From 66f0225e9232a6ce75a327d198640e198c878ecd Mon Sep 17 00:00:00 2001 From: ZhongdaoWang Date: Wed, 29 Jan 2020 23:23:27 +0800 Subject: [PATCH] Update README.md --- README.md | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index edaae19..99b2308 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ # Towards-Realtime-MOT **NEWS:** +- **[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. - **[2019.10.01]** Demo code and pre-trained model released! ## Introduction -This repo is the a codebase of the Joint Detection and Embedding (JDE) model. 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. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) . +This repo is the a codebase of the Joint Detection and Embedding (JDE) model. 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. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **22~38 FPS** (Note this speed is for the entire system, including the detection step! ) . We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles. @@ -29,7 +30,7 @@ python demo.py --input-video path/to/your/input/video --weights path/to/model/we --output-format video --output-root path/to/output/root ``` -## docker demo example +## Docker demo example ```bash docker build -t towards-realtime-mot docker/ @@ -43,10 +44,24 @@ python demo.py --input-video path/to/your/input/video --weights path/to/model/we ## Dataset zoo Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for detailed description of the training/evaluation datasets. ## Pretrained model and baseline models -Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74) +Darknet-53 ImageNet pretrained model: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74) + +Trained models: + +|Model| MOTA | IDF1 | IDS | FP | FN | FPS | Link | +|-----|------|------|-----|----|----|-----|------| +|JDE-1088x608-uncertainty| 74.8| 67.3| 1189| 5558| 21505| 22.2| [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q) | +|JDE-864x480-uncertainty| 70.8| 65.8| 1279| 5653| 25806| 20.3| [[Google Drive]]() [[Baidu NetDisk]]() | +|JDE-576x320-uncertainty| 63.7| 63.3| 1307| 6657| 32794| 37.9|[[Google Drive]]() [[Baidu NetDisk]]() | + +The performance is tested on the MOT-16 training set, just for reference. Running speed is tested on an Nvidia Titan Xp GPU. For a more comprehensive comparison with other methods you can test on MOT-16 test set and submit a result to the [MOT-16 benchmark](https://motchallenge.net/results/MOT16/?det=Private). Note that the results should be submitted to the private detector track. -JDE-1088x608-uncertainty: [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q) ## Test on MOT-16 Challenge +``` +python track.py --cfg ./cfg/yolov3_1088x608.cfg --weights /path/to/model/weights +``` +By default the script runs evaluation on the MOT-16 training set. If you want to evaluate on the test set, please add `--test-mot16` to the command line. +Results are saved in text files in `$DATASET_ROOT/results/*.txt`. You can also add `--save-images` or `--save-videos` flags to obtain the visualized results. Visualized results are saved in `$DATASET_ROOT/outputs/` ## Training instruction - Download the training datasets.