MeanAveragePrecision

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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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

9. final def eq(arg0: AnyRef): Boolean

   

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11. def finalize(): Unit

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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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

19. final def synchronized[T0](arg0: ⇒ T0): T0

    

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21. final def wait(): Unit

    

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22. final def wait(arg0: Long, arg1: Int): Unit

    

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23. final def wait(arg0: Long): Unit

    

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