English | [简体中文](README_cn.md) # FairMOT (FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking) ## Table of Contents - [Introduction](#Introduction) - [Model Zoo](#Model_Zoo) - [Getting Start](#Getting_Start) - [Citations](#Citations) ## Introduction [FairMOT](https://arxiv.org/abs/2004.01888) is based on an Anchor Free detector Centernet, which overcomes the problem of anchor and feature misalignment in anchor based detection framework. The fusion of deep and shallow features enables the detection and ReID tasks to obtain the required features respectively. It also uses low dimensional ReID features. FairMOT is a simple baseline composed of two homogeneous branches propose to predict the pixel level target score and ReID features. It achieves the fairness between the two tasks and obtains a higher level of real-time MOT performance. ## Model Zoo ### FairMOT Results on MOT-16 Training Set | backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | DLA-34(paper) | 1088x608 | 83.3 | 81.9 | 544 | 3822 | 14095 | - | - | - | | DLA-34 | 1088x608 | 83.7 | 83.3 | 435 | 3829 | 13764 | - | [model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml) | ### FairMOT Results on MOT-16 Test Set | backbone | input shape | MOTA | IDF1 | IDS | FP | FN | FPS | download | config | | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: | | DLA-34(paper) | 1088x608 | 74.9 | 72.8 | 1074 | - | - | 25.9 | - | - | | DLA-34 | 1088x608 | 74.8 | 74.4 | 930 | 7038 | 37994 | - | [model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml) | **Notes:** FairMOT used 8 GPUs for training and mini-batch size as 6 on each GPU, and trained for 30 epoches. ## Getting Start ### 1. Training Training FairMOT on 8 GPUs with following command ```bash python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml ``` ### 2. Evaluation Evaluating the track performance of FairMOT 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/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams # use saved checkpoint in training CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=output/fairmot_dla34_30e_1088x608/model_final.pdparams ``` **Notes:** The default evaluation dataset is MOT-16 Train Set. If you want to change the evaluation dataset, please refer to the following code and modify `configs/datasets/mot.yml`: ``` EvalMOTDataset: !MOTImageFolder task: MOT17_train dataset_dir: dataset/mot data_root: MOT17/images/train keep_ori_im: False # set True if save visualization images or video ``` ### 3. Inference Inference a vidoe on single GPU with following command: ```bash # inference on video and save a video CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams --video_file={your video name}.mp4 --save_videos ``` **Notes:** Please make sure that [ffmpeg](https://ffmpeg.org/ffmpeg.html) is installed first, on Linux(Ubuntu) platform you can directly install it by the following command:`apt-get update && apt-get install -y ffmpeg`. ## Citations ``` @article{zhang2020fair, title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking}, author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu}, journal={arXiv preprint arXiv:2004.01888}, year={2020} } ```