To perform k-fold cross-validation with an image datastore in MATLAB, you can manually split the data for each fold. The 'splitEachLabel' function doesn't directly support k-fold cross-validation, but you can implement it by iterating over the number of folds and creating training and validation sets for each fold. Here's a general approach to achieve this:
- Decide on the number of folds k you want to use for cross-validation.
- Randomly shuffle and partition the data into k folds.
- For each fold, designate one part as the validation set and the rest as the training set. The ‘setdiff’ and ‘subset’ functions can be used for finding the training indices by excluding validation indices and partitioning the data into training and validation sets, respectively. You may refer to the following documentation links for more insights:
- https://www.mathworks.com/help/matlab/ref/double.setdiff.html
- https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.imagedatastore.subset.html
Here's a sample code to illustrate this process:
% Define the number of folds
k = 5;
% Get the number of images
numImages = numel(Images.Files);
% Shuffle the indices
indices = randperm(numImages);
% Calculate the number of images per fold
numImagesPerFold = floor(numImages / k);
% Iterate over each fold
for fold = 1:k
% Determine the indices for the validation set
valStart = (fold - 1) * numImagesPerFold + 1;
if fold == k
valEnd = numImages; % Include all remaining images in the last fold
else
valEnd = fold * numImagesPerFold;
end
valIndices = indices(valStart:valEnd);
% Determine the indices for the training set
trainIndices = setdiff(indices, valIndices);
% Create the training and validation datastores
trainDatastore = subset(Images, trainIndices);
valDatastore = subset(Images, valIndices);
% (Optional) Create a test datastore if needed
% testDatastore = ...; % Define your test dataset separately if needed
% Use these datastores to train and validate your CNN
% For example:
% net = trainNetwork(trainDatastore, layers, options);
% valResults = classify(net, valDatastore);
% Compute accuracy or other metrics
end
Hope this helps!
