README_ORIGINAL.md

Introduction

Object Perception & Application (Opera) is a unified toolbox for multiple computer vision tasks: detection, segmentation, pose estimation, etc.

To date, Opera implements the following algorithms:

• PETR (CVPR'2022 Oral)
• SOIT (AAAI'2022)
• InsPose (ACM MM'2021)

Installation

Please refer to get_started.md for installation.

Requirements

• Linux
• Python 3.7+
• PyTorch 1.8+
• CUDA 10.1+
• MMCV
• MMDetection

Getting Started

Please see get_started.md for the basic usage of Opera.

Acknowledgement

Opera is an open source project built upon OpenMMLab. We appreciate all the contributors who implement this flexible and efficient toolkits.

Citations

If you find our works useful in your research, please consider citing:

@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}
}

@inproceedings{yu2022soit,
  title={SOIT: Segmenting Objects with Instance-Aware Transformers},
  author={Yu, Xiaodong and Shi, Dahu and Wei, Xing and Ren, Ye and Ye, Tingqun and Tan, Wenming},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}

@inproceedings{shi2021inspose,
  title={Inspose: instance-aware networks for single-stage multi-person pose estimation},
  author={Shi, Dahu and Wei, Xing and Yu, Xiaodong and Tan, Wenming and Ren, Ye and Pu, Shiliang},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={3079--3087},
  year={2021}
}

License

This project is released under the Apache 2.0 license.