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instanceSegmentationMetrics

Instance segmentation quality metrics

Since R2022b

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    Description

    Use the instanceSegmentationMetrics object and its object functions to evaluate the quality of instance segmentation results.

    An instanceSegmentationMetrics object stores instance segmentation quality metrics for a set of images, such as the average precision (AP) and precision and recall, computed per class and per image. To compute the AP and precision recall metrics, pass the instanceSegmentationMetrics object to the averagePrecision or the precisionRecall object functions, respectively. To compute the confusion matrix, pass the instanceSegmentationMetrics object to the confusionMatrix object function. Evaluate the summary of all metrics across all classes and all images in the data set using the summarize object function.

    Creation

    Create an instanceSegmentationMetrics object using the evaluateInstanceSegmentation function.

    Properties

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    This property is read-only.

    Metrics per class, specified as a table with C rows, where C is the number of classes in the instance segmentation. ClassMetrics has these columns, corresponding to these instance segmentation metrics:

    • mAP — Mean average precision calculated by averaging over all thresholds specified in the threshold argument for a class.

    • AP — Average precision calculated for a class at each overlap threshold in OverlapThreshold, returned as a numThresh-by-1 array, where numThresh is the number of overlap thresholds.

    • Precision — Precision values, returned as a numThresh-by-(numPredictions+1) matrix, where numPredictions is the number of predicted object masks. Precision is the ratio of the number of true positives (TP) and the total number of predicted positives​.

      Precision = TP / (TP + FP)

      FP is the number of false positives, or misclassified pixels that comprise object instances. Larger precision scores imply that most object instance classes match ground truth objects.

    • Recall — Recall values, returned as a numThresh-by-(numPredictions+1) matrix, where numPredictions is the number of predicted object masks. Recall is the ratio of the number of true positives (TP) and the total number of ground truth positives​.

      Recall = TP / (TP + FN)

      FN is the number of false negatives, or the number of pixels that comprise instances that have been misclassified as background pixels. Larger recall scores imply that most ground truth objects are detected.

    This property is read-only.

    Metrics for each image in the data set, specified as a table with numImages rows, where numImages is the number of images in the data set. ImageMetrics has these columns, corresponding to these instance segmentation metrics:

    • NumObjects — Number of objects in the ground truth data in each image, stored as a positive integer.

    • mAP — Mean average precision (mAP), calculated by averaging the average precision (AP) across all classes at each overlap threshold in the OverlapThreshold property, stored as a numThresh-by-1 numeric vector. numThresh is the number of overlap thresholds. Specify the overlap thresholds for an image using the threshold argument.

    • mAPOverlapAvg — Mean average precision (mAP), calculated by averaging the AP across all classes and all overlap thresholds specified by the OverlapThreshold property, stored as a numeric scalar.

    Class names of segmented objects, specified as an array of strings.

    Example: ["sky" "grass" "building" "sidewalk"]

    Overlap threshold, specified as a numeric scalar or numeric vector. When the intersection over union (IoU) of the pixels in the predicted object mask and ground truth object mask is equal to or greater than the overlap threshold, the prediction is considered a true positive.

    IoU, or the Jaccard Index, is the number of pixels in the intersection of the binary masks divided by the number of pixels in the union of the masks. In other words, IoU is the ratio of correctly classified pixels to the total number of pixels that are assigned that class by the ground truth and the predictor.

    Object Functions

    averagePrecisionEvaluate average precision metric of instance segmentation results
    confusionMatrixCompute confusion matrix of instance segmentation results
    precisionRecallGet precision recall metrics of instance segmentation results
    summarizeSummarize instance segmentation performance metrics at data set and class level
    metricsByAreaEvaluate instance segmentation across object mask size ranges

    Version History

    Introduced in R2022b

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    See Also

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