R-CNN detector
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I have trained Alexnet via transfer learning for my 4 categories(Bush,Branch,Tree,Rock)...How to make a R-CNN detector that i can use for my new images to detect objects within those categories?
Here is the code i used to train my network.
%% Load a pre-trained, deep, convolutional network
alex = alexnet;
layers = alex.Layers
%% Modify the network to use four categories
layers(23) = fullyConnectedLayer(4);
layers(25) = classificationLayer
%% Set up our training data
allImages = imageDatastore('myImages', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
[trainingImages, testImages] = splitEachLabel(allImages, 0.8, 'randomize');
%% Re-train the Network
opts = trainingOptions('sgdm', 'InitialLearnRate', 0.001, 'MaxEpochs', 20, 'MiniBatchSize', 64);
myNet = trainNetwork(trainingImages, layers, opts);
%% Measure network accuracy
predictedLabels = classify(myNet, testImages);
accuracy = mean(predictedLabels == testImages.Labels)
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Answers (1)
Srivardhan Gadila
on 7 Apr 2020
The total number of classes the RCNNdetector should detect will be the number of object classes you want to detect plus an additional background class.
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