object MeanAveragePrecision extends Serializable

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  5. def classification(nClasses: Int, topK: Int = -1): MeanAveragePrecision[Float]

    Calculate the Mean Average Precision (MAP) for classification output and target The algorithm follows VOC Challenge after 2007 Require class label beginning with 0

    Calculate the Mean Average Precision (MAP) for classification output and target The algorithm follows VOC Challenge after 2007 Require class label beginning with 0

    nClasses

    The number of classes

    topK

    Take top-k confident predictions into account. If k=-1,calculate on all predictions

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  7. def cocoBBox(nClasses: Int, topK: Int = -1, skipClass: Int = 0, iouThres: (Float, Float, Int) = (0.5f, 0.05f, 10)): MeanAveragePrecisionObjectDetection[Float]

    Create MeanAveragePrecision validation method using COCO's algorithm for object detection.

    Create MeanAveragePrecision validation method using COCO's algorithm for object detection. IOU computed by the bounding boxes

    nClasses

    the number of classes (including skipped class)

    topK

    only take topK confident predictions (-1 for all predictions)

    skipClass

    skip calculating on a specific class (e.g. background) the class index starts from 0, or is -1 if no skipping

    iouThres

    the IOU thresholds, (rangeStart, stepSize, numOfThres), inclusive

    returns

    MeanAveragePrecisionObjectDetection

  8. def cocoSegmentation(nClasses: Int, topK: Int = -1, skipClass: Int = 0, iouThres: (Float, Float, Int) = (0.5f, 0.05f, 10)): MeanAveragePrecisionObjectDetection[Float]

    Create MeanAveragePrecision validation method using COCO's algorithm for object detection.

    Create MeanAveragePrecision validation method using COCO's algorithm for object detection. IOU computed by the segmentation masks

    nClasses

    the number of classes (including skipped class)

    topK

    only take topK confident predictions (-1 for all predictions)

    skipClass

    skip calculating on a specific class (e.g. background) the class index starts from 0, or is -1 if no skipping

    iouThres

    the IOU thresholds, (rangeStart, stepSize, numOfThres), inclusive

    returns

    MeanAveragePrecisionObjectDetection

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  18. def pascalVOC(nClasses: Int, useVoc2007: Boolean = false, topK: Int = -1, skipClass: Int = 0): MeanAveragePrecisionObjectDetection[Float]

    Create MeanAveragePrecision validation method using Pascal VOC's algorithm for object detection

    Create MeanAveragePrecision validation method using Pascal VOC's algorithm for object detection

    nClasses

    the number of classes

    useVoc2007

    if using the algorithm in Voc2007 (11 points). Otherwise, use Voc2010

    topK

    only take topK confident predictions (-1 for all predictions)

    skipClass

    skip calculating on a specific class (e.g. background) the class index starts from 0, or is -1 if no skipping

    returns

    MeanAveragePrecisionObjectDetection

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