzli on 16 May 2022
Commented: yanqi liu on 20 May 2022
I have tried to find the way to build image dataset but all of the example are using Python. But i want to use Matlab. Please help me.

Answers (2)

Abhijit Bhattacharjee
Abhijit Bhattacharjee on 19 May 2022
This is easy to do in MATLAB! You can put all your images into a folder and use the imageDatastore command.
Assuming the folder of images is on the path, here is an example:
imds = imageDatastore("name_of_image_folder");
Abhijit Bhattacharjee
Abhijit Bhattacharjee on 19 May 2022
What you do next depends on your application. In your original question, you asked what you need to make a dataset. The code I provided should be sufficient for that.

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yanqi liu
yanqi liu on 20 May 2022
yes,sir,may be use cnn transfer to train model,such as
% use image folder to get dataset
imds = imageDatastore('MerchData','IncludeSubfolders',true,'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
% use Alexnet to get cnn model
alex_net = alexnet;
class_number = length(unique(imds.Labels));
alex_net_share = alex_net.Layers(1:end-3);
alex_net_add = [
fullyConnectedLayer(class_number,'Name','fc8','WeightLearnRateFactor',10, 'BiasLearnRateFactor',20)
layers_1 = [alex_net_share
% train
augimdsTrain = augmentedImageDatastore([227 227],imdsTrain);
augimdsValidation = augmentedImageDatastore([227 227],imdsValidation);
miniBatchSize = 10;
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',miniBatchSize, ...
'MaxEpochs',5, ...
'InitialLearnRate',3e-4, ...
'Shuffle','every-epoch', ...
'ValidationData',augimdsValidation, ...
'ValidationFrequency',valFrequency, ...
trainedNet = trainNetwork(augimdsTrain,layers_1,options);
% test
[YPred,probs] = classify(trainedNet,augimdsValidation);
accuracy = mean(YPred == imdsValidation.Labels)
accuracy = 1
% app
idx = randperm(numel(imdsValidation.Files),4);
for i = 1:4
I = readimage(imdsValidation,idx(i));
label = YPred(idx(i));
title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),3) + "%");
yanqi liu
yanqi liu on 20 May 2022
yes,sir,let us check the folder MerchData,we can find that one subfolder is one class,so if use our data,we can just make a new subfolder, and use name as subfolder name
then put images in it,and run code

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