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Rank-DETR for High Quality Object Detection (NeurIPS 2023)
Yifan Pu, Weicong Liang, Yiduo Hao, Yuhui Yuan, Yukang Yang, Chao Zhang, Han Hu, and Gao Huang
<div align="center"> <img src="projects/rank_detr/assets/rank_detr_overview.png"/> </div><br/>Table of Contents
Installation
Please refer to the installation document of detrex.
Pretrained Models
Here we provide the Rank-DETR model pretrained weights based on detrex:
<table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Name</th> <th valign="bottom">Backbone</th> <th valign="bottom">Query Num</th> <th valign="bottom">Epochs</th> <th valign="bottom">AP</th> <th valign="bottom">download</th> <!-- TABLE BODY --> </tr> <tr><td align="left"><a href="configs/rank_detr_r50_two_stage_12ep.py">Rank-DETR</a></td> <td align="center">R50</td> <td align="center">300</td> <td align="center">12</td> <td align="center">50.2</td> <td align="center"><a href="https://cloud.tsinghua.edu.cn/f/4cc3dea3c2f64360894f/?dl=1">model</a></td> </tr> </tbody> </tr> <tr><td align="left"><a href="configs/rank_detr_r50_two_stage_12ep.py">Rank-DETR</a></td> <td align="center">R50</td> <td align="center">300</td> <td align="center">36</td> <td align="center">51.2</td> <td align="center"><a href="https://cloud.tsinghua.edu.cn/f/761f8e9e5bc74d2fa4ce/?dl=1">model</a></td> </tr> </tbody> </tr> <tr><td align="left"><a href="configs/rank_detr_r50_two_stage_12ep.py">Rank-DETR</a></td> <td align="center">Swin Tiny</td> <td align="center">300</td> <td align="center">12</td> <td align="center">52.7</td> <td align="center"><a href="https://cloud.tsinghua.edu.cn/f/b32aae34fce449aa9aca/?dl=1">model</a></td> </tr> </tbody> </tr> <tr><td align="left"><a href="configs/rank_detr_r50_two_stage_12ep.py">Rank-DETR</a></td> <td align="center">Swin Tiny</td> <td align="center">300</td> <td align="center">36</td> <td align="center">54.7 </td> <td align="center"><a href="https://cloud.tsinghua.edu.cn/f/649dc9b265a641f5be5c/?dl=1">model</a></td> </tr> </tbody> </tr> <tr><td align="left"><a href="configs/rank_detr_r50_two_stage_12ep.py">Rank-DETR</a></td> <td align="center">Swin Large</td> <td align="center">300</td> <td align="center">12</td> <td align="center">57.3</td> <td align="center"><a href="https://cloud.tsinghua.edu.cn/f/b03f2e1a148045e78619/?dl=1">model</a></td> </tr> </tbody> </tr> <tr><td align="left"><a href="configs/rank_detr_r50_two_stage_12ep.py">Rank-DETR</a></td> <td align="center">Swin Large</td> <td align="center">300</td> <td align="center">36</td> <td align="center">58.2</td> <td align="center"><a href="https://cloud.tsinghua.edu.cn/f/34912e493fb644dd8bf4/?dl=1">model</a></td> </tr> </tbody></table>Run
Training
All configs can be trained with:
cd detrex
python projects/rank_detr/train_net.py --config-file projects/rank_detr/configs/path/to/config.py --num-gpus 8
- By default, we use 8 GPUs with total batch size as 16 for training.
- To train/eval a model with the swin transformer backbone, you need to download the backbone from the offical repo frist and specify argument
train.init_checkpoint
like our configs.
Evaluation
Model evaluation can be done as follows:
cd detrex
python projects/rank_detr/train_net.py --config-file projects/rank_detr/configs/path/to/config.py --eval-only train.init_checkpoint=/path/to/model_checkpoint
Citing Rank-DETR
If you find Rank-DETR useful in your research, please consider citing:
@inproceedings{pu2023rank,
title={Rank-DETR for High Quality Object Detection},
author={Pu, Yifan and Liang, Weicong and Hao, Yiduo and Yuan, Yuhui and Yang, Yukang and Zhang, Chao and Hu, Han and Huang, Gao},
booktitle={NeurIPS},
year={2023}
}