The 2D semantic instance prediction task involves detecting and segmenting the object in an image.

Evaluation and metrics

Our evaluation ranks all methods according to the average precision for each class. We report the mean average precision AP (from overlaps [0.5:0.95:0.05]), as well as AP 50% for an overlap value of 50. Note that multiple predictions of the same ground truth instance are penalized as false positives.



This table lists the benchmark results for the 2D semantic instance scenario.




Method Infoavg apbathtubbedbookshelfcabinetchaircountercurtaindeskdoorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwindow
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MaskRCNN_ScanNetpermissive0.119 10.129 10.212 10.002 10.112 10.148 10.014 10.205 10.044 10.066 10.078 10.095 10.142 10.030 10.128 10.139 10.080 10.459 10.057 1
Re-implementation of Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick: Mask R-CNN. ICCV'17