  We present in this paper a novel denoising training method to speedup DETR
(DEtection TRansformer) training and offer a deepened understanding of the slow
convergence issue of DETR-like methods. We show that the slow convergence
results from the instability of bipartite graph matching which causes
inconsistent optimization goals in early training stages. To address this
issue, except for the Hungarian loss, our method additionally feeds
ground-truth bounding boxes with noises into Transformer decoder and trains the
model to reconstruct the original boxes, which effectively reduces the
bipartite graph matching difficulty and leads to a faster convergence. Our
method is universal and can be easily plugged into any DETR-like methods by
adding dozens of lines of code to achieve a remarkable improvement. As a
result, our DN-DETR results in a remarkable improvement ($+1.9$AP) under the
same setting and achieves the best result (AP $43.4$ and $48.6$ with $12$ and
$50$ epochs of training respectively) among DETR-like methods with ResNet-$50$
backbone. Compared with the baseline under the same setting, DN-DETR achieves
comparable performance with $50\%$ training epochs. Code is available at
\url{https://github.com/FengLi-ust/DN-DETR}.
