Papers › Deformable DETR: Deformable Transformers for End-to-End Object Detection
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is released at https://github.com/fundamentalvision/Deformable-DETR.
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Code
Syntology Ran 29 of 55 code samples harvested from 13 repositories linked to this paper; 26 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · violated contract; 10 ran · our draft was wrong; 1 ran · fixture could not drive it; 14 ran with no contract checked.
By repository: community (archive-listed): 55 samples from 13 repositories, 29 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
20 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
55 samples harvested; 29 ran; 1 honoured the contract we drafted; 26 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Object Detection | SARDet-100K | Deformable DETR | box mAP | 50.0 | #7 of 13 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | AP50 | 71.9 | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | AP75 | 58.1 | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | APL | 65.6 | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | APM | 54.4 | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | APS | 34.4 | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | Hardware Burden | 17G | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | Operations per network pass | 17.3G | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Deformable DETR (ResNeXt-101+DCN) | box mAP | 52.3 | #71 of 225 | Archive leaderboard | report |
| Object Detection | COCO-O | Deformable-DETR (ResNet-50) | Average mAP | 18.5 | #33 of 45 | Archive leaderboard | report |
| Object Detection | COCO-O | Deformable-DETR (ResNet-50) | Effective Robustness | -1.49 | #33 of 45 | 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
Introduced by this paper: Deformable Attention Module, Deformable DETR
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