Griffon Thomas asked . 2022-04-19

Improper initialization of classification layer in rcnn

load 'D:\Documents\MATLAB\bridgeLabels.mat', 'gTruth';
%these are the labels I made in the image labeler app

trainingData = objectDetectorTrainingData(gTruth);
%this apparently makes the training data for me

layers = [imageInputLayer([2160 3840 3])
        convolution2dLayer([5 5],10)
        reluLayer()
        fullyConnectedLayer(10)
        softmaxLayer()
        classificationLayer()];
%I understand what all these things do, kind of. 
%I just copied this code from the demonstration in the reference
%I'm getting some error with the classification layer I don't know how to fix    

options = trainingOptions('sgdm',...
    'LearnRateSchedule','piecewise',...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'MaxEpochs',20,...
    'MiniBatchSize',64,...
    'Plots','training-progress');
%again, most of this makes sense to me

detector = trainRCNNObjectDetector(trainingData, layers, options);
%ok so now the network is made apparently

image = imread('D:\Documents\MATLAB\clubroot_shots\lcbo1.png');
%this is my testing image

wid = 10;
rois = zeros(1, (image.width/wid)*(image.height/wid));

for i=1:image.width/wid
    for j=1:image.height/wid
        rois(i+j*width) = [1+(i-1)*wid, 1+(j-1)*wid, wid, wid];
    end
end
%I believe this code will split up the image into 10x10 regions of interest. 
%I wrote this block myself.

classifyRegions(detector, image, rois)
%and here the regions get classified. Semicolon off because i want to see what happens

When I run this code, I get the following errors:

Error using vision.internal.cnn.validation.checkNetworkClassificationLayer (line 9)
The number object classes in the network classification layer must be equal to the number of classes
defined in the input trainingData plus 1 for the "Background" class.

Error in vision.internal.rcnn.parseInputs (line 35)
    vision.internal.cnn.validation.checkNetworkClassificationLayer(network, trainingData);

Error in trainRCNNObjectDetector (line 185)
params = vision.internal.rcnn.parseInputs(trainingData, network, options, mfilename, varargin{:});

Error in imagenn (line 20)
detector = trainRCNNObjectDetector(trainingData, layers, options);

Error in run (line 91)
evalin('caller', strcat(script, ';'));

AI, Data Science, and Statistics , Deep Learning Toolbox , Deep Learning with Images , cnn , rcn