Size of an input image in Object detection using AlexNet transfer Learning

Hi I am trying to use transfer learning using Alexnet in an example "Object Detection Using Deep Learning" Below is the modification that I have done cifar10Net = alexnet; doTraining = True; if doTraining

    % Set training options
    options = trainingOptions('sgdm', ...
        'MiniBatchSize', 128, ...
        'InitialLearnRate', 1e-3, ...
        'LearnRateSchedule', 'piecewise', ...
        'LearnRateDropFactor', 0.1, ...
        'LearnRateDropPeriod', 100, ...
        'MaxEpochs', 100, ...
        'Verbose', true);

    % Train an R-CNN object detector. This will take several minutes.    
    rcnnStopNet= trainRCNNObjectDetector(stopSigns, cifar10Net, options, ...
    'NegativeOverlapRange', [0 0.3], 'PositiveOverlapRange',[0.5 1])
     save rcnnStopNet;
else
    % Load pre-trained network for the example.
    load('rcnnStopNet.mat','rcnn')     
end
For same set of images (Stop Signs) ,Training happens successfully but results are less accurate when using original Cifar10 net.
1) Am I doing something wrong. 
2) Do we need to resize STOP images to 227,227 when using Alexnet for transfer learning.

Answer 1

[Prajit T R]

Hi Hanish

Alexnet was trained using images of size 227,227 so you need to resize your training images using the 'imresize' function.

As for accuracy, 'alexnet' is a pre-trained network which may not be accurate for your specific use-case so you may have to perform some fine tuning with respect to the training parameters.

Prajit