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# Towards-Realtime-MOT # Towards-Realtime-MOT
**NEWS:** **NEWS:**
- **[2021.06.01]** A [nice re-implementation](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/configs/mot) (and document) by Baidu [PaddlePaddle](https://github.com/PaddlePaddle) team! - **[2021.08.19]** A [pure C++ re-implementation](https://github.com/samylee/Towards-Realtime-MOT-Cpp) by [samylee](https://github.com/samylee). Helpful if you want to deploy JDE in your own project!
- **[2021.06.01]** A [nice re-implementation](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/configs/mot) (and document) by Baidu [PaddlePaddle](https://github.com/PaddlePaddle) team.
- **[2020.07.14]** Our paper is accepted to ECCV 2020! - **[2020.07.14]** Our paper is accepted to ECCV 2020!
- **[2020.01.29]** More models uploaded! The fastest one runs at around **38 FPS!**. - **[2020.01.29]** More models uploaded! The fastest one runs at around **38 FPS!**.
- **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details. - **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details.
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Adding custom datsets is quite simple, all you need to do is to organize your annotation files in the same format as in our training sets. Please refer to [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for the dataset format. Adding custom datsets is quite simple, all you need to do is to organize your annotation files in the same format as in our training sets. Please refer to [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for the dataset format.
## Related Resources ## Related Resources
See also the current SOTA on MOT16 private track, [FairMOT](https://github.com/ifzhang/FairMOT), which follows the framework of JDE. They employ an anchor-free, high-resolution network and thus mitigate the embedding mis-alignment/overlapping issue, and achieves higher performance. The results are surprisingly good -- 68.7 MOTA / 70.4 IDF-1 / 953 IDs ! - [FairMOT](https://github.com/ifzhang/FairMOT): An improved method based on the JDE framework, SOTA performance.
- [CSTrack](https://arxiv.org/pdf/2010.12138.pdf): Better disentangled detection/embedding heads for JDE.
- [JDE-Paddle](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/configs/mot): A nice re-implementation (and document) by Baidu [PaddlePaddle](https://github.com/PaddlePaddle) team.
- [JDE-CPP](https://github.com/samylee/Towards-Realtime-MOT-Cpp): A pure C++ re-implementation by [samylee](https://github.com/samylee). Helpful if you want to deploy JDE in your own project!
## Acknowledgement ## Acknowledgement
A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work! A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!