dino

DINO

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

Abstract

We present DINO (DETR with Improved deNoising anchOr boxes), a state-of-the-art end-to-end object detector. 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 49.4AP in 12 epochs and 51.3AP in 24 epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a significant improvement of +6.0AP and +2.7AP, 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 val2017 (63.2AP) and test-dev (63.3AP). Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.

Results and Models

Backbone	Model	Lr schd	Better-Hyper	box AP	Config	Download
R-50	DINO-4scale	12e	False	49.0	config	model | log
R-50	DINO-4scale	12e	True	50.1	config	model | log
Swin-L	DINO-5scale	12e	False	57.2	config	model | log
Swin-L	DINO-5scale	36e	False	58.4	config	model | log

NOTE

The performance is unstable. DINO-4scale with R-50 may fluctuate about 0.4 mAP.

Citation

We provide the config files for DINO: DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

@misc{zhang2022dino,
  title={DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection},
  author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel M. Ni and Heung-Yeung Shum},
  year={2022},
  eprint={2203.03605},
  archivePrefix={arXiv},
  primaryClass={cs.CV}}