English | [简体中文](README_cn.md) # JDE (Towards-Realtime-MOT) ## Table of Contents - [Introduction](#Introduction) - [Model Zoo](#Model_Zoo) - [Getting Start](#Getting_Start) ## Introduction [Joint Detection and Embedding](https://arxiv.org/abs/1909.12605)(JDE) is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network.
## Model Zoo ### JDE on MOT-16 training set | backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config | | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: | | DarkNet53 | 1088x608 | 73.2 | 69.4 | 1320 | 6613 | 21629 | - |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_1088x608.yml) | **Notes:** JDE used 8 GPUs for training and mini-batch size as 4 on each GPU, and trained for 30 epoches. ## Getting Start ### 1. Training Training JDE on 8 GPUs with following command ```bash python -m paddle.distributed.launch --log_dir=./jde_darknet53_30e_1088x608/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/jde/jde_darknet53_30e_1088x608.yml &>jde_darknet53_30e_1088x608.log 2>&1 & ``` ### 2. Evaluation Evaluating the track performance of JDE on val dataset in single GPU with following commands: ```bash # use weights released in PaddleDetection model zoo CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams # use saved checkpoint in training CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608.yml -o weights=output/jde_darknet53_30e_1088x608/model_final ``` ### 3. Inference Inference a vidoe in single GPU with following commands. ```bash # inference on video CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py configs/mot/jde/jde_darknet53_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams --video_file={your video name}.mp4 ``` ## Citations ``` @article{wang2019towards, title={Towards Real-Time Multi-Object Tracking}, author={Wang, Zhongdao and Zheng, Liang and Liu, Yixuan and Wang, Shengjin}, journal={arXiv preprint arXiv:1909.12605}, year={2019} } ```