  The YOLO series has become the most popular framework for real-time object
detection due to its reasonable trade-off between speed and accuracy. However,
we observe that the speed and accuracy of YOLOs are negatively affected by the
NMS. Recently, end-to-end Transformer-based detectors (DETRs) have provided an
alternative to eliminating NMS. Nevertheless, the high computational cost
limits their practicality and hinders them from fully exploiting the advantage
of excluding NMS. In this paper, we propose the Real-Time DEtection TRansformer
(RT-DETR), the first real-time end-to-end object detector to our best knowledge
that addresses the above dilemma. We build RT-DETR in two steps, drawing on the
advanced DETR: first we focus on maintaining accuracy while improving speed,
followed by maintaining speed while improving accuracy. Specifically, we design
an efficient hybrid encoder to expeditiously process multi-scale features by
decoupling intra-scale interaction and cross-scale fusion to improve speed.
Then, we propose the uncertainty-minimal query selection to provide
high-quality initial queries to the decoder, thereby improving accuracy. In
addition, RT-DETR supports flexible speed tuning by adjusting the number of
decoder layers to adapt to various scenarios without retraining. Our
RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4
GPU, outperforming previously advanced YOLOs in both speed and accuracy. We
also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and
M models). Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy
and about 21 times in FPS. After pre-training with Objects365, RT-DETR-R50 /
R101 achieves 55.3% / 56.2% AP. The project page:
https://zhao-yian.github.io/RTDETR.
