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H-PETR-Pose

arXiv visitors

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:

Name Backbone epochs AP (Reproduced / Reported) download
Deformable-DETR R50 100 69.3 / 68.8 model
Deformable-DETR R101 100 69.9 / 70.0 model
Deformable-DETR Swin Large 100 73.3 / 73.1 model
H-Deformable-DETR R50 100 70.9 model
H-Deformable-DETR R101 100 71.0 model
H-Deformable-DETR Swin Large 100 74.9 model
  • 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

Modified files compared to Opera

To support Hybrid-branch

  • opera/models/dense_heads/petr_head.py
  • opera/models/dense_heads/__init__.py

To support checkpoint

  • opera/models/utils/transformer.py
  • opera/models/utils/__init__.py