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# Yolo-v3 and Yolo-v2 for Windows and Linux # Yolo-v3 and Yolo-v2 for Windows and Linux
### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg)
[![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors
More details: http://pjreddie.com/darknet/yolo/
* [Requirements](#requirements)
[![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)
[![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master)
* [Requirements (and how to install dependecies)](#requirements)
* [Pre-trained models](#pre-trained-models) * [Pre-trained models](#pre-trained-models)
* [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations)
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* Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg
* Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg
## "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)"
A Yolo cross-platform Windows and Linux version (for object detection). Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors
This repository is forked from Linux-version: https://github.com/pjreddie/darknet
More details: http://pjreddie.com/darknet/yolo/
This repository supports:
* both Windows and Linux
* both OpenCV 2.x.x and OpenCV <= 4.0
* both cuDNN >= v7
* CUDA >= 7.5
* also create SO-library on Linux and DLL-library on Windows
### Requirements ### Requirements
* Windows or Linux
* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/
* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions))
* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` where are `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png))
* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (set system variable `CUDNN` = `C:\cudnn` where did you unpack cuDNN. On Linux in `.bashrc`-file, on Windows see the [image](https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg) ) * **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows )
* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
* on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15# * on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15#
@ -111,15 +103,15 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector
On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64`
* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25`
* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` * **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg`
* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` * Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4`
* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0` * Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0`
* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` * Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg`
* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` * Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -out_filename res.avi`
* Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` * Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4`
* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` * **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output`
* Yolo v3 Tiny **on GPU #0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4`
* Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25`
* Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox: * Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox:
`./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` `./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map`
@ -501,7 +493,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
* increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision
* check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark * check that each object is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark
* for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more