Errors With Training the RCNN dectector trainRCNNobjectDetector

Hi Professionals,

I need a push here "PLEASE"!!!

I am trying to really understand what these errors mean in my code that leaves me bewildered!!

I am traning a network and the RCNN decoder bit argues about this error that is challenging me!

Can a professional Please Assist me? I really don;t get it!!

MY CODE:

clear
clc
deepNetworkDesigner
gunfolder = '/Users/mmgp/Desktop/gunsGT' 
save('gunlables.mat','gunfolder');
gTruth=('gTruth.mat');

%% Specifying Image Amount In Specified Folder
total_images = numel(gunfolder)

%% Accessing Content of Folder TrainingSet Using Datastore
imds = imageDatastore(gunfolder,'IncludeSubFolders',true,'LabelSource','Foldernames');

%% Setting Output Function(images my have size variation resizing for consistency with pretrain net)
imds.ReadFcn=@(loc)imresize(imread(loc),[227,227])

%% Counting Images In Each Category "If not equal this will create issues"
tbl=countEachLabel(imds)

%% Making Category The Same Number Of Images
minSetCount=min(tbl{:,2})

%% Splitting Inputs Into Training and Testing Sets
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized')

%% Loading Pretrained Network
net = alexnet; %Trained on 1million+ images/classify images into 1000 object categories
analyzeNetwork(net) % Display Alexnet architecture & network layer details

%% Alter InputSize Of 1st Layer/ Alexnet Image requirements is 277 width 277 height by 3 colour channels 
inputSize = net.Layers(1).InputSize %Displays the input size of Alexnet

%% Counting Total Number Of Images Including Subfolders **IF AVAILABLE**
imgTotal = length(imds.Files)

%% Displaying Multiple Randomized Images Within The Dataset
a = 4;
b = 4;
n = randperm(imgTotal, a*b);

figure(),
Idx = 1;
for j=1:a
    for k=1:b
        img=readimage(imds,n(Idx));
        subplot(a,b,Idx)
        imshow(img);
        Idx=Idx+1;
    end
end

%% Replace Final Layer/Last 3 Configure For 1000 classes
% Finetuning these 3 layers for new classification
% Extracting all Layers except the last 3
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(imdsTrain.Labels))
layers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'WeightLearnRateFactor',25,'BiasLearnRateFactor',25)
    softmaxLayer
    classificationLayer];

%% Load .MAT file, the ground truths and the Network layers
load('gimlab.mat', 'gTruth', 'net');

%% Training the network
% Resizing images in datastore to meet Alexnet's size requirements
% Utilising Augmented Data Store for automatic resizing of training images
%% Augmented Data Store Prevents Over Fitting By Randomly Flipping Along The Vertical Axis
%  Stopping the network from memorizing exact details of the training data
%  Also Randomly Translates them up to 30 pixels horizontally & Vertically
pixelRange = [-30 30];
imageAugmenter = imageDataAugmenter( ...
    'RandXReflection',true, ...
    'RandXTranslation',pixelRange, ...
    'RandYTranslation',pixelRange);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
    'DataAugmentation',imageAugmenter);

%% Utilising Data Augmentation For Resizing Validation Data
%  implemented without specifying overfit prevention procedures
%  By not specifying these procedures the system will be precise via
%  predicitons
%% Resizing Images, Assists With Preventing Overfitting 
augmentedTrainingSet = augmentedImageDatastore(inputSize ,imdsTrain,'ColorPreprocessing', 'gray2rgb')
augimdsValidation = augmentedImageDatastore(inputSize,imdsValidation,'ColorPreprocessing', 'gray2rgb')

%% Specifying Training Options
%  Keep features from earlier layers of pretrained networked for transfer learning
%  Specify epoch training cycle, the mini-batch size and validation data
%  Validate the network for each iteration during training
%  (SGDM)groups the full dataset into disjoint mini-batches This reaches convergence faster 
%  as it updates the network?s weight value more frequently increase the
%  computationl speed

opts = trainingOptions('sgdm', ...
    'Momentum', 0.9, ...
    'InitialLearnRate', 0.004, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', 0.1, ...
    'LearnRateDropPeriod', 8, ...
    'L2Regularization', 0.004, ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 128, ...
    'Verbose', true, 'Plots','training-progress');
doTraining = true;

[height,width,numChannels, ~] = size(imdsTrain);
imageSize = [height width numChannels];
inputLayer = imageInputLayer(imageSize);

%% Train the R-CNN detector. Training can take a few minutes to complete.
rcnn = trainRCNNObjectDetector(gTruth, net, opts, 'NegativeOverlapRange', [0 0.3]);

%% Training network Consisting Of Transferred & New Layers. 
netTransfer = trainNetwork(augmentedTrainingSet,layers,opts);

%% Classifying Validation Images Utilising Fine-tuned Network
[YPred,scores] = classify(netTransfer,augimdsValidation);

%% Displaying 4 Validation Image Samples With Predicted Labels
idx = randperm(numel(imdsValidation.Files),4);
figure
for i = 1:4
    subplot(2,2,i)
    I = readimage(imdsValidation,idx(i));
    imshow(I)
    label = YPred(idx(i));
    title(string(label));
end

%% Calculating Validation Data Classification Accuracy (Accuracy Labels Predicted Accurately By Network)
YValidation = imdsValidation.Labels;
accuracy = mean(YPred == YValidation)

MY ERRORS

Error using trainRCNNObjectDetector
Expected input number 1, trainingData, to be one of these types:

table

Error in vision.internal.cnn.validation.checkGroundTruth (line 2)
validateattributes(gt, {'table'},{'nonempty'}, name, 'trainingData',1);

Error in trainRCNNObjectDetector>parseInputs (line 311)
vision.internal.cnn.validation.checkGroundTruth(trainingData, fname);

Error in trainRCNNObjectDetector (line 248)
[network, params] = parseInputs(trainingData, network, options, mfilename, varargin{:});

Error in guntest2 (line 117)
rcnn = trainRCNNObjectDetector(gTruth, net, opts, 'NegativeOverlapRange', [0 0.3]);
 
>> 

Answer 1

[Raunak Gupta]

Hi,

I think this is a similar question that is posted here. You may find the answer mentioned to that question useful.