# YOLO detector and SOTA Multi-object tracker Toolbox

## ❗❗Important Notes

Compared to the previous version, this is an ***entirely new version (branch v2)***!!!

**Please use this version directly, as I have almost rewritten all the code to ensure better readability and improved results, as well as to correct some errors in the past code.**

```bash 
git clone https://github.com/JackWoo0831/Yolov7-tracker.git
git checkout v2  # change to v2 branch !!
```

🙌 ***If you have any suggestions for adding trackers***, please leave a comment in the Issues section with the paper title or link! Everyone is welcome to contribute to making this repo better.

<div align="center">

**Language**: English | [简体中文](README_CN.md)

</div>

## ❤️ Introduction

This repo is a toolbox that implements the **tracking-by-detection paradigm multi-object tracker**. The detector supports:

- YOLOX 
- YOLO v7
- YOLO v8, 

and the tracker supports:

- SORT
- DeepSORT 
- ByteTrack ([ECCV2022](https://arxiv.org/pdf/2110.06864))
- Bot-SORT ([arxiv2206](https://arxiv.org/pdf/2206.14651.pdf))
- OCSORT ([CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.pdf))
- C_BIoU Track ([arxiv2211](https://arxiv.org/pdf/2211.14317v2.pdf))
- Strong SORT ([IEEE TMM 2023](https://arxiv.org/pdf/2202.13514))
- Sparse Track ([arxiv 2306](https://arxiv.org/pdf/2306.05238))

and the reid model supports:

- OSNet
- Extractor from DeepSort

The highlights are:
- Supporting more trackers than MMTracking
- Rewrite multiple trackers with a ***unified code style***, without the need to configure multiple environments for each tracker 
- Modular design, which ***decouples*** the detector, tracker, reid model and Kalman filter for easy conducting experiments

![gif](figure/demo.gif)

## 🗺️ Roadmap

- [ x ] Add StrongSort and SparseTrack
- [ x ] Add save video function
- [ x ] Add timer function to calculate fps
- [] Add more ReID modules.

##  🔨 Installation

The basic env is:
- Ubuntu 18.04
- Python：3.9, Pytorch: 1.12

Run following commond to install other packages:

```bash
pip3 install -r requirements.txt
```

### 🔍 Detector installation

1. YOLOX:

The version of YOLOX is **0.1.0 (same as ByteTrack)**. To install it, you can clone the ByteTrack repo somewhere, and run:

``` bash
https://github.com/ifzhang/ByteTrack.git

python3 setup.py develop
```

2. YOLO v7:

There is no need to execute addtional steps as the repo itself is based on YOLOv7.

3. YOLO v8:

Please run:

```bash
pip3 install ultralytics==8.0.94
```

### 📑 Data preparation

***If you do not want to test on the specific dataset, instead, you only want to run demos, please skip this section.***

***No matter what dataset you want to test, please organize it in the following way (YOLO style):***

```
dataset_name
     |---images
           |---train
                 |---sequence_name1
                             |---000001.jpg
                             |---000002.jpg ...
           |---val ...
           |---test ...

     |

```

You can refer to the codes in `./tools` to see how to organize the datasets.

***Then, you need to prepare a `yaml` file to indicate the path so that the code can find the images.***

Some examples are in `tracker/config_files`. The important keys are:

```
DATASET_ROOT: '/data/xxxx/datasets/MOT17'  # your dataset root
SPLIT: test  # train, test or val
CATEGORY_NAMES:  # same in YOLO training
  - 'pedestrian'

CATEGORY_DICT:
  0: 'pedestrian'
```



## 🚗 Practice 

### 🏃 Training 

Trackers generally do not require parameters to be trained. Please refer to the training methods of different detectors to train YOLOs.

Some references may help you:

- YOLOX: `tracker/yolox_utils/train_yolox.py`

- YOLO v7:

```shell
python train_aux.py --dataset visdrone --workers 8 --device <$GPU_id$> --batch-size 16 --data data/visdrone_all.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights <$YOLO v7 pretrained model path$> --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
```  

- YOLO v8: `tracker/yolov8_utils/train_yolov8.py`



### 😊 Tracking ! 

If you only want to run a demo:

```bash
python tracker/track_demo.py --obj ${video path or images folder path} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path} --save_images
```

For example:

```bash
python tracker/track_demo.py --obj M0203.mp4 --detector yolov8 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt --save_images
```

If you want to run trackers on dataset:

```bash
python tracker/track.py --dataset ${dataset name, related with the yaml file} --detector ${yolox, yolov8 or yolov7} --tracker ${tracker name} --kalman_format ${kalman format, sort, byte, ...} --detector_model_path ${detector weight path}
```

For example:

- SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sort --kalman_format sort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt `

- DeepSORT: `python tracker/track.py --dataset uavdt --detector yolov7 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov7_UAVDT_35epochs_20230507.pt`

- ByteTrack: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker bytetrack --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`

- OCSort: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker ocsort --kalman_format ocsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`

- C-BIoU Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker c_bioutrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`

- BoT-SORT: `python tracker/track.py --dataset uavdt --detector yolox --tracker botsort --kalman_format bot --detector_model_path weights/yolox_m_uavdt_50epochs.pth.tar`

- Strong SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker strongsort --kalman_format strongsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`

- Sparse Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`

### ✅ Evaluation 

Coming Soon. As an alternative, after obtaining the result txt file, you can use the [Easier to use TrackEval repo](https://github.com/JackWoo0831/Easier_To_Use_TrackEval).