Update README.md

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.