What is the best CNN for a small dataset?

Illustration
Suparna_kumar - 2021-07-06T10:20:55+00:00
Question: What is the best CNN for a small dataset?

I have a dataset of around 370 images of people, and I want to classify their expressions. Should I build my  CNN from scratch? How many hidden layers should I aim for?  

Related Questions

  • How to create an attention layer for deep learning networks?
  • Is it possible to create a CNN with real output?
  • Architecture of convolutional autoencoders in Matlab 2019b
  • What is the data "YTrain" in the Matlab CNN Regression example?
  • What is the best CNN for a small dataset?

    • Expert Answer

    Profile picture of Prashant Kumar Prashant Kumar answered . 2025-11-20

    Building a cnn from scratch isn't too hard. How many expressions are you trying to classify? With only 370 images, even with image augmentation, you won't be able to build a very big dataset, and it will be difficult to classify many different expression types. You will need a small CNN to start with, with only a couple of layers, otherwise you will get overfitting like there is no tomorrow (that is, your network will memorize all the images you gave it, rather than learning to tell the difference).
     
    Here is how I would go about doing this. Let's say you want to tell the difference between happy and sad expressions. Organize your photos so they are in two folders, one for each type of expression. You will need to put them there manually. As there are only 370 images this won't be too hard (for my project I've been having to classify over 14,000 images and I've got a lot more to do still, automating this process on more images you might come across is a topic for another time). Now, here is some basic code which will help you. Note that you will need the Neural Network Toolbox and preferably the Parallel Processing Toolbox (optional, but strongly recommended).
     
    You will either need to resize all of your images to the same resolution or use the augmentedImageDatastore to do it for you, but note that the augmentedImageDatastore is a bit slower because it has to resize images on the fly, rather than just reading them from the disk. The below example will assume you want to use the augmentedImageDatastore, but it is easy to take out if you want to.
     
    I forgot what doesn't work on R2017b vs R2018a so let me know if the below code doesn't work.
     
        % first, the hyperparameters, you will need to play with these  
train_percent = 0.90; % amount from each label to use in training, if you want to do cross validation let me know
% test perc = 1 - train
mini_batch = 256; % more is faster, limited by gpu memory if you have a gpu
max_epochs = 30;
initial_learn_rate = 0.001;
learn_rate_drop_period = 30; % reduce this if you decide you want to drop the learn rate
learn_rate_drop_factor = 0.1;
momentum = 0.9;
l2reg = 0.00001;
validation_freq = 256; % in iterations
validation_patience = 3; % early stopping
verbose = false;
augmentedResolution = [128 128]; % or whatever image resolution you want to use
inputResolution = augmentedResolution;
inputResolution(3) = 3; % color dimension, set to 1 for black/white images
layers = [imageInputLayer(inputResolution);
          convolution2dLayer(3,32,'Stride',1,'Padding',1); % first number is color dimension, second number is number of neurons/ filters to use (this can be set to whatever positive integer you want, more requiring more computations and memory and parameters - so more means greater chance of overfitting), stride and padding set to 1 ensure that the output resolution is the same as the input resolution
          batchNormalizationLayer(); % if r2017b supports it
          reluLayer();
          maxPooling2dLayer(2,'Stride',2);
          dropoutLayer(0.5); % reduces overfitting
          fullyConnectedLayer(2); % set "2" to number of classes
          softmaxLayer();
          classificationLayer()];
augmenter = imageDataAugmenter('RandRotation', [-10 10]); % optional, used to augment data, see documentation for full options
    % now getting everything in place and ready to run
datastore = imageDatastore(fullfile('.'), 'IncludeSubFolders', true, 'LabelSource', 'foldernames'); % you need to run Matlab from where you folders are located for this to work
[trainStore, validStore] = splitEachLabel(datastore, train_percent);
trainStoreAug = augmentedImageDatastore(augmentedResolution, trainStore, 'DataAugmentation', augmenter);
options = trainingOptions('sgdm', 'MiniBatchSize', mini_batch_size, ...
'LearnRateSchedule', 'piecewise', 'MaxEpochs', max_epochs, 'InitialLearnRate', initial_learn_rate, ...
'LearnRateDropPeriod', learn_rate_drop_period, ...
'LearnRateDropFactor', learn_rate_drop_factor, ...
'L2Regularization', l2reg, 'Momentum', momentum, ...
'Verbose', verbose, 'VerboseFrequency', validation_freq, ...
'ValidationFrequency', validation_freq, 'ValidationData', validStore, ...
'ValidationPatience', validation_patience, 'Plots', 'training-progess');
 convnet = trainNetwork(trainStoreAug, layers, options);


    Not satisfied with the answer ?? ASK NOW

    Get a Free Consultation or a Sample Assignment Review!

    Upload Assignment Talk to Expert