ByteTrack超详细教程!训练自己的数据集&&摄像头实时检测跟踪

Overview

#ByteTrack训练自己数据集详细教程!!

一、配置环境

1. Installing on the host machine

Step1. Install ByteTrack.

git clone https://github.com/Double-zh/ByteTrack.git
cd ByteTrack
pip3 install -r requirements.txt
python3 setup.py develop

Step2. Install pycocotools.

pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

Step3. Others

pip3 install cython_bbox

2. Docker build

docker build -t bytetrack:latest .

# Startup sample
mkdir -p pretrained && \
mkdir -p YOLOX_outputs && \
xhost +local: && \
docker run --gpus all -it --rm \
-v $PWD/pretrained:/workspace/ByteTrack/pretrained \
-v $PWD/datasets:/workspace/ByteTrack/datasets \
-v $PWD/YOLOX_outputs:/workspace/ByteTrack/YOLOX_outputs \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
bytetrack:latest

二、准备VOC数据集和下载预训练模型

### 1. datasets
           └——————VOCdevkit
           |         └——————VOC2012
           |                   └——————Annotations
           |                   └——————ImageSets
           |                                 └——————Main
           |                   └——————JPEGImages
                               └—————— divide_dataset.py

2. Download pretrained model

The COCO pretrained YOLOX model can be downloaded from their [model zoo](https://github.com/Megvii-BaseDetection/YOLOX/tree/0.1.0). After downloading the pretrained models, you can put them under 
   
    /pretrained.

   

三、准备模型配置文件{create a Exp file for your dataset && modify get_data_loader and get_eval_loader in your Exp file}

根据需求修改文件yolox_voc_s_ZZH.py的种类数,在路径"exps/example/custom/"文件夹下

class Exp(MyExp):
    def __init__(self):
        super(Exp, self).__init__()
        self.num_classes = 2 #在这进行修改
        self.depth = 0.33
        self.width = 0.50
        self.warmup_epochs = 1

四、Training

Train with custom dataset

cd <ByteTrack_HOME>
python3 train.py -f exps/example/custom/yolox_voc_s_ZZH.py -d 1 -b 1 --fp16 -o -c pretrained/yolox_s.pth

五、Demo

1. 调用摄像头进行实时检测跟踪,并保存结果

cd <ByteTrack_HOME>

python3 ZZH_track.py webcam -f exps/example/custom/yolox_voc_s_ZZH.py -c YOLOX_outputs/yolox_voc_s_ZZH/latest_ckpt.pth.tar --fp16 --fuse --save_result

2. 对视频进行检测跟踪,并保存结果

取消注释ZZH_track.py第227行代码,并注释第228行代码

```shell
cd 
   
    

python3 ZZH_track.py video -f exps/example/custom/yolox_voc_s_ZZH.py -c YOLOX_outputs/yolox_voc_s_ZZH/latest_ckpt.pth.tar --fp16 --fuse --save_result

   

六、Deploy

  1. ONNX export and ONNXRuntime
  2. TensorRT in Python
  3. TensorRT in C++
  4. ncnn in C++

七、Citation

@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}
}

八、Acknowledgement

A large part of the code is borrowed from YOLOX, FairMOT, TransTrack and JDE-Cpp. Many thanks for their wonderful works.

Owner
Double-zh
Double-zh
DIT is a DTLS MitM proxy implemented in Python 3. It can intercept, manipulate and suppress datagrams between two DTLS endpoints and supports psk-based and certificate-based authentication schemes (RSA + ECC).

DIT - DTLS Interception Tool DIT is a MitM proxy tool to intercept DTLS traffic. It can intercept, manipulate and/or suppress DTLS datagrams between t

52 Nov 30, 2022
Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

53 Nov 22, 2022
Official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION.

IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION This is the official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSU

电线杆 14 Dec 15, 2022
Implementation of Diverse Semantic Image Synthesis via Probability Distribution Modeling

Diverse Semantic Image Synthesis via Probability Distribution Modeling (CVPR 2021) Paper Zhentao Tan, Menglei Chai, Dongdong Chen, Jing Liao, Qi Chu,

tzt 45 Nov 17, 2022
Normalizing Flows with a resampled base distribution

Resampling Base Distributions of Normalizing Flows Normalizing flows are a popular class of models for approximating probability distributions. Howeve

Vincent Stimper 24 Nov 03, 2022
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized C

Sam Bond-Taylor 139 Jan 04, 2023
DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

DeepConsensus DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS)

Google 149 Dec 19, 2022
PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners

Masked Autoencoders: A PyTorch Implementation This is a PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners: @

Meta Research 4.8k Jan 04, 2023
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
なりすまし検出(anti-spoof-mn3)のWebカメラ向けデモ

FaceDetection-Anti-Spoof-Demo なりすまし検出(anti-spoof-mn3)のWebカメラ向けデモです。 モデルはPINTO_model_zoo/191_anti-spoof-mn3からONNX形式のモデルを使用しています。 Requirement mediapipe

KazuhitoTakahashi 8 Nov 18, 2022
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
A PyTorch implementation of unsupervised SimCSE

A PyTorch implementation of unsupervised SimCSE

99 Dec 23, 2022
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

SNU ADSL 0 Feb 07, 2022
(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks

Joseph K J 37 Jan 03, 2023
Pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model'

RTK-PAD This is an official pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model', which is accepted by IEEE T

6 Aug 01, 2022
Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train format

ttopt Description Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train (TT) format and maximu

5 May 23, 2022
[ECE NTUA] 👁 Computer Vision - Lab Projects & Theoretical Problem Sets (2020-2021)

Computer Vision - NTUA (2020-2021) This repository hosts the lab projects and theoretical problem sets of the Computer Vision course held by ECE NTUA

Dimitris Dimos 6 Jul 21, 2022
Repository for the paper "PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation", CVPR 2021.

PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation Code repository for the paper: PoseAug: A Differentiable Pose Augme

Pyjcsx 328 Dec 17, 2022
Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"

Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet Implementation detail for our paper "Multi-level colonoscopy malignant ti

CVSM Group - email: <a href=[email protected]"> 84 Nov 22, 2022
Post-Training Quantization for Vision transformers.

PTQ4ViT Post-Training Quantization Framework for Vision Transformers. We use the twin uniform quantization method to reduce the quantization error on

Zhihang Yuan 61 Dec 28, 2022