R-CNN detector

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)

Answer 1

[Srivardhan Gadila]

Please refer to the following workflow: Create R-CNN Object Detection Network.

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.