detrex

• Modular Design. detrex decomposes the Transformer-based detection framework into various components which help users easily build their own customized models.

• Strong Baselines. detrex provides a series of strong baselines for Transformer-based detection models. We have further boosted the model performance from 0.2 AP to 1.1 AP through optimizing hyper-parameters among most of the supported algorithms.

• Easy to Use. detrex is designed to be light-weight and easy for users to use:

  • LazyConfig System for more flexible syntax and cleaner config files.
  • Light-weight training engine modified from detectron2 lazyconfig_train_net.py

Apart from detrex, we also released a repo Awesome Detection Transformer to present papers about Transformer for detection and segmentation.

• DETR (ECCV’2020)
• Deformable-DETR (ICLR’2021 Oral)
• PnP-DETR (ICCV’2021)
• Conditional-DETR (ICCV’2021)
• Anchor-DETR (AAAI 2022)
• DAB-DETR (ICLR’2022)
• DAB-Deformable-DETR (ICLR’2022)
• DN-DETR (CVPR’2022 Oral)
• DN-Deformable-DETR (CVPR’2022 Oral)
• Group-DETR (ICCV’2023)
• DETA (ArXiv’2022)
• DINO (ICLR’2023)
• H-Deformable-DETR (CVPR’2023)
• MaskDINO (CVPR’2023)
• CO-MOT (ArXiv’2023)
• SQR-DETR (CVPR’2023)
• Align-DETR (ArXiv’2023)
• EVA-01 (CVPR’2023 Highlight)
• EVA-02 (ArXiv’2023)
• Focus-DETR (ICCV’2023)

Please see projects for the details about projects that are built based on detrex.

@inproceedings{carion2020end,
  title={End-to-end object detection with transformers},
  author={Carion, Nicolas and Massa, Francisco and Synnaeve, Gabriel and Usunier, Nicolas and Kirillov, Alexander and Zagoruyko, Sergey},
  booktitle={European conference on computer vision},
  pages={213--229},
  year={2020},
  organization={Springer}
}

@inproceedings{
  zhu2021deformable,
  title={Deformable {\{}DETR{\}}: Deformable Transformers for End-to-End Object Detection},
  author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=gZ9hCDWe6ke}
}

@inproceedings{meng2021-CondDETR,
  title       = {Conditional DETR for Fast Training Convergence},
  author      = {Meng, Depu and Chen, Xiaokang and Fan, Zejia and Zeng, Gang and Li, Houqiang and Yuan, Yuhui and Sun, Lei and Wang, Jingdong},
  booktitle   = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  year        = {2021}
}

@inproceedings{
  liu2022dabdetr,
  title={{DAB}-{DETR}: Dynamic Anchor Boxes are Better Queries for {DETR}},
  author={Shilong Liu and Feng Li and Hao Zhang and Xiao Yang and Xianbiao Qi and Hang Su and Jun Zhu and Lei Zhang},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=oMI9PjOb9Jl}
}

@inproceedings{li2022dn,
  title={Dn-detr: Accelerate detr training by introducing query denoising},
  author={Li, Feng and Zhang, Hao and Liu, Shilong and Guo, Jian and Ni, Lionel M and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13619--13627},
  year={2022}
}

@inproceedings{
  zhang2023dino,
  title={{DINO}: {DETR} with Improved DeNoising Anchor Boxes for End-to-End Object Detection},
  author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel Ni and Heung-Yeung Shum},
  booktitle={The Eleventh International Conference on Learning Representations },
  year={2023},
  url={https://openreview.net/forum?id=3mRwyG5one}
}

@InProceedings{Chen_2023_ICCV,
  author    = {Chen, Qiang and Chen, Xiaokang and Wang, Jian and Zhang, Shan and Yao, Kun and Feng, Haocheng and Han, Junyu and Ding, Errui and Zeng, Gang and Wang, Jingdong},
  title     = {Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2023},
  pages     = {6633-6642}
}

@InProceedings{Jia_2023_CVPR,
  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},
  title     = {DETRs With Hybrid Matching},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {19702-19712}
}

@InProceedings{Li_2023_CVPR,
  author    = {Li, Feng and Zhang, Hao and Xu, Huaizhe and Liu, Shilong and Zhang, Lei and Ni, Lionel M. and Shum, Heung-Yeung},
  title     = {Mask DINO: Towards a Unified Transformer-Based Framework for Object Detection and Segmentation},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {3041-3050}
}

@article{yan2023bridging,
  title={Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking},
  author={Yan, Feng and Luo, Weixin and Zhong, Yujie and Gan, Yiyang and Ma, Lin},
  journal={arXiv preprint arXiv:2305.12724},
  year={2023}
}

@InProceedings{Chen_2023_CVPR,
  author    = {Chen, Fangyi and Zhang, Han and Hu, Kai and Huang, Yu-Kai and Zhu, Chenchen and Savvides, Marios},
  title     = {Enhanced Training of Query-Based Object Detection via Selective Query Recollection},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {23756-23765}
}