SortedAP: Rethinking evaluation metrics for instance segmentation
04 Oct 2023
Reading time ~1 minute
Authors: Long Chen, Yuli Wu and Johannes Stegmaier
Published in: International Conference on Computer Vision (ICCV) Workshops 2023
Abstract: Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties, such as sensitivity, continuity, and equality, are overlooked in the current study. In this paper, we reveal that most existing metrics have a limited resolution of segmentation quality. They are only conditionally sensitive to the change of masks or false predictions. For certain metrics, the score can change drastically in a narrow range which could provide a misleading indication of the quality gap between results. Therefore, we propose a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain. We provide the evaluation toolkit and experiment code here.