{"id":22354726,"url":"https://github.com/pprp/simple_deep_sort","last_synced_at":"2026-02-09T06:05:53.123Z","repository":{"id":112699237,"uuid":"228027134","full_name":"pprp/simple_deep_sort","owner":"pprp","description":"deep sort with yolov3. 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Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in [PAPER](https://arxiv.org/abs/1703.07402) is FasterRCNN , and the original source code is [HERE](https://github.com/nwojke/deep_sort).  \nHowever in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use **YOLOv3** to generate bboxes instead of FasterRCNN.\n\n## Dependencies\n- python 3 (python2 not sure)\n- numpy\n- scipy\n- opencv-python\n- sklearn\n- pytorch 0.4 or 1.x\n\n## Quick Start\n0. Check all dependencies installed\n```bash\npip install -r requirements.txt\n```\nfor user in china, you can specify pypi source to accelerate install like:\n```bash\npip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple \n```\n\n1. Clone this repository\n```\ngit clone git@github.com:ZQPei/deep_sort_pytorch.git\n```\n\n2. Download YOLOv3 parameters\n```\ncd YOLOv3/\nwget https://pjreddie.com/media/files/yolov3.weights\ncd ..\n```\n\n3. Download deepsort parameters ckpt.t7\n```\ncd deep_sort/deep/checkpoint\n# download ckpt.t7 from \nhttps://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder\ncd ../../../\n```  \n\n4. Run demo\n```\nusage: demo_yolo3_deepsort.py VIDEO_PATH\n                              [--help] \n                              [--yolo_cfg YOLO_CFG]\n                              [--yolo_weights YOLO_WEIGHTS]\n                              [--yolo_names YOLO_NAMES]\n                              [--conf_thresh CONF_THRESH]\n                              [--nms_thresh NMS_THRESH]\n                              [--deepsort_checkpoint DEEPSORT_CHECKPOINT]\n                              [--max_dist MAX_DIST] [--ignore_display]\n                              [--display_width DISPLAY_WIDTH]\n                              [--display_height DISPLAY_HEIGHT]\n                              [--save_path SAVE_PATH]          \n```\n\nAll files above can also be accessed from BaiduDisk!  \nlinker：https://pan.baidu.com/s/1TEFdef9tkJVT0Vf0DUZvrg  \npasswd：1eqo  \n\n## Training the RE-ID model\nThe original model used in paper is in original_model.py, and its parameter here [original_ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6).  \n\nTo train the model, first you need download [Market1501](http://www.liangzheng.org/Project/project_reid.html) dataset or [Mars](http://www.liangzheng.com.cn/Project/project_mars.html) dataset.  \n\nThen you can try [train.py](deep_sort/deep/train.py) to train your own parameter and evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py).\n![train.jpg](deep_sort/deep/train.jpg)\n\n## Demo videos and images\n[demo.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6)\n[demo2.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6)\n\n![1.jpg](images/1.jpg)\n![2.jpg](images/2.jpg)\n\n\n## References\n- paper: [Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402)\n\n- code: [nwojke/deep_sort](https://github.com/nwojke/deep_sort)\n\n- paper: [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf)\n\n- code: [Joseph Redmon/yolov3](https://pjreddie.com/darknet/yolo/)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpprp%2Fsimple_deep_sort","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpprp%2Fsimple_deep_sort","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpprp%2Fsimple_deep_sort/lists"}