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

Code

• github.com/IDEACVR/DINO
  OfficialIn paperpytorch★ 2,765
• github.com/IDEA-Research/Grounded-Segment-Anything
  pytorch★ 17,472
• github.com/lucasjinreal/yolov7_d2
  pytorch★ 3,114
• github.com/idea-research/dino
  pytorch★ 2,765
• github.com/IDEA-Research/detrex
  pytorch★ 2,274
• github.com/alibaba/EasyCV
  pytorch★ 1,949
• github.com/IDEACVR/MaskDINO
  pytorch★ 1,505
• github.com/idea-research/maskdino
  pytorch★ 1,505
• github.com/NVlabs/FasterViT
  pytorch★ 911
• github.com/idea-research/dn-detr
  pytorch★ 604

Abstract

We present DINO (DETR with Improved deNoising anchOr 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 49.4AP 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. Our code will be available at https://github.com/IDEACVR/DINO.

Tasks

Benchmark Results

Reproductions