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Mr.DETR

Paper: Mr. DETR: Instructive Multi-Route Training for Detection Transformers Project Page: Code:

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Other
File size
211 MB–1.61 GB
Version
vRevision 3f25ee2
Updated
Sep 16, 2026
Downloads
0
Tasks
Object detection
Source
Hugging Face

Source description

Mr. DETR: Instructive Multi-Route Training for Detection Transformers

Conference Paper Project PWC

Paper: Mr. DETR: Instructive Multi-Route Training for Detection Transformers
Project Page: https://visual-ai.github.io/mrdetr Code: https://github.com/Visual-AI/Mr.DETR

Abstract

Existing methods enhance the training of detection transformers by incorporating an auxiliary one-to-many assignment. In this work, we treat the model as a multi-task framework, simultaneously performing one-to-one and one-to-many predictions. We investigate the roles of each component in the transformer decoder across these two training targets, including self-attention, cross-attention, and feed-forward network. Our empirical results demonstrate that any independent component in the decoder can effectively learn both targets simultaneously, even when other components are shared. This finding leads us to propose a multi-route training mechanism, featuring a primary route for one-to-one prediction and two auxiliary training routes for one-to-many prediction. We enhance the training mechanism with a novel instructive self-attention that dynamically and flexibly guides object queries for one-to-many prediction. The auxiliary routes are removed during inference, ensuring no impact on model architecture or inference cost. We conduct extensive experiments on various baselines, achieving consistent improvements as shown in Figure 1.

Updates

  • [04/25] Mr. DETR supports Instance segmentation now. We release the code and pre-trained weights.
  • [03/25] We release the code and weights of Mr. DETR for object detection. You may find pre-trained weights at Huggingface.
  • [03/25] Mr. DETR is accepted by CVPR 2025.

Performance

Demo Video for Street
Demo Video for Dense and Crowded Scene

Sources

1 source
Hugging FacePrimaryVersion 3f25ee280505d8e6c0d35502151818e5b11344d3
huggingface.co/allencbzhang/Mr.DETR/tree/3f25ee280505d8e6c0d35502151818e5b11344d3

Verified Sep 16

Model artifacts

28 artifacts

model_final.pth

pth · 637 MB · SHA-256 25354c126790…5a51 · Hugging Face

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Mr_DETR_pp_align_12ep.pth

pth · 427 MB · SHA-256 54ab9a74d3a8…d113 · Hugging Face

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Mr_DETR_pp_align_24ep.pth

pth · 427 MB · SHA-256 02ac71e5d460…4711 · Hugging Face

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Mr_DETR_pp_def_12ep_insseg.pth

pth · 554 MB · SHA-256 8de7079440dd…30a1 · Hugging Face

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Mr_DETR_pp_def_12ep.pth

pth · 455 MB · SHA-256 5b54207e14a7…b724 · Hugging Face

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Mr_DETR_pp_def_24ep_insseg.pth

pth · 1.61 GB · SHA-256 aca6a4e2942c…0004 · Hugging Face

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Mr_DETR_pp_def_24ep.pth

pth · 455 MB · SHA-256 8da8962cee62…cffe · Hugging Face

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Mr_DETR_pp_dino_12ep.pth

pth · 395 MB · SHA-256 e53b9039ca9a…54c7 · Hugging Face

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Mr_DETR_pp_dino_24ep.pth

pth · 395 MB · SHA-256 35d3b03abeeb…4455 · Hugging Face

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Mr_MaskDINO_12ep.pth

pth · 706 MB · SHA-256 13bc5710efce…d5f1 · Hugging Face

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MrDETR_align_r50_12ep_900q.pth

pth · 211 MB · SHA-256 7fd5c4653249…cd81 · Hugging Face

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MrDETR_align_r50_24ep_900q.pth

pth · 211 MB · SHA-256 85a902b2029a…3f03 · Hugging Face

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MrDETR_align_swinl_12ep_900q_objects365_processed.pth

pth · 862 MB · SHA-256 546757f213c9…7028 · Hugging Face

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MrDETR_align_swinL_12ep_900q_safe.pth

pth · 863 MB · SHA-256 f16dec5e47ba…e26d · Hugging Face

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MrDETR_align_swinL_12ep_900q.pth

pth · 863 MB · SHA-256 f16dec5e47ba…e26d · Hugging Face

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MrDETR_deformable_r50_12ep_300q_insseg.pth

pth · 306 MB · SHA-256 8b98b855c3c5…17da · Hugging Face

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MrDETR_deformable_r50_12ep_300q.pth

pth · 211 MB · SHA-256 91001a65633d…5fa9 · Hugging Face

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MrDETR_deformable_r50_24ep_300q_insseg.pth

pth · 306 MB · SHA-256 98d911c55755…fc02 · Hugging Face

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MrDETR_deformable_r50_24ep_900q.pth

pth · 211 MB · SHA-256 29d48497df67…40e6 · Hugging Face

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MrDETR_deformbale_r50_12ep_900q.pth

pth · 211 MB · SHA-256 ebfe6bb5d3e4…4aa7 · Hugging Face

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MrDETR_dino_r50_12ep_900q.pth

pth · 211 MB · SHA-256 1d671f09eb9f…9b0e · Hugging Face

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MrDETR_pp_align_swinL_12ep_900q.pth

pth · 1.02 GB · SHA-256 117aeec53b2a…fe11 · Hugging Face

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update_mrdetr++_coco_12ep_def.pth

pth · 416 MB · SHA-256 d3735a8578c9…c333 · Hugging Face

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update_mrdetr++_coco_12ep_dino_swinL.pth

pth · 1.04 GB · SHA-256 f75e8cece16d…6f53 · Hugging Face

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update_mrdetr++_coco_12ep_dino.pth

pth · 415 MB · SHA-256 be1ef1b7c4d4…e19b · Hugging Face

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update_mrdetr++_coco_12ep_maskdino.pth

pth · 540 MB · SHA-256 73308281d2c6…a177 · Hugging Face

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update_mrdetr++_coco_24ep_dino.pth

pth · 415 MB · SHA-256 97095626f3c7…f566 · Hugging Face

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update_mrdetr++_objects365.pth

pth · 1.22 GB · SHA-256 67877e621970…80da · Hugging Face

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Source excerpts

2 excerpts

--- license: mit pipeline_tag: object-detection library_name: detectron2 --- # Mr. DETR: Instructive Multi-Route Training for Detection Transformers [![Conference](https://img.shields.io/badge/CVPR-2025-blue)]() [![Paper](https://img.shields.io/badge/arXiv-2412.10028-brightgreen)](https://arxiv.org/abs/2412.10028) [![Project](https://img.shields.io/badge/Project-red)](https://visual-ai.github.io/mrdetr/) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/mr-detr-instructive-multi-route-training-for/object-detection-on-coco-2017-val)](https://paperswithcode.com/sota/object-detection-on-coco-2017-val?p=mr-detr-instructive-multi-route-training-for) **Paper:** [Mr. DETR: Instructive Multi-Route Training for Detection Transformers](https://huggingface.co/papers/2412.10028) **Project Page:** https://visual-ai.github.io/mrdetr **Code:** https://github.com/Visual-AI/Mr.DETR ## Abstract Existing methods enhance the training of detection transformers by incorporating an auxiliary one-to-many assignment. In this work, we treat the model as a multi-task framework, simultaneously performing one-to-one and one-to-many predictions. We investigate the roles of each component in the transformer decoder across these two training targets, including self-attention, cross-attention, and feed-forward network. Our empirical results demonstrate that any independent component in the decoder can effectively learn both targets simultaneously, even when other components are shared. This finding leads us to propose a multi-route training mechanism, featuring a primary route for one-to-one prediction and two auxiliary training routes for one-to-many prediction. We enhance the training mechanism with a novel instructive self-attention that dynamically and flexibly guides object queries for one-to-many prediction. The auxiliary routes are removed during inference, ensuring no impact on model architecture or inference cost. We conduct extensive experiments on various baselines, achieving consistent improvements as shown in Figure 1. ## Updates - [04/25] Mr. DETR supports Instance segmentation now. We release the code and pre-trained weights. - [03/25] We release the code and weights of Mr. DETR for object detection. You may find pre-trained weights at [Huggingface](https://huggingface.co/allencbzhang/Mr.DETR/tree/main). - [03/25] Mr. DETR is accepted b...

Source context: 0 downloads · 1 likes · Pipeline object-detection · Library detectron2 · Repo allencbzhang/Mr.DETR