how do i create a dataset for rcnn object detection and i need help on Rcnn object detection i.e how does it work line by line.

data = load('fasterRCNNVehicleTrainingData.mat'); vehicleDataset = data.vehicleTrainingData; vehicleDataset(1:4,:)

% Add fullpath to the local vehicle data folder. dataDir = fullfile(toolboxdir('vision'),'visiondata'); vehicleDataset.imageFilename = fullfile(dataDir, vehicleDataset.imageFilename);

% Read one of the images. I = imread(vehicleDataset.imageFilename{10});

% Insert the ROI labels. I = insertShape(I, 'Rectangle', vehicleDataset.vehicle{10});

% Resize and display image. I = imresize(I, 3); figure imshow(I)

% Split data into a training and test set. idx = floor(0.6 * height(vehicleDataset)); trainingData = vehicleDataset(1:idx,:); testData = vehicleDataset(idx:end,:);

% Create image input layer. inputLayer = imageInputLayer([32 32 3]);

% Define the convolutional layer parameters. filterSize = [3 3]; numFilters = 32;

% Create the middle layers. middleLayers = [

    convolution2dLayer(filterSize, numFilters, 'Padding', 1)
    reluLayer()
    convolution2dLayer(filterSize, numFilters, 'Padding', 1)
    reluLayer()
    maxPooling2dLayer(3, 'Stride',2)

    ];

finalLayers = [

    % Add a fully connected layer with 64 output neurons. The output size
    % of this layer will be an array with a length of 64.
    fullyConnectedLayer(64)

    % Add a ReLU non-linearity.
    reluLayer()

    % Add the last fully connected layer. At this point, the network must
    % produce outputs that can be used to measure whether the input image
    % belongs to one of the object classes or background. This measurement
    % is made using the subsequent loss layers.
    fullyConnectedLayer(width(vehicleDataset))

    % Add the softmax loss layer and classification layer.
    softmaxLayer()
    classificationLayer()
];

layers = [ inputLayer middleLayers finalLayers ]

%Options for step 1. optionsStage1 = trainingOptions('sgdm', ... 'MaxEpochs', 10, ... 'InitialLearnRate', 1e-5, ... 'CheckpointPath', tempdir);

% Options for step 2. optionsStage2 = trainingOptions('sgdm', ... 'MaxEpochs', 10, ... 'InitialLearnRate', 1e-5, ... 'CheckpointPath', tempdir);

% Options for step 3. optionsStage3 = trainingOptions('sgdm', ... 'MaxEpochs', 10, ... 'InitialLearnRate', 1e-6, ... 'CheckpointPath', tempdir);

% Options for step 4. optionsStage4 = trainingOptions('sgdm', ... 'MaxEpochs', 10, ... 'InitialLearnRate', 1e-6, ... 'CheckpointPath', tempdir);

options = [ optionsStage1 optionsStage2 optionsStage3 optionsStage4 ];

% A trained network is loaded from disk to save time when running the % example. Set this flag to true to train the network. doTrainingAndEval = false;

if doTrainingAndEval % Set random seed to ensure example training reproducibility. rng(0);

    % Train Faster R-CNN detector. Select a BoxPyramidScale of 1.2 to allow
    % for finer resolution for multiscale object detection.
    detector = trainFasterRCNNObjectDetector(trainingData, layers, options, ...
        'NegativeOverlapRange', [0 0.3], ...
        'PositiveOverlapRange', [0.6 1], ...
        'BoxPyramidScale', 1.2);
else
    % Load pretrained detector for the example.
    detector = data.detector;
end

% Read a test image. I = imread('F:\Aditya\download.jpg');

% Run the detector. [bboxes, scores] = detect(detector, I);

% Annotate detections in the image. I = insertObjectAnnotation(I, 'rectangle', bboxes, scores); figure imshow(I)