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test/yolov7-tracker/README.md
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# YOLO detector and SOTA Multi-object tracker Toolbox
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## ❗❗Important Notes
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Compared to the previous version, this is an ***entirely new version (branch v2)***!!!
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**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.**
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```bash
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git clone https://github.com/JackWoo0831/Yolov7-tracker.git
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git checkout v2 # change to v2 branch !!
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```
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🙌 ***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.
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<div align="center">
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**Language**: English | [简体中文](README_CN.md)
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</div>
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## ❤️ Introduction
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This repo is a toolbox that implements the **tracking-by-detection paradigm multi-object tracker**. The detector supports:
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- YOLOX
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- YOLO v7
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- YOLO v8,
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and the tracker supports:
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- SORT
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- DeepSORT
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- ByteTrack ([ECCV2022](https://arxiv.org/pdf/2110.06864))
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- Bot-SORT ([arxiv2206](https://arxiv.org/pdf/2206.14651.pdf))
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- OCSORT ([CVPR2023](https://openaccess.thecvf.com/content/CVPR2023/papers/Cao_Observation-Centric_SORT_Rethinking_SORT_for_Robust_Multi-Object_Tracking_CVPR_2023_paper.pdf))
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- C_BIoU Track ([arxiv2211](https://arxiv.org/pdf/2211.14317v2.pdf))
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- Strong SORT ([IEEE TMM 2023](https://arxiv.org/pdf/2202.13514))
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- Sparse Track ([arxiv 2306](https://arxiv.org/pdf/2306.05238))
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and the reid model supports:
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- OSNet
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- Extractor from DeepSort
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The highlights are:
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- Supporting more trackers than MMTracking
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- Rewrite multiple trackers with a ***unified code style***, without the need to configure multiple environments for each tracker
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- Modular design, which ***decouples*** the detector, tracker, reid model and Kalman filter for easy conducting experiments
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## 🗺️ Roadmap
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- [ x ] Add StrongSort and SparseTrack
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- [ x ] Add save video function
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- [ x ] Add timer function to calculate fps
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- [] Add more ReID modules.
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## 🔨 Installation
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The basic env is:
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- Ubuntu 18.04
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- Python：3.9, Pytorch: 1.12
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Run following commond to install other packages:
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```bash
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pip3 install -r requirements.txt
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```
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### 🔍 Detector installation
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1. YOLOX:
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The version of YOLOX is **0.1.0 (same as ByteTrack)**. To install it, you can clone the ByteTrack repo somewhere, and run:
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``` bash
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https://github.com/ifzhang/ByteTrack.git
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python3 setup.py develop
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```
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2. YOLO v7:
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There is no need to execute addtional steps as the repo itself is based on YOLOv7.
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3. YOLO v8:
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Please run:
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```bash
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pip3 install ultralytics==8.0.94
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```
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### 📑 Data preparation
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***If you do not want to test on the specific dataset, instead, you only want to run demos, please skip this section.***
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***No matter what dataset you want to test, please organize it in the following way (YOLO style):***
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```
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dataset_name
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|---images
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|---train
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|---sequence_name1
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|---000001.jpg
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|---000002.jpg ...
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|---val ...
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|---test ...
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```
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You can refer to the codes in `./tools` to see how to organize the datasets.
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***Then, you need to prepare a `yaml` file to indicate the path so that the code can find the images.***
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Some examples are in `tracker/config_files`. The important keys are:
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```
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DATASET_ROOT: '/data/xxxx/datasets/MOT17' # your dataset root
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SPLIT: test # train, test or val
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CATEGORY_NAMES: # same in YOLO training
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- 'pedestrian'
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CATEGORY_DICT:
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0: 'pedestrian'
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```
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## 🚗 Practice
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### 🏃 Training
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Trackers generally do not require parameters to be trained. Please refer to the training methods of different detectors to train YOLOs.
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Some references may help you:
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- YOLOX: `tracker/yolox_utils/train_yolox.py`
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- YOLO v7:
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```shell
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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
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```
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- YOLO v8: `tracker/yolov8_utils/train_yolov8.py`
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### 😊 Tracking !
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If you only want to run a demo:
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```bash
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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
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```
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For example:
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```bash
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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
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```
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If you want to run trackers on dataset:
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```bash
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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}
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```
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For example:
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- SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sort --kalman_format sort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt `
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- DeepSORT: `python tracker/track.py --dataset uavdt --detector yolov7 --tracker deepsort --kalman_format byte --detector_model_path weights/yolov7_UAVDT_35epochs_20230507.pt`
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- ByteTrack: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker bytetrack --kalman_format byte --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`
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- OCSort: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker ocsort --kalman_format ocsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`
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- 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`
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- 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`
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- Strong SORT: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker strongsort --kalman_format strongsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`
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- Sparse Track: `python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format bot --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt`
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### ✅ Evaluation
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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).
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