Awesome

H-PETR-Pose

This is the official implementation of the paper "DETRs with Hybrid Matching".

Authors: Ding Jia, Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, Weihong Lin, Lei Sun, Chao Zhang, Han Hu

Citing H-PETR-Pose

If you find H-PETR-Pose useful in your research, please consider citing:

@article{jia2022detrs,
  title={DETRs with Hybrid Matching},
  author={Jia, Ding and Yuan, Yuhui and He, Haodi and Wu, Xiaopei and Yu, Haojun and Lin, Weihong and Sun, Lei and Zhang, Chao and Hu, Han},
  journal={arXiv preprint arXiv:2207.13080},
  year={2022}
}

@inproceedings{shi2022end,
  title={End-to-End Multi-Person Pose Estimation With Transformers},
  author={Shi, Dahu and Wei, Xing and Li, Liangqi and Ren, Ye and Tan, Wenming},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11069--11078},
  year={2022}
}

Model ZOO

We provide a set of baseline results and trained models available for download:

<table><tbody>   <th valign="bottom">Name</th> <th valign="bottom">Backbone</th> <th valign="bottom">epochs</th> <th valign="bottom">AP (Reproduced / Reported)</th> <th valign="bottom">download</th>  <tr><td align="left"><a href="configs/petr/petr_r50_8x1_100e_coco_custom.py">Deformable-DETR</a></td> <td align="center">R50</td> <td align="center">100</td> <td align="center">69.3 / 68.8</td> <td align="center"><a href="https://github.com/HDETR/H-PETR-Pose/releases/download/v1.0.0/r50_baseline.pth">model</a></td> <tr><td align="left"><a href="HDETR_opera/configs/petr/petr_r101_8x1_100e_coco_custom.py">Deformable-DETR</a></td> <td align="center">R101</td> <td align="center">100</td> <td align="center">69.9 / 70.0</td> <td align="center"><a href="https://github.com/HDETR/H-PETR-Pose/releases/download/v1.0.0/r101_baseline.pth">model</a></td> </tr> </tr> <tr><td align="left"><a href="configs/petr/petr_swin-l-p4-w7-22kto1k_8x1_100e_coco_custom_droppath0.5.py">Deformable-DETR</a></td> <td align="center">Swin Large</td> <td align="center">100</td> <td align="center">73.3 / 73.1</td> <td align="center"><a href="https://github.com/HDETR/H-PETR-Pose/releases/download/v1.0.0/swinL_baseline.pth">model</a></td> </tr> </tr> <tr><td align="left"><a href="configs/petr/petr_r50_8x1_100e_coco_t900_group5_ffn2x_dp0_custom.py">H-Deformable-DETR</a></td> <td align="center">R50</td> <td align="center">100</td> <td align="center">70.9</td> <td align="center"><a href="https://github.com/HDETR/H-PETR-Pose/releases/download/v1.0.0/r50_H-PETR.pth">model</a></td> </tr> </tr> <tr><td align="left"><a href="configs/petr/petr_r101_8x1_100e_coco_t900_group5_ffn2x_dp0_custom.py">H-Deformable-DETR</a></td> <td align="center">R101</td> <td align="center">100</td> <td align="center">71.0</td> <td align="center"><a href="https://github.com/HDETR/H-PETR-Pose/releases/download/v1.0.0/r101_H-PETR.pth">model</a></td> </tr> </tr> <tr><td align="left"><a href="configs/petr/petr_swin-l-p4-w7-22kto1k_8x1_100e_coco_t900_group5_ffn2x_dp0_custom_droppath0.5.py">H-Deformable-DETR</a></td> <td align="center">Swin Large</td> <td align="center">100</td> <td align="center">74.9</td> <td align="center"><a href="https://github.com/HDETR/H-PETR-Pose/releases/download/v1.0.0/swinL_H-PETR.pth">model</a></td> </tr> </tbody></table>

We use 8 V-100 GPUs and batch_size = 8 for all experiments.
We tune the droppath of Swin Large backbone from 0.3 to 0.5 for experiments of baseline and our method.

Installation

We test our models under python=3.7.10,pytorch=1.10.1,cuda=10.2. Other versions might be available as well.

Please follow get_started.md to install the repo.

Run

To train a model using 8 cards

bash ./tools/dist_train.sh <config_path> 8

To eval a model using 8 cards

bash ./tools/dist_test.sh  <config_path> <checkpoint_path> 8 --eval keypoints