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https://github.com/hikvision-research/opera
A Unified Toolbox for Object Perception & Application
https://github.com/hikvision-research/opera
Last synced: 2 months ago
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A Unified Toolbox for Object Perception & Application
- Host: GitHub
- URL: https://github.com/hikvision-research/opera
- Owner: hikvision-research
- License: apache-2.0
- Created: 2022-03-23T09:23:35.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-05T07:56:56.000Z (about 1 year ago)
- Last Synced: 2024-08-04T22:13:14.416Z (6 months ago)
- Language: Python
- Homepage:
- Size: 284 KB
- Stars: 145
- Watchers: 7
- Forks: 12
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-human-pose-estimation - [code - to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.pdf) - CVPR 22, PETR (Multi-Person 2D Pose Estimation / 2022)
README
## Introduction
**O**bject **Per**ception & **A**pplication (Opera) is a unified toolbox for multiple computer vision tasks: detection, segmentation, pose estimation, etc.
To date, Opera implements the following algorithms:
- [DK-DETR (ICCV'2023)](configs/dk-detr)
- [PETR (CVPR'2022 Oral)](configs/petr)
- [SOIT (AAAI'2022)](configs/soit)
- [InsPose (ACM MM'2021)](configs/inspose)
## Installation
Please refer to [get_started.md](docs/get_started.md) for installation.
## Requirements
- Linux
- Python 3.7+
- PyTorch 1.8+
- CUDA 10.1+
- [MMCV](https://mmcv.readthedocs.io/en/latest/#installation)
- [MMDetection](https://mmdetection.readthedocs.io/en/latest/#installation)
## Getting Started
Please see [get_started.md](docs/get_started.md) for the basic usage of Opera.
## Acknowledgement
Opera is an open source project built upon [OpenMMLab](https://github.com/open-mmlab/). 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:
```BibTeX
@inproceedings{li2023distilling,
title={Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object Detection},
author={Li, Liangqi and Miao, Jiaxu and Shi, Dahu and Tan, Wenming and Ren, Ye and Yang, Yi and Pu, Shiliang},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={6501--6510},
year={2023}
}
@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},
pages={3188--3196},
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](LICENSE).