  Current anchor-free object detectors are quite simple and effective yet lack
accurate label assignment methods, which limits their potential in competing
with classic anchor-based models that are supported by well-designed assignment
methods based on the Intersection-over-Union~(IoU) metric. In this paper, we
present \textbf{Pseudo-Intersection-over-Union~(Pseudo-IoU)}: a simple metric
that brings more standardized and accurate assignment rule into anchor-free
object detection frameworks without any additional computational cost or extra
parameters for training and testing, making it possible to further improve
anchor-free object detection by utilizing training samples of good quality
under effective assignment rules that have been previously applied in
anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end
single-stage anchor-free object detection framework, we observe consistent
improvements in their performance on general object detection benchmarks such
as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also
achieves comparable performance to other recent state-of-the-art anchor-free
methods without bells and whistles. Our code is based on mmdetection toolbox
and will be made publicly available at
https://github.com/SHI-Labs/Pseudo-IoU-for-Anchor-Free-Object-Detection.
