DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

TL;DR

DINO is a state-of-the-art end-to-end object detection model that improves performance and efficiency over previous DETR-like models through innovative training and prediction techniques, achieving high accuracy on COCO benchmarks.

Contribution

The paper introduces DINO, a novel DETR-based object detector with improved denoising, anchor initialization, and prediction schemes, leading to superior performance and scalability.

Findings

Achieves 49.4 AP in 12 epochs on COCO with ResNet-50.

Attains 63.2 AP on COCO val2017 after pre-training on Objects365.

Outperforms previous DETR-like models with fewer resources.

Abstract

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}.