Improve model card: Add pipeline tag, library name and abstract (#1)

- Improve model card: Add pipeline tag, library name and abstract (50ab1ba54698d3e39887c2c38eaf6613919fa9e0)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,3 +1,47 @@
----
-license: mit
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+---
+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/)
+
+**Paper:** [Mr. DETR: Instructive Multi-Route Training for Detection Transformers](https://huggingface.co/papers/2412.10028)
+**Project Page:** https://visual-ai.github.io/mrdetr
+
+## 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. 
+
+**(Content of the original README below)**
+
+# Mr. DETR
+**<center><font size=4>[CVPR 2025] Mr. DETR: Instructive Multi-Route Training for Detection Transformers</font></center>**  
+[Chang-Bin Zhang](https://zhangchbin.github.io)<sup>1</sup>, Yujie Zhong<sup>2</sup>, Kai Han<sup>1</sup>  
+<sup>1</sup> <sub>The University of Hong Kong</sub>  
+<sup>2</sup> <sub>Meituan Inc.</sub>  
+
+[![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)
+<a href="mailto: [email protected]">
+        <img alt="emal" src="https://img.shields.io/badge/contact_me-email-yellow">
+    </a>
+
+
+## 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 by CVPR 2025.
+
+## Performance
+[Demo Video for Street](https://www.bilibili.com/video/BV1ThZnYxE5G/?spm_id_from=333.1387.homepage.video_card.click&vd_source=3b32a049a039d0ef814f8588b3c9b2d9)  
+[Demo Video for Dense and Crowded Scene](https://www.zhihu.com/zvideo/1890060966391153546)
+
+
+## Method
+<img width="1230" alt="" src="assets/mrdetrmethod.png">
+
+...(rest of the original README content)