hoe to evaluate trained faster rcnn detector

hi everyone

i want ti ecaluate my faster Rcnn detector on testdta set i tried this code but i got error on evaluationobjectdetector

lassID = 1;

metrics = evaluateObjectDetection(detectionResults,testData);

precision = metrics.ClassMetrics.Precision{classID};

recall = metrics.ClassMetrics.Recall{classID}

than i tried this code is aldo so error

testData = transform(testData,@(data)preprocessData(data,inputSize));

detectionResults = detect(detector,testData,'MinibatchSize',10);

% Determine the number of classes in your dataset

num_classes = 5;

% Preallocate arrays to store precision and recall values for all classes

precisions = zeros(1, num_classes);

recalls = zeros(1, num_classes);

% Loop through all class IDs

for classID = 1:num_classes

% Evaluate precision and recall for the current class ID

metrics = fasterRCNNtestdetect(detectionResults, testData);

precision = metrics.ClassMetrics.Precision{classID};

recall = metrics.ClassMetrics.Recall{classID};

% Store precision and recall for the current class

precisions(classID) = precision;

recalls(classID) = recall;

Answer 1

[Maneet Kaur Bagga]

Hi Ahmad,

After reproducing the code at my end, as per my understanding the syntax error is because of the "ClassMetrics" of the "evaluateObjectDetection" function. The metrics.ClassMetrics represents a table and for each class it represents a row, hence to access each class iterate through all the rows and for each row access the "precision" and "recall" property.

Please refer to the following code snippet for better understanding of the workaround.

% Evaluate object detection
metrics = evaluateObjectDetection(detectionResults, groundTruthData);

% Access precision and recall for each class
classMetrics = metrics.ClassMetrics;

% Loop through each class and obtain precision and recall
for classID = 1:numel(classMetrics)
    precision = classMetrics(classID).Precision;
    recall = classMetrics(classID).Recall;

    % Do something with precision and recall for the current class
    fprintf('Class %d: Precision = %.2f, Recall = %.2f\n', ...
        classID, precision, recall);
end

Please refer to the following MATLAB documentaion specially the "ClassMetrics" section.

objectDetectionMetrics : https://www.mathworks.com/help/vision/ref/objectdetectionmetrics.html

Hope this helps!