To my understanding, you are getting error in matlab while using both CNN and SVM for classification.
The error is mainly arising in the line:
cvpt = cvpartition(featuresTrain,"KFold",5)
In 'cvpartition' function, the first argument should correspond to number of observations in the sample data but you are passing featuresTrain which causes an error. Instead pass the number of observations which can be done by:
cvpt = cvpartition(augimdsTrain.NumObservations,"KFold",5)
So the whole code can be rewritten as:
imds = imageDatastore('MerchData', 'IncludeSubfolders',true, 'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);
testnet = resnet18
inputSize = testnet.Layers(1).InputSize
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain)
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation)
layer = 'pool5';
featuresTrain = activations(testnet,augimdsTrain,layer,'OutputAs','rows')
featuresTest = activations(testnet,augimdsValidation,layer,'OutputAs','rows');
whos featuresTrain
YTrain = imdsTrain.Labels;
YValidation = imdsValidation.Labels;
%% -- Modified code -- %%
cvpt = cvpartition(augimdsTrain.NumObservations,"KFold",5)
%% -- %%
opt = struct("CVPartition",cvpt)
classifier = fitcecoc(featuresTrain,YTrain,"OptimizeHyperparameters","auto","HyperparameterOptimizationOptions",opt);
