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
The number of classes
Take top-k confident predictions into account. If k=-1,calculate on all predictions
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
the number of classes (including skipped class)
only take topK confident predictions (-1 for all predictions)
skip calculating on a specific class (e.g. background) the class index starts from 0, or is -1 if no skipping
the IOU thresholds, (rangeStart, stepSize, numOfThres), inclusive
MeanAveragePrecisionObjectDetection
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
the number of classes (including skipped class)
only take topK confident predictions (-1 for all predictions)
skip calculating on a specific class (e.g. background) the class index starts from 0, or is -1 if no skipping
the IOU thresholds, (rangeStart, stepSize, numOfThres), inclusive
MeanAveragePrecisionObjectDetection
Create MeanAveragePrecision validation method using Pascal VOC's algorithm for object detection
Create MeanAveragePrecision validation method using Pascal VOC's algorithm for object detection
the number of classes
if using the algorithm in Voc2007 (11 points). Otherwise, use Voc2010
only take topK confident predictions (-1 for all predictions)
skip calculating on a specific class (e.g. background) the class index starts from 0, or is -1 if no skipping
MeanAveragePrecisionObjectDetection