  In this paper, we aim to design an efficient real-time object detector that
exceeds the YOLO series and is easily extensible for many object recognition
tasks such as instance segmentation and rotated object detection. To obtain a
more efficient model architecture, we explore an architecture that has
compatible capacities in the backbone and neck, constructed by a basic building
block that consists of large-kernel depth-wise convolutions. We further
introduce soft labels when calculating matching costs in the dynamic label
assignment to improve accuracy. Together with better training techniques, the
resulting object detector, named RTMDet, achieves 52.8% AP on COCO with 300+
FPS on an NVIDIA 3090 GPU, outperforming the current mainstream industrial
detectors. RTMDet achieves the best parameter-accuracy trade-off with
tiny/small/medium/large/extra-large model sizes for various application
scenarios, and obtains new state-of-the-art performance on real-time instance
segmentation and rotated object detection. We hope the experimental results can
provide new insights into designing versatile real-time object detectors for
many object recognition tasks. Code and models are released at
https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet.
