CenterNet2 is an object detection architecture that combines one-stage and two-stage detection paradigms. The first stage estimates object probabilities using a class-agnostic detector as a proposal network, while the second stage conditionally classifies detected objects. Built on detectron2, it achieves state-of-the-art accuracy (56.4 mAP on COCO) while maintaining competitive inference speeds.