  Recent advances in label assignment in object detection mainly seek to
independently define positive/negative training samples for each ground-truth
(gt) object. In this paper, we innovatively revisit the label assignment from a
global perspective and propose to formulate the assigning procedure as an
Optimal Transport (OT) problem -- a well-studied topic in Optimization Theory.
Concretely, we define the unit transportation cost between each demander
(anchor) and supplier (gt) pair as the weighted summation of their
classification and regression losses. After formulation, finding the best
assignment solution is converted to solve the optimal transport plan at minimal
transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On
COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport
Assignment (OTA) can reach 40.7% mAP under 1X scheduler, outperforming all
other existing assigning methods. Extensive experiments conducted on COCO and
CrowdHuman further validate the effectiveness of our proposed OTA, especially
its superiority in crowd scenarios. The code is available at
https://github.com/Megvii-BaseDetection/OTA.
