Error forming mini-batch for network input

i want to train a cnn with a folder of images with the size [h w c], the imageInputLayerargument is the same size of the images (h w c), but when training the network matlab says:

Error using trainnet (line 46)

Error forming mini-batch for network input "imageinput". Data interpreted with format "SSCB". To specify a

different format, use the InputDataFormats option.

Caused by:

Dimensions of arrays being concatenated are not consistent.

[Matt J]

We c have no way of knowing what you did. Please attach materials needed to reproduce it.

[Walid]

Thanks for the replay, this is my code for multiclass (3 classes) classification using CNN based on image files stored in three subfolders in the main folder: data.:

clear all; close all; clc
% Define the path to your main data folder
dataFolder = 'C:\data';
% Create an image datastore
imds = imageDatastore(dataFolder, ...
    'IncludeSubfolders', true, ...
    'LabelSource', 'foldernames');
% View the class names and the number of images per class
%imds=aug_imds;
labelCount = countEachLabel(imds);
disp(labelCount);
% Split the datastore into training and validation sets (e.g., 70% for training, 30% for validation)
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.7, 'randomized');
inputSize = [570 714 3];
augimdsT = augmentedImageDatastore(inputSize,imdsTrain,'ColorPreprocessing','rgb2gray');
augimdsV = augmentedImageDatastore(inputSize,imdsValidation,'ColorPreprocessing','rgb2gray');
numClasses = 3;
layers = [
    imageInputLayer(inputSize)
    convolution2dLayer(5,5)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer];
%
options = trainingOptions("sgdm", ...
    MaxEpochs=4, ...
    ValidationData=imdsValidation, ...
    ValidationFrequency=30, ...
    Plots="training-progress", ...
    Metrics="accuracy", ...
    Verbose=false);
% train
net = trainnet(augimdsT,layers,"crossentropy",options);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

I receive the following message:

Error forming validation data mini-batch.

Caused by:

Error forming mini-batch for network input "imageinput". Data interpreted with format "SSCB". To specify

a different format, use the InputDataFormats option.

Dimensions of arrays being concatenated are not consistent.

Answer 1

[Matt J]

This is not enough to reproduce the error. You haven't provided input images. My guess, however, is that there are some files in C:\data that are not 570x714x3.

[Walid]

Hi, the is the download link of the data folder: data

[Walid]

there is some image with different size, so i used augmentedImageDatastore function to resize them to a unique size !

[Walid]

i uploaded the files in matlab drive: data in matlab drive

[Matt J]

You need,

inputSize = [570 714 1];

and

ValidationData=augimdsV,...

[Walid]

Thank you for the response,

performing the code, Matlab return this error:

Error using trainnet (line 46)

Unable to apply function specified by 'MiniBatchFcn' value.

Error in march (line 47)

net = trainnet(imdsTrain,layers,"crossentropy",options);

Caused by:

Error using deep.internal.train.createMiniBatch (line 22)

Error forming mini-batch for network input "imageinput". Data interpreted with format "SSCB". To specify

a different format, use the InputDataFormats option.

Dimensions of arrays being concatenated are not consistent.

my code is attached. in this Link