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Creating RCNN Detection Using Transfer Learning

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Hi Professionals,
can a professional guide in obtaing the layers, I keep getting this error no matter what i change!
I am lacking some knowledge required for moving forward please assist!
Attach is the label for the files I tried uploading the image folder bit but it too large sorry!
This is my code please assist in pointing me in the right direction, really would like to solve this! it's metally chanllenging when the errors are seemingly invisible!
%% Train R-CNN Stop Sign Detector
% Load training data and network layers.
load('gunx.mat', 'guntr', 'layers')
%% Add the image directory to the MATLAB path.
imDir = fullfile(matlabroot, 'toolbox', 'vision', 'visiondata','gunsGT');
addpath(imDir);
%% Set network training options to use mini-batch size of 32 to reduce
% GPU/CPU memory usage. Lower the InitialLearnRate to reduce the rate at which
% network parameters are changed. This is beneficial when fine-tuning a
% pre-trained network and prevents the network from changing too rapidly.
options = trainingOptions('sgdm','MiniBatchSize', 32,'InitialLearnRate', 1e-6,'MaxEpochs', 10);
%% Train the R-CNN detector. Training can take a few minutes to complete.
rcnn = trainRCNNObjectDetector(gunsGT, layers, options, 'NegativeOverlapRange', [0 0.3]);
%% Test the R-CNN detector on a test image.
img = imread('Gun00012.jpg');
[bbox, score, label] = detect(rcnn, img, 'MiniBatchSize', 32);
%% Display strongest detection result.
[score, idx] = max(score);
bbox = bbox(idx, :);
annotation = sprintf('%s: (Confidence = %f)', label(idx), score);
detectedImg = insertObjectAnnotation(img, 'rectangle', bbox, annotation);
figure
imshow(detectedImg)
%% Remove the image directory from the path.
rmpath(imDir);
This is my errors:
>> guntest2
Warning: Variable 'layers' not found.
> In guntest2 (line 3)
Undefined function or variable 'gunsGT'.
Error in guntest2 (line 16)
rcnn = trainRCNNObjectDetector(gunsGT, layers, options, 'NegativeOverlapRange', [0 0.3]);

Answers (1)

Shashank Gupta
Shashank Gupta on 20 Jan 2020
Hi Matpar,
I am not sure what “gunx.mat” file contains, but by looking at the error message, one can decode that the variable name “layers” not been found. Can you check the mat file once more, does it contain the required “layers” variable?
The second error message is related to “gunsGT” which I do not have any access to, but again looking at the error it seems like gunsGT is not the right argument which needs to be passed in trainRCNNObjectDetector, this detector function must have a datastore or {image,label} array as the first argument.
Also if you want me to further investigate, attach all the required mat file and function which is used.
I hope this helps.
  4 Comments
Matpar
Matpar on 26 Jan 2020
Hi SG,
I just did it over and got the same result, what am i soing wrong, please teach me? I think by now i should have solve this and still i am challenged. i need a push please.
this is my code:
clc
clear
net = alexnet
layers = [
imageInputLayer([227 227 3],"Name","data")
convolution2dLayer([11 11],96,"Name","conv1","BiasLearnRateFactor",2,"Stride",[4 4])
reluLayer("Name","relu1")
crossChannelNormalizationLayer(5,"Name","norm1","K",1)
maxPooling2dLayer([3 3],"Name","pool1","Stride",[2 2])
groupedConvolution2dLayer([5 5],128,2,"Name","conv2","BiasLearnRateFactor",2,"Padding",[2 2 2 2])
reluLayer("Name","relu2")
crossChannelNormalizationLayer(5,"Name","norm2","K",1)
maxPooling2dLayer([3 3],"Name","pool2","Stride",[2 2])
convolution2dLayer([3 3],384,"Name","conv3","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu3")
groupedConvolution2dLayer([3 3],192,2,"Name","conv4","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu4")
groupedConvolution2dLayer([3 3],128,2,"Name","conv5","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu5")
maxPooling2dLayer([3 3],"Name","pool5","Stride",[2 2])
fullyConnectedLayer(4096,"Name","fc6","BiasLearnRateFactor",2)
reluLayer("Name","relu6")
dropoutLayer(0.5,"Name","drop6")
fullyConnectedLayer(4096,"Name","fc7","BiasLearnRateFactor",2)
reluLayer("Name","relu7")
dropoutLayer(0.5,"Name","drop7")
fullyConnectedLayer(10,"Name","fc","BiasLearnRateFactor",10,"WeightLearnRateFactor",10)
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")]
imfolder = '/Users/mmgp/Desktop/gunsGT';
filenames = dir(fullfile(imfolder,'*.jpg'))
total_images = numel(filenames);
load('gTruth.mat','filenames')
% for n = 1:total_images
% f = fullfile(imfolder,filenames(n).name);
% myims = imread(f);
% figure(n)
% imshow(myims);
% end
imds = imageDatastore(imfolder,'IncludeSubFolders',true,'LabelSource','Foldernames')
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
inputSize = net.Layers(1).InputSize
layersTransfer = net.Layers(1:end-3);
numClasses = numel(categories(imdsTrain.Labels));
Tlayers = [
layersTransfer
fullyConnectedLayer(numClasses,'WeightLearnRateFactor',80,'BiasLearnRateFactor',80);
softmaxLayer
classificationLayer];
size(inputSize)
imds.ReadFcn=@(loc)imresize(imread(loc),[227,227])
pixelRange = [-30 30];
imageAugmenter = imageDataAugmenter( ...
'RandXReflection',true, ...
'RandXTranslation',pixelRange, ...
'RandYTranslation',pixelRange);
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
'DataAugmentation',imageAugmenter)
augTrainingSet = augmentedImageDatastore(inputSize ,imdsTrain,'ColorPreprocessing', 'gray2rgb');
augValidation = augmentedImageDatastore(inputSize,imdsValidation,'ColorPreprocessing', 'gray2rgb');
layer = 'fc7';
featuresTrain = activations(net,augTrainingSet,layer,'OutputAs','rows');
featuresTest = activations(net,augValidation,layer,'OutputAs','rows');
YTrain = imdsTrain.Labels;
YTest = imdsValidation.Labels;
mdl = fitcecoc(featuresTrain,YTrain)
YPred = predict(mdl,featuresTest);
opts = trainingOptions('sgdm',...
'Momentum',0.9,...
'InitialLearnRate', 1e-4,...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.1, ...
'Shuffle','every-epoch', ...
'LearnRateDropPeriod', 8, ...
'L2Regularization', 1e-4, ...
'MaxEpochs', 10,...
'MiniBatchSize',25,...
'Verbose', true);
trainedNet = trainNetwork(augTrainingSet,Tlayers,opts)
[YPred,probs] = classify(trainedNet,augValidation);
YValidation = imdsValidation.Labels;
Class_accuracy = mean(YPred == YTest)
idx = [1 5 10 15];
figure
for i = 1:numel(idx)
subplot(2,2,i)
I = readimage(imdsTrain,idx(i));
label = YPred(idx(i));
imshow(I)
title('Gun Predictions')
end
% idx = randperm(numel(imdsValidation.Files),16);
% figure
% for i = 1:16
% subplot(4,4,i)
% I = readimage(imdsValidation,idx(i));
% imshow(I)
% label = YPred(idx(i));
% title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),16) + "%");
% end
load('gTruth.mat')
% Positive and Negative Overlap Range Controls Which Image Patch is Used
rcnn = trainRCNNObjectDetector(gTruth, trainedNet, opts, 'NegativeOverlapRange', [0 0.3]);
%% Step 21 Testing the R-CNN detector on a test image.
testimg = imread('Gun00011.jpg');
[bboxes,score,label] = detect(rcnn,testimg,'MiniBatchSize',25)
%% Step 22 Display strongest detection result.
[score, idx] = max(score);
bbox = bboxes(idx, :);
annotation = sprintf('%s: (Confidence = %f)', label(idx), score);
Imgdetected = insertObjectAnnotation(testimg, 'rectangle', bbox, annotation);
figure
imshow(Imgdetected);
stil the box is not showing :(
please help me i would like very much if you can point out my error and teach me how to code the solution please...
Demet Hanife
Demet Hanife on 26 Jan 2021
testImage = imread('F:\a.jpg');
[bboxes, score, label] = detect(rcnn, testImage, 'MiniBatchSize', 128)
T = 0.9;
idx = score >= T;
s = score(idx);
lbl = label(idx);
bbox = bboxes(idx, :);
for ii = 1 : size(bbox, 1)
annotation = sprintf('%s:()', lbl(ii));
outputImage = insertObjectAnnotation(outputImage, 'rectangle', bbox(ii,:), annotation);
end
figure
imshow(outputImage)
I don't know if it helps, but I'm doing a face recognition test in a 12-person classroom with these codes. I'm inexperienced in Matlab. Maybe it gives an idea.

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