DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
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.
| Backbone | Model | Lr schd | box AP | Config | Download |
|---|---|---|---|---|---|
| R-50 | DINO-4scale | 12e | 49.0 | config | model | log |
| Swin-L | DINO-5scale | 12e | 57.2 | config | model | log |
| Swin-L | DINO-5scale | 36e | 58.4 | config | model | log |
The performance is unstable. DINO-4scale with R-50 may fluctuate about 0.4 mAP.
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}}