Papers › DETR Does Not Need Multi-Scale or Locality Design
DETR Does Not Need Multi-Scale or Locality Design
Yutong Lin, Yuhui Yuan, Zheng Zhang, Chen Li, Nanning Zheng, Han Hu
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints. The first is a box-to-pixel relative position bias (BoxRPB) term added to the cross-attention formulation, which well guides each query to attend to the corresponding object region while also providing encoding flexibility. The second is masked image modeling (MIM)-based backbone pre-training which helps learn representation with fine-grained localization ability and proves crucial for remedying dependencies on the multi-scale feature maps. By incorporating these technologies and recent advancements in training and problem formation, the improved "plain" DETR showed exceptional improvements over the original DETR detector. By leveraging the Object365 dataset for pre-training, it achieved 63.9 mAP accuracy using a Swin-L backbone, which is highly competitive with state-of-the-art detectors which all heavily rely on multi-scale feature maps and region-based feature extraction. Code will be available at https://github.com/impiga/Plain-DETR.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | AP50 | 82.1 | #12 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | AP75 | 70.7 | #12 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | APL | 76.7 | #12 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | APM | 66.8 | #12 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | APS | 48.2 | #12 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | Params (M) | 228 | #12 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Plain-DETR (Swin-L) | box mAP | 63.9 | #12 of 225 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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