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English | [简体中文](README_cn.md)
# MCFairMOT (Multi-class FairMOT)
## Table of Contents
- [Introduction](#Introduction)
- [Model Zoo](#Model_Zoo)
- [Getting Start](#Getting_Start)
- [Citations](#Citations)
## Introduction
MCFairMOT is the Multi-class extended version of [FairMOT](https://arxiv.org/abs/2004.01888).
### PP-Tracking real-time MOT system
In addition, PaddleDetection also provides [PP-Tracking](../../../deploy/pptracking/README.md) real-time multi-object tracking system.
PP-Tracking is the first open source real-time Multi-Object Tracking system, and it is based on PaddlePaddle deep learning framework. It has rich models, wide application and high efficiency deployment.
PP-Tracking supports two paradigms: single camera tracking (MOT) and multi-camera tracking (MTMCT). Aiming at the difficulties and pain points of actual business, PP-Tracking provides various MOT functions and applications such as pedestrian tracking, vehicle tracking, multi-class tracking, small object tracking, traffic statistics and multi-camera tracking. The deployment method supports API and GUI visual interface, and the deployment language supports Python and C++, The deployment platform environment supports Linux, NVIDIA Jetson, etc.
### AI studio public project tutorial
PP-tracking provides an AI studio public project tutorial. Please refer to this [tutorial](https://aistudio.baidu.com/aistudio/projectdetail/3022582).
## Model Zoo
### MCFairMOT Results on VisDrone2019 Val Set
| backbone | input shape | MOTA | IDF1 | IDS | FPS | download | config |
| :--------------| :------- | :----: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 24.3 | 41.6 | 2314 | - |[model](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams) | [config](./mcfairmot_dla34_30e_1088x608_visdrone.yml) |
| HRNetV2-W18 | 1088x608 | 20.4 | 39.9 | 2603 | - |[model](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.pdparams) | [config](./mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.yml) |
| HRNetV2-W18 | 864x480 | 18.2 | 38.7 | 2416 | - |[model](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone.pdparams) | [config](./mcfairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone.yml) |
| HRNetV2-W18 | 576x320 | 12.0 | 33.8 | 2178 | - |[model](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone.pdparams) | [config](./mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone.yml) |
**Notes:**
- MOTA is the average MOTA of 10 categories in the VisDrone2019 MOT dataset, and its value is also equal to the average MOTA of all the evaluated video sequences. Here we provide the download [link](https://bj.bcebos.com/v1/paddledet/data/mot/visdrone_mcmot.zip) of the dataset.
- MCFairMOT used 4 GPUs for training 30 epochs. The batch size is 6 on each GPU for MCFairMOT DLA-34, and 8 for MCFairMOT HRNetV2-W18.
### MCFairMOT Results on VisDrone Vehicle Val Set
| backbone | input shape | MOTA | IDF1 | IDS | FPS | download | config |
| :--------------| :------- | :----: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 37.7 | 56.8 | 199 | - |[model](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.pdparams) | [config](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml) |
| HRNetV2-W18 | 1088x608 | 35.6 | 56.3 | 190 | - |[model](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_vehicle_bytetracker.pdparams) | [config](./mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_vehicle_bytetracker.yml) |
**Notes:**
- MOTA is the average MOTA of 4 categories in the VisDrone Vehicle dataset, and this dataset is extracted from the VisDrone2019 MOT dataset, here we provide the download [link](https://bj.bcebos.com/v1/paddledet/data/mot/visdrone_mcmot_vehicle.zip).
- The tracker used in MCFairMOT model here is ByteTracker.
### MCFairMOT off-line quantization results on VisDrone Vehicle val-set
| Model | Compression Strategy | Prediction Delay（T4） |Prediction Delay（V100）| Model Configuration File |Compression Algorithm Configuration File |
| :--------------| :------- | :------: | :----: | :----: | :----: |
| DLA-34 | baseline | 41.3 | 21.9 |[Configuration File](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)| - |
| DLA-34 | off-line quantization | 37.8 | 21.2 |[Configuration File](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)|[Configuration File](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/post_quant/mcfairmot_ptq.yml)|
## Getting Start
### 1. Training
Training MCFairMOT on 4 GPUs with following command
```bash
python -m paddle.distributed.launch --log_dir=./mcfairmot_dla34_30e_1088x608_visdrone/ --gpus 0,1,2,3 tools/train.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml
```
### 2. Evaluation
Evaluating the track performance of MCFairMOT 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/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams
# use saved checkpoint in training
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=output/mcfairmot_dla34_30e_1088x608_visdrone/model_final.pdparams
```
**Notes:**
- The default evaluation dataset is VisDrone2019 MOT val-set. If you want to change the evaluation dataset, please refer to the following code and modify `configs/datasets/mcmot.yml`：
```
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
data_root: your_dataset/images/val
keep_ori_im: False # set True if save visualization images or video
```
- Tracking results will be saved in `{output_dir}/mot_results/`, and every sequence has one txt file, each line of the txt file is `frame,id,x1,y1,w,h,score,cls_id,-1,-1`, and you can set `{output_dir}` by `--output_dir`.
### 3. Inference
Inference a video 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/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.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`.
### 4. Export model
```bash
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams
```
### 5. Using exported model for python inference
```bash
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/mcfairmot_dla34_30e_1088x608_visdrone --video_file={your video name}.mp4 --device=GPU --save_mot_txts
```
**Notes:**
- The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
- Each line of the tracking results txt file is `frame,id,x1,y1,w,h,score,cls_id,-1,-1`.
### 6. Off-line quantization
The offline quantization model is calibrated using the VisDrone Vehicle val-set, running as:
```bash
CUDA_VISIBLE_DEVICES=0 python3.7 tools/post_quant.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml --slim_config=configs/slim/post_quant/mcfairmot_ptq.yml
```
**Notes:**
- Offline quantization uses the VisDrone Vehicle val-set dataset and a 4-class vehicle tracking model by default.
## 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}
}
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3119563}
}
@article{zhang2021bytetrack,
title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
journal={arXiv preprint arXiv:2110.06864},
year={2021}
}
```

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简体中文 | [English](README.md)
# MCFairMOT (Multi-class FairMOT)
## 内容
- [简介](#简介)
- [模型库](#模型库)
- [快速开始](#快速开始)
- [引用](#引用)
## 内容
MCFairMOT是[FairMOT](https://arxiv.org/abs/2004.01888)的多类别扩展版本。
### PP-Tracking 实时多目标跟踪系统
此外，PaddleDetection还提供了[PP-Tracking](../../../deploy/pptracking/README.md)实时多目标跟踪系统。PP-Tracking是基于PaddlePaddle深度学习框架的业界首个开源的实时多目标跟踪系统，具有模型丰富、应用广泛和部署高效三大优势。
PP-Tracking支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式，针对实际业务的难点和痛点，提供了行人跟踪、车辆跟踪、多类别跟踪、小目标跟踪、流量统计以及跨镜头跟踪等各种多目标跟踪功能和应用，部署方式支持API调用和GUI可视化界面，部署语言支持Python和C++，部署平台环境支持Linux、NVIDIA Jetson等。
### AI Studio公开项目案例
PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking之手把手玩转多目标跟踪](https://aistudio.baidu.com/aistudio/projectdetail/3022582)。
## 模型库
### MCFairMOT 在VisDrone2019 MOT val-set上结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 24.3 | 41.6 | 2314 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams) | [配置文件](./mcfairmot_dla34_30e_1088x608_visdrone.yml) |
| HRNetV2-W18 | 1088x608 | 20.4 | 39.9 | 2603 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.pdparams) | [配置文件](./mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.yml) |
| HRNetV2-W18 | 864x480 | 18.2 | 38.7 | 2416 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone.pdparams) | [配置文件](./mcfairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone.yml) |
| HRNetV2-W18 | 576x320 | 12.0 | 33.8 | 2178 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone.pdparams) | [配置文件](./mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone.yml) |
**注意:**
- MOTA是VisDrone2019 MOT数据集10类目标的平均MOTA, 其值也等于所有评估的视频序列的平均MOTA，此处提供数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/mot/visdrone_mcmot.zip)。
- MCFairMOT模型均使用4个GPU进行训练，训练30个epoch。DLA-34骨干网络的每个GPU上batch size为6，HRNetV2-W18骨干网络的每个GPU上batch size为8。
### MCFairMOT 在VisDrone Vehicle val-set上结果
| 骨干网络 | 输入尺寸 | MOTA | IDF1 | IDS | FPS | 下载链接 | 配置文件 |
| :--------------| :------- | :----: | :----: | :---: | :------: | :----: |:----: |
| DLA-34 | 1088x608 | 37.7 | 56.8 | 199 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.pdparams) | [配置文件](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml) |
| HRNetV2-W18 | 1088x608 | 35.6 | 56.3 | 190 | - |[下载链接](https://paddledet.bj.bcebos.com/models/mot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_vehicle_bytetracker.pdparams) | [配置文件](./mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_vehicle_bytetracker.yml) |
**注意:**
- MOTA是VisDrone Vehicle数据集4类车辆目标的平均MOTA, 该数据集是VisDrone数据集中抽出4类车辆类别组成的，此处提供数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/mot/visdrone_mcmot_vehicle.zip)。
- MCFairMOT模型此处使用的跟踪器是使用的ByteTracker。
### MCFairMOT 在VisDrone Vehicle val-set上离线量化结果
| 骨干网络 | 压缩策略 | 预测时延（T4） |预测时延（V100）| 配置文件 |压缩算法配置文件 |
| :--------------| :------- | :------: | :----: | :----: | :----: |
| DLA-34 | baseline | 41.3 | 21.9 |[配置文件](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)| - |
| DLA-34 | 离线量化 | 37.8 | 21.2 |[配置文件](./mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/post_quant/mcfairmot_ptq.yml)|
## 快速开始
### 1. 训练
使用4个GPU通过如下命令一键式启动训练
```bash
python -m paddle.distributed.launch --log_dir=./mcfairmot_dla34_30e_1088x608_visdrone/ --gpus 0,1,2,3 tools/train.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml
```
### 2. 评估
使用单张GPU通过如下命令一键式启动评估
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=output/mcfairmot_dla34_30e_1088x608_visdrone/model_final.pdparams
```
**注意:**
- 默认评估的是VisDrone2019 MOT val-set数据集, 如需换评估数据集可参照以下代码修改`configs/datasets/mcmot.yml`：
```
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
data_root: your_dataset/images/val
keep_ori_im: False # set True if save visualization images or video
```
- 多类别跟踪结果会存于`{output_dir}/mot_results/`中，里面每个视频序列对应一个txt，每个txt文件每行信息是`frame,id,x1,y1,w,h,score,cls_id,-1,-1`, 此外`{output_dir}`可通过`--output_dir`设置。
### 3. 预测
使用单个GPU通过如下命令预测一个视频，并保存为视频
```bash
# 预测一个视频
CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams --video_file={your video name}.mp4 --save_videos
```
**注意:**
- 请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
### 4. 导出预测模型
```bash
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone.pdparams
```
### 5. 用导出的模型基于Python去预测
```bash
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/mcfairmot_dla34_30e_1088x608_visdrone --video_file={your video name}.mp4 --device=GPU --save_mot_txts
```
**注意:**
- 跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。
- 多类别跟踪结果txt文件每行信息是`frame,id,x1,y1,w,h,score,cls_id,-1,-1`。
### 6. 离线量化
使用 VisDrone Vehicle val-set 对离线量化模型进行校准，运行方式：
```bash
CUDA_VISIBLE_DEVICES=0 python3.7 tools/post_quant.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.yml --slim_config=configs/slim/post_quant/mcfairmot_ptq.yml
```
**注意:**
- 离线量化默认使用的是VisDrone Vehicle val-set数据集以及4类车辆跟踪模型。
## 引用
```
@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}
}
@ARTICLE{9573394,
author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Detection and Tracking Meet Drones Challenge},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3119563}
}
@article{zhang2021bytetrack,
title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
journal={arXiv preprint arXiv:2110.06864},
year={2021}
}
```

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_BASE_: [
'../fairmot/fairmot_dla34_30e_1088x608.yml',
'../../datasets/mcmot.yml'
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/fairmot_dla34_crowdhuman_pretrained.pdparams
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
weights: output/mcfairmot_dla34_30e_1088x608_visdrone/model_final
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL

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_BASE_: [
'../fairmot/fairmot_dla34_30e_1088x608.yml',
'../../datasets/mcmot.yml'
]
metric: MCMOT
num_classes: 4
# for MCMOT training
TrainDataset:
!MCMOTDataSet
dataset_dir: dataset/mot
image_lists: ['visdrone_mcmot_vehicle.train']
data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
label_list: label_list.txt
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
data_root: visdrone_mcmot_vehicle/images/val
keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
anno_path: dataset/mot/visdrone_mcmot_vehicle/label_list.txt
# for MCMOT video inference
TestMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
keep_ori_im: True # set True if save visualization images or video
anno_path: dataset/mot/visdrone_mcmot_vehicle/label_list.txt
pretrain_weights: https://paddledet.bj.bcebos.com/models/centernet_dla34_140e_coco.pdparams
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
use_byte: True
match_thres: 0.8
conf_thres: 0.4
low_conf_thres: 0.2
weights: output/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker/model_final
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL

47
paddle_detection/configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone.yml Normal file
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_BASE_: [
'../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml',
'../../datasets/mcmot.yml'
]
architecture: FairMOT
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/HRNet_W18_C_pretrained.pdparams
for_mot: True
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
weights: output/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone/model_final
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL
TrainReader:
batch_size: 8

78
paddle_detection/configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_vehicle_bytetracker.yml Normal file
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_BASE_: [
'../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml',
'../../datasets/mcmot.yml'
]
metric: MCMOT
num_classes: 4
# for MCMOT training
TrainDataset:
!MCMOTDataSet
dataset_dir: dataset/mot
image_lists: ['visdrone_mcmot_vehicle.train']
data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
label_list: label_list.txt
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
data_root: visdrone_mcmot_vehicle/images/val
keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
anno_path: dataset/mot/visdrone_mcmot_vehicle/label_list.txt
# for MCMOT video inference
TestMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
keep_ori_im: True # set True if save visualization images or video
anno_path: dataset/mot/visdrone_mcmot_vehicle/label_list.txt
architecture: FairMOT
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/HRNet_W18_C_pretrained.pdparams
for_mot: True
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
use_byte: True
match_thres: 0.8
conf_thres: 0.4
low_conf_thres: 0.2
weights: output/mcfairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_vehicle_bytetracker/model_final
epoch: 30
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [15, 22]
use_warmup: True
- !ExpWarmup
steps: 1000
power: 4
OptimizerBuilder:
optimizer:
type: Momentum
regularizer:
factor: 0.0001
type: L2
TrainReader:
batch_size: 8

64
paddle_detection/configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot.yml Normal file
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_BASE_: [
'../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml',
'../../datasets/mcmot.yml'
]
metric: MCMOT
num_classes: 11
weights: output/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_bdd100k_mcmot/model_final
# for MCMOT training
TrainDataset:
!MCMOTDataSet
dataset_dir: dataset/mot
image_lists: ['bdd100k_mcmot.train']
data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
label_list: label_list.txt
EvalMOTDataset:
!MOTImageFolder
dataset_dir: dataset/mot
data_root: bdd100k_mcmot/images/val
keep_ori_im: False
# model config
architecture: FairMOT
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/HRNet_W18_C_pretrained.pdparams
for_mot: True
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL
TrainReader:
batch_size: 8

47
paddle_detection/configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone.yml Normal file
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_BASE_: [
'../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml',
'../../datasets/mcmot.yml'
]
architecture: FairMOT
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/HRNet_W18_C_pretrained.pdparams
for_mot: True
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
weights: output/mcfairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone/model_final
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL
TrainReader:
batch_size: 8

47
paddle_detection/configs/mot/mcfairmot/mcfairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone.yml Normal file
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@@ -0,0 +1,47 @@
_BASE_: [
'../fairmot/fairmot_hrnetv2_w18_dlafpn_30e_864x480.yml',
'../../datasets/mcmot.yml'
]
architecture: FairMOT
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/HRNet_W18_C_pretrained.pdparams
for_mot: True
FairMOT:
detector: CenterNet
reid: FairMOTEmbeddingHead
loss: FairMOTLoss
tracker: JDETracker # multi-class tracker
CenterNetHead:
regress_ltrb: False
CenterNetPostProcess:
regress_ltrb: False
max_per_img: 200
JDETracker:
min_box_area: 0
vertical_ratio: 0 # no need to filter bboxes according to w/h
conf_thres: 0.4
tracked_thresh: 0.4
metric_type: cosine
weights: output/mcfairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone/model_final
epoch: 30
LearningRate:
base_lr: 0.0005
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [10, 20]
use_warmup: False
OptimizerBuilder:
optimizer:
type: Adam
regularizer: NULL
TrainReader:
batch_size: 8