  Model efficiency is crucial for object detection. Mostprevious works rely on
either hand-crafted design or auto-search methods to obtain a static
architecture, regardless ofthe difference of inputs. In this paper, we
introduce a newperspective of designing efficient detectors, which is
automatically generating sample-adaptive model architectureon the fly. The
proposed method is named content-aware dynamic detectors (CADDet). It first
applies a multi-scale densely connected network with dynamic routing as the
supernet. Furthermore, we introduce a course-to-fine strat-egy tailored for
object detection to guide the learning of dynamic routing, which contains two
metrics: 1) dynamic global budget constraint assigns data-dependent
expectedbudgets for individual samples; 2) local path similarity regularization
aims to generate more diverse routing paths. With these, our method achieves
higher computational efficiency while maintaining good performance. To the best
of our knowledge, our CADDet is the first work to introduce dynamic routing
mechanism in object detection. Experiments on MS-COCO dataset demonstrate that
CADDet achieves 1.8 higher mAP with 10% fewer FLOPs compared with vanilla
routing strategy. Compared with the models based upon similar building blocks,
CADDet achieves a 42% FLOPs reduction with a competitive mAP.
