# Yolo v4, v3 and v2 for Windows and Linux
## (neural networks for object detection)
Paper YOLO v4: https://arxiv.org/abs/2004.10934
Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
More details in articles on medium:
- [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8)
- [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7)
Manual: https://github.com/AlexeyAB/darknet/wiki
Discussion:
- [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/)
- [Google-groups](https://groups.google.com/forum/#!forum/darknet)
- [Discord](https://discord.gg/zSq8rtW)
About Darknet framework: http://pjreddie.com/darknet/
[![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22)
[![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet)
[![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet)
[![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors)
[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE)
[![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965)
[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934)
[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2011.08036-B31B1B.svg)](https://arxiv.org/abs/2011.08036)
[![colab](https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE)
[![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg)
- [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo)
- [Requirements (and how to install dependencies)](#requirements-for-windows-linux-and-macos)
- [Pre-trained models](#pre-trained-models)
- [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions)
- [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations)
- [Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v4-in-other-frameworks)
- [Datasets](#datasets)
- [Yolo v4, v3 and v2 for Windows and Linux](#yolo-v4-v3-and-v2-for-windows-and-linux)
- [(neural networks for object detection)](#neural-networks-for-object-detection)
- [GeForce RTX 2080 Ti](#geforce-rtx-2080-ti)
- [Youtube video of results](#youtube-video-of-results)
- [How to evaluate AP of YOLOv4 on the MS COCO evaluation server](#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server)
- [How to evaluate FPS of YOLOv4 on GPU](#how-to-evaluate-fps-of-yolov4-on-gpu)
- [Pre-trained models](#pre-trained-models)
- [Requirements for Windows, Linux and macOS](#requirements-for-windows-linux-and-macos)
- [Yolo v4 in other frameworks](#yolo-v4-in-other-frameworks)
- [Datasets](#datasets)
- [Improvements in this repository](#improvements-in-this-repository)
- [How to use on the command line](#how-to-use-on-the-command-line)
- [For using network video-camera mjpeg-stream with any Android smartphone](#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone)
- [How to compile on Linux/macOS (using `CMake`)](#how-to-compile-on-linuxmacos-using-cmake)
- [Using also PowerShell](#using-also-powershell)
- [How to compile on Linux (using `make`)](#how-to-compile-on-linux-using-make)
- [How to compile on Windows (using `CMake`)](#how-to-compile-on-windows-using-cmake)
- [How to compile on Windows (using `vcpkg`)](#how-to-compile-on-windows-using-vcpkg)
- [How to train with multi-GPU](#how-to-train-with-multi-gpu)
- [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
- [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects)
- [When should I stop training](#when-should-i-stop-training)
- [Custom object detection](#custom-object-detection)
- [How to improve object detection](#how-to-improve-object-detection)
- [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
- [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries)
- [Citation](#citation)
![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png)
![scaled_yolov4](https://user-images.githubusercontent.com/4096485/112776361-281d8380-9048-11eb-8083-8728b12dcd55.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036
----
![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934
tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4.
- tkDNN: https://github.com/ceccocats/tkDNN
- OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf
### GeForce RTX 2080 Ti
| Network Size | Darknet, FPS (avg) | tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | OpenCV FP16 batch=4, FPS | tkDNN Speedup |
|:--------------------------:|:------------------:|-------------------------:|-------------------------:|-----------------:|---------------------------------:|-------------------------:|--------------:|
|320 | 100 | 116 | **202** | 183 | 423 | **430** | **4.3x** |
|416 | 82 | 103 | **162** | 159 | 284 | **294** | **3.6x** |
|512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** |
|608 | 53 | 62 | 103 | **115** | 150 | **150** | **2.8x** |
|Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** |
|Tiny 416 CPU Core i7 7700HQ | 3.4 | - | - | 42 | - | 39 | **12x** |
- Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png)
- Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png)
- CSPNet: [paper](https://arxiv.org/abs/1911.11929) and [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
【作品名称】:基于Yolov4的行人检测、行人距离估计、多目标跟踪系统 【适用人群】:适用于希望学习不同技术领域的小白或进阶学习者。可作为毕设项目、课程设计、大作业、工程实训或初期项目立项。 【项目介绍】: 1、先进入编译目录 cd DeepSocial/darknet 2、根据自己是否有GPU修改Makefile文件 vim Makefile 我修改后内容为如下: GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1 AVX=1 OPENMP=1 LIBSO=1 ZED_CAMERA=0 ZED_CAMERA_v2_8=0 3、开始编译 因为是我在自己本地是编译过得,因重新编译之前,先执行一下sudo make clean,删除之前编译生成的文件! sudo make clean sudo make 注意: 如果编译报错:/bin/sh:nvcc not found 解决方式(参考): 1)查看nvcc可执行文件的路径 which nvcc 2)修改Makefile文件中的NVCC=nvcc,把nvcc替换为上面查询到的nvcc可执行文件的
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