Issues with Faster R-CNN regarding Input Image Size, Mini-Batch Loss (NaN value) and another error.

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Lathi20 on 12 Nov 2020
Edited: ramin takmil on 22 Feb 2022
Hello Everyone,
I am trying to use Faster R-CNN for an object detection task. My code is based on the info in the following link:
I had several issues as follow:
1) The size of the images that I have is 512x640x1 (jpg format) and the size of the object vary from 3x3x1 to 10x10x1. The input layer in the ResNet-50 is 224x224x3. My question is how to change the input layer size of ResNet-50 to match the size of my input images?
2) What is the reason to get (NaN value) for the (Mini-batch Loss) and (empty column) for (Mini-batch Accuracy) and (Mini-batch RMSE)?
Knowing that I also decreased the 'InitialLearnRate' from 1e-3 to 1e-5 as suggested in one of answers in other questions, but still the issue has not been solved. (Image Attached below)
3) If I rescale the input images to 224x244x3 Using the code below, I got the following error. What is the reason of this error (Image attached below)?
ERROR: Undefined function 'categories' for input arguments of type 'double'.
% First, specify the network input size.
inputSize = [224 224 3];
%inputSize = [512 640 3];
% Estimate anchor boxes based on the size of objects in the training data.
preprocessedTrainingData = transform(trainingData, @(data)preprocessData(data,inputSize));
numAnchors = 3;
anchorBoxes = estimateAnchorBoxes(preprocessedTrainingData,numAnchors)
% load a pretrained ResNet-50 model.
featureExtractionNetwork = resnet50;
% This feature extraction layer outputs feature maps that are downsampled by a factor of 16.
featureLayer = 'activation_40_relu';
% analyzeNetwork(resnet50)
% Define the number of classes to detect.
numClasses = width(vehicleDataset)-1;
% Create the Faster R-CNN object detection network.
lgraph = fasterRCNNLayers(inputSize,numClasses,anchorBoxes,featureExtractionNetwork,featureLayer);
Thank you so much for your help in adavnce
  1 Comment
ramin takmil
ramin takmil on 22 Feb 2022
hello I have this problem too
my input size is [512 512 1] but resnet-50 input is [224 224 1] so when I run the code it gives me this error : Input size mismatch. Size of input to this layer [512 512 1] is different from the expected input size [512 512 3].
I have tried to change the input layer using 'replaceLayer' function but it gives me error when I want to build "lgraph"
how did you solve the input size problem???
thanks in advance

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Accepted Answer

Raynier Suresh
Raynier Suresh on 20 Jan 2021
To change the input layer size, you can replace the layer either in MATLAB Terminal using ‘replaceLayer’ function or in Deep Network Designer App.
Refer the below links for more information:
The function categories will output the number of categories in a categorical array¸ I think that the training data might be improper, this could also be the reason for NaN’s in the Mini-Batch Loss.

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