Why I cannot get results when I detect multiple labels

Question

Carla Shaw 2019-9-15

0
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https://ww2.mathworks.cn/matlabcentral/answers/480463-why-i-cannot-get-results-when-i-detect-multiple-labels

评论： Zaime Ali 2020-9-27

在 MATLAB Online 中打开

after I train FasterRCNN object detector, and use the detector to detect multiple labels, the results did not shown

I cannot figure out why the results not shown in the table. so that I cannot calculate the average precision.

I showed my code

%%
trainingData = apple
numClasses = 3;
numClassesPlusBackground = numClasses + 1;
%%
inputLayer = imageInputLayer([32 32 3]);
filterSize = [3 3];
numFilters = 32;
middleLayers = [             
    convolution2dLayer(filterSize, numFilters, 'Padding', 1)   
    reluLayer()     
    convolution2dLayer(filterSize, numFilters, 'Padding', 1)  
    reluLayer() 
    maxPooling2dLayer(3, 'Stride',2)    
    ];
finalLayers = [
    fullyConnectedLayer(4)
    reluLayer
    fullyConnectedLayer(width(trainingData))
    softmaxLayer
    classificationLayer
    ];
layers = [
    inputLayer
    middleLayers
    finalLayers
    ]
options = trainingOptions('sgdm', ...
      'MiniBatchSize', 1, ...
      'InitialLearnRate', 1e-3, ...
      'MaxEpochs', 10, ...
      'VerboseFrequency', 200, ...
      'CheckpointPath', tempdir);
detector = trainFasterRCNNObjectDetector(trainingData, layers, ...
        'NegativeOverlapRange',[0 0.3], ...
        'PositiveOverlapRange',[0.6 1]);
%%
numImages = height(trainingData);
% Run detector on each image in the test set and collect results.
results = table('Size',[numImages 3],...
       'VariableTypes',{'cell','cell','cell'},...
       'VariableNames',{'Boxes','Scores','Labels'});  
for i = 1:numImages
    %ds = imageDatastore(trainingData.imageFilename{i});
    % Read the image.
    I = imread(trainingData.imageFilename{i});
    % Run the detector.
    [bboxes,scores,labels] = detect(detector, I);
    
    I=insertObjectAnnotation(I, 'rectangle', bboxes, scores);
    
    % Collect the results.
    results.Boxes{i,1} = bboxes;
    results.Scores{i,1} = scores;
    results.Labels{i,1} = labels;
end
results=struct2table(resultsStruct);
 expectedResults = trainingData(:, 2:4);
[ap, recall, precision] = evaluateDetectionPrecision(results,expectedResults);
figure
xlabel('unripeRecall')
ylabel('unripePrecision')
plot(recall, precision)
grid ON
title(sprintf('Average Precision = %.2f', ap));