  We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising
anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in
this paper. DINO improves over previous DETR-like models in performance and
efficiency by using a contrastive way for denoising training, a mixed query
selection method for anchor initialization, and a look forward twice scheme for
box prediction. DINO achieves $49.4$AP in $12$ epochs and $51.3$AP in $24$
epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a
significant improvement of $\textbf{+6.0}$\textbf{AP} and
$\textbf{+2.7}$\textbf{AP}, respectively, compared to DN-DETR, the previous
best DETR-like model. DINO scales well in both model size and data size.
Without bells and whistles, after pre-training on the Objects365 dataset with a
SwinL backbone, DINO obtains the best results on both COCO \texttt{val2017}
($\textbf{63.2}$\textbf{AP}) and \texttt{test-dev}
(\textbf{$\textbf{63.3}$AP}). Compared to other models on the leaderboard, DINO
significantly reduces its model size and pre-training data size while achieving
better results. Our code will be available at
\url{https://github.com/IDEACVR/DINO}.
