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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 pre- diction. DINO achieves 49.4AP in 12 epochs and 51.3AP in 24 epochs on COCO with a ResNet-50 backbone and multi-scale features, yield- ing 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

Algorithm Config Params
(backbone/total)		 inference time(V100)
(ms/img)		 bbox_mAPval
0.5:0.95		 APval
50		 Download
DINO_4sc_r50_12e DINO_4sc_r50_12e 23M/47M 184ms 48.71 66.27 model - log
DINO_4sc_r50_36e DINO_4sc_r50_36e 23M/47M 184ms 50.69 68.60 model - log
DINO_4sc_swinl_12e DINO_4sc_swinl_12e 195M/217M 155ms 56.86 75.61 model - log
DINO_4sc_swinl_36e DINO_4sc_swinl_36e 195M/217M 155ms 58.04 76.76 model - log
DINO_5sc_swinl_36e DINO_5sc_swinl_36e 195M/217M 235ms 58.47 77.10 model - log
DINO++_5sc_swinl_18e DINO++_5sc_swinl_18e 195M/218M 325ms 63.39 80.25 model - log
(objects365 dataset processing tools: https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/dino/obj365_download_tools.tar.gz)

Citation

@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}
}