load('gTruth.mat')
smokedetection = selectLabels(gTruth,'car');
if isfolder(fullfile('TrainingData'))
cd TrainingData
else
mkdir TrainingData
end
addpath('TrainingData');
options = trainingOptions('sgdm', ...
'MiniBatchSize', 32, ...
'InitialLearnRate', 1e-6, ...
'MaxEpochs', 10);
layers = [
imageInputLayer([32 32 3],"Name","imageinput")
convolution2dLayer([5 5],32,"Name","conv","BiasLearnRateFactor",2,"Padding",[2 2 2 2],"WeightsInitializer","narrow-normal")
maxPooling2dLayer([3 3],"Name","maxpool","Stride",[2 2])
reluLayer("Name","relu")
averagePooling2dLayer([3 3],"Name","avgpool","Stride",[2 2])
fullyConnectedLayer(2,"Name","fc_rcnn","BiasL2Factor",1,"BiasLearnRateFactor",10,"WeightLearnRateFactor",20,"WeightsInitializer","narrow-normal")
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")];
trainingData = objectDetectorTrainingData(smokedetection,'SamplingFactor',1,...
'WriteLocation','TrainingData');
detector = trainRCNNObjectDetector(trainingData, layers, options, ...
'NegativeOverlapRange', [0 0.3]);
save('Detector.mat','detector');
[detector,info] = trainRCNNObjectDetector('Epoch','Mini-batch Loss','Training Accuracy','Base Learning Rate','Mini-batch Accuracy');
x = ('Epoch');
y = ('Mini-batch Loss');
figure
plot(x,y)
title('Training Phase')
xlabel('Number of Epochs')
ylabel('Training Loss')
Error using trainRCNNObjectDetector
Expected input number 1, trainingData, to be one of these types:
table
Error in vision.internal.cnn.validation.checkGroundTruth (line 2)
validateattributes(gt, {'table'},{'nonempty'}, name, 'trainingData',1);
Error in trainRCNNObjectDetector>parseInputs (line 311)
vision.internal.cnn.validation.checkGroundTruth(trainingData, fname);
Error in trainRCNNObjectDetector (line 248)
[network, params] = parseInputs(trainingData, network, options, mfilename, varargin{:});
Error in TrainingSmokeDetectionwithRCNN (line 29)
[detector,info] = trainRCNNObjectDetector('Epoch','Mini-batch Loss','Training Accuracy','Base
Learning Rate','Mini-batch Accuracy');
