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Oversampling for deep learning: classification example

version 2.0 (405 KB) by Kenta
This example show how to classify images with imbalanced training dataset where the number of images per class is different over classes. 深

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Updated 28 Jul 2021

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Image classification using oversampling imagedatastore

[English]

This example shows how to classify images with imbalanced training dataset where the number of images per class is different over classes. Two of the most popular solutions are down-sampling and over-sampling. In down-sampling, the number of images per class is reduced to the minimal number of images among all classes. On the other hand, the number of images per class is increased when performing over-sampling. Both strategies are effective for imbalanced datasets. Implementation of down-sampling is easy: just use splitEachLabel function and specify the minimal number over the classes, however, over-sampling requires more complicated processes.

 This code does not use transform datastore that cannot be associated with augmented datastore. It means, it becomes difficult to perform data augmentation with transform datastore as of 2020a. 

[Japanese]

このスクリプトでは、オーバーサンプリングと呼ばれる手法を用いて、訓練データ内での各クラスの画像の枚数を均一にします。深層学習による画像分類では、分類するクラスの訓練データの枚数がクラス間でばらつきがあると学習が難しくなります。クラス間で枚数が不均衡になってしまう場合の対策として有名なもので、オーバーサンプリングやダウンサンプリングと呼ばれるものがあります。ダウンサンプリングでは、各クラスで最も少ない枚数にあわせ、多い画像は利用しないもので、MATLABでのコーディングは比較的簡単です。一方、オーバーサンプリングの場合は、少し実装が複雑になります。この例ではオーバーサンプリングによる画像分類の例を示します。

image_0.png

Load data

Please download Food image dataset provided from MathWorks. The Example Food Images data set contains 978 photographs of food in nine classes (ceaser_salad, caprese_salad, french_fries, greek_salad, hamburger, hot_dog, pizza, sashimi, and sushi).

Other dataset is available at https://jp.mathworks.com/help/deeplearning/ug/data-sets-for-deep-learning.html.

clear;clc;close all
url = "https://www.mathworks.com/supportfiles/nnet/data/ExampleFoodImageDataset.zip";
downloadFolder = pwd;
filename = fullfile(downloadFolder,'ExampleFoodImageDataset.zip');

dataFolder = fullfile(downloadFolder, "ExampleFoodImageDataset");
if ~exist('ExampleFoodImageDataset.zip')
    fprintf("Downloading Example Food Image data set (77 MB)... ")
    websave(filename,url);
    unzip(filename,downloadFolder);
    fprintf("Done.\n")
end

imds=imageDatastore('myimages', ...
    'IncludeSubfolders',true,'LabelSource','foldernames');
[imdsTrain, imdsValid,imdsTest]=splitEachLabel(imds,0.8,0.1);

Confirm the imbalance

Note that the number of images among the classes is imbalanced.

labelCount = countEachLabel(imdsTrain)
Label Count
1 caesar_salad 21
2 caprese_salad 12
3 french_fries 145
4 greek_salad 19
5 hamburger 190
6 hot_dog 25
7 pizza 239
8 sashimi 32
9 sushi 99

I refferred to https://jp.mathworks.com/help/vision/examples/point-cloud-classification-using-pointnet-deep-learning.html for this section.

histogram(imdsTrain.Labels)
labels=imdsTrain.Labels;
[G,classes] = findgroups(labels);
numObservations = splitapply(@numel,labels,G);

desiredNumObservationsPerClass is the maximum number of sample among all classes.

desiredNumObservationsPerClass = max(numObservations);

randReplicateFiles is a supporting function just shuffling the files. The number of images to select is difined by desiredNumObservationsPerClass. Then, the files are randomly extracted from imdsTrain.Files.

files = splitapply(@(x){randReplicateFiles(x,desiredNumObservationsPerClass)},imdsTrain.Files,G);
files = vertcat(files{:});
labels=[];info=strfind(files,'\');
for i=1:numel(files)
    idx=info{i};
    dirName=files{i};
    targetStr=dirName(idx(end-1)+1:idx(end)-1);
    targetStr2=cellstr(targetStr);
    labels=[labels;categorical(targetStr2)];
end
imdsTrain.Files = files;
imdsTrain.Labels=labels;
labelCount_oversampled = countEachLabel(imdsTrain)
Label Count
1 caesar_salad 239
2 caprese_salad 239
3 french_fries 239
4 greek_salad 239
5 hamburger 239
6 hot_dog 239
7 pizza 239
8 sashimi 239
9 sushi 239
histogram(imdsTrain.Labels)

Load the pre-trained model, ResNet-18

net = resnet18;
inputSize = net.Layers(1).InputSize;
lgraph = layerGraph(net);
learnableLayer='fc1000';
classLayer='ClassificationLayer_predictions';

Modify the network for the current task

numClasses = numel(categories(imds.Labels));
newLearnableLayer = fullyConnectedLayer(numClasses, ...
        'Name','new_fc', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
lgraph = replaceLayer(lgraph,learnableLayer,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,classLayer,newClassLayer);

Define image augmenter

pixelRange = [-30 30];
RotationRange = [-30 30];
scaleRange = [0.8 1.2];
imageAugmenter = imageDataAugmenter( ...
    'RandXReflection',true, ...
    'RandXTranslation',pixelRange, ...
    'RandYTranslation',pixelRange, ...
    'RandXScale',scaleRange, ...
    'RandYScale',scaleRange, ...
    'RandRotation',RotationRange ...
    ); 
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
     'DataAugmentation',imageAugmenter);
augimdsValid = augmentedImageDatastore(inputSize(1:2),imdsValid);
augimdsTest = augmentedImageDatastore(inputSize(1:2),imdsTest);

Specify the training options

miniBatchSize = 64;
valFrequency = max(floor(numel(augimdsTest.Files)/miniBatchSize)*10,1);
options = trainingOptions('sgdm', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',30, ...
    'InitialLearnRate',3e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',augimdsValid, ...
    'ValidationFrequency',valFrequency, ...
    'Verbose',false, ...
    'Plots','training-progress');

Train the network

net = trainNetwork(augimdsTrain,lgraph,options);

figure_0.png

Classification assessment

[YPred,probs] = classify(net,augimdsTest);
accuracy = mean(YPred == imdsTest.Labels)
accuracy = 0.9072
YValidation = imdsTest.Labels;
YTrue=imdsTest.Labels;
figure;cm=confusionchart(YTrue,YPred);

figure_1.png

When I run this code, the main mis-classification was made between sashimi and sushi, which look similar. Please try this code for over-sampling and hope it helps your work.

Supporting function

This sub-function randomly replicate the image directry for each class. When the target class is A, the image directry of image A was found from imds.Files and the image directry was copied in order to balance the number of images over the classes.

function files = randReplicateFiles(files,numDesired)
n = numel(files);
ind = randi(n,numDesired,1);
files = files(ind);
end

Cite As

Kenta (2021). Oversampling for deep learning: classification example (https://github.com/KentaItakura/Image-classification-using-oversampling-imagedatastore/releases/tag/2.0), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2020a
Compatible with any release
Platform Compatibility
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.