Yolo-Windows v1

• You can get new more accurate and faster Yolo v2 Windows by the link: https://github.com/AlexeyAB/darknet

"You Only Look Once: Unified, Real-Time Object Detection"

Here is a Yolo v1 windows version (for object detection)

Contributtors: https://github.com/pjreddie/darknet/graphs/contributors

This repository is forked from: https://github.com/frischzenger/yolo-windows Which is forked from Linux-version: https://github.com/pjreddie/darknet

More details: http://pjreddie.com/darknet/yolov1/

Requires:

• MS Visual Studio 2013 (v120)
• CUDA 7.5 for Windows x64: https://developer.nvidia.com/cuda-75-downloads-archive
• OpenCV 2.4.9: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download
  • To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor
  • To compile with different OpenCV version - change in file yolo_kernels.cu each string #pragma comment(lib, "opencv_core249.lib") from 249 to required version.
  • With OpenCV will show image or video detection in window and store result to test_dnn_out.avi

Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):

• yolo.cfg (1 GB) - require 4 GB GPU-RAM: https://drive.google.com/open?id=0BwRgzHpNbsWBLU04Znc4U1BMZ0k
• yolo-small.cfg (359 MB) - require 2 GB GPU-RAM: https://drive.google.com/open?id=0BwRgzHpNbsWBQzlHeVpZRnhlY2M
• yolo-tiny.cfg (172 MB) - require 1 GB GPU-RAM: https://drive.google.com/open?id=0BwRgzHpNbsWBQlMxcXNBMWtkaFk

Examples of results:

Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg

Example of usage in cmd-files:

• darknet.cmd - initialization with 1 GB model yolo.weights & yolo.cfg and get command prompt: enter the jpg-image-filename
• darknet_demo.cmd - initialization with 1 GB model yolo.weights & yolo.cfg and play your video file which you must rename to: test.mp4
• darknet_demo_small.cmd - initialization with 359 MB small model yolo-small.weights & yolo-small.cfg and play your video file which you must rename to: test.mp4
• darknet_net_cam.cmd - initialization with 1 GB model, play video from network video-camera mjpeg-stream and store result to: test_dnn_out.avi

For using network video-camera mjpeg-stream with any Android smartphone:

1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam

Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam

2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
3. Start Smart WebCam on your phone
4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:

darknet.exe yolo demo yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0

Windows (Visual Studio) Support Now yolo supports windows with Visual Studio. Just change the include directories / library directories to your own ones, and compile the codes with X64 Release mode. You may need pthread-windows while compiling and linking

How to train yourself:

To training for your 20 objects download pre-trained convolutional layers:

• darknet.conv.weights: https://drive.google.com/open?id=0BwRgzHpNbsWBUXdmdXdaa1dDU2s
• extraction.conv.weights: https://drive.google.com/open?id=0BwRgzHpNbsWBaHR1NjZ2T3FKT1E

Then use the following command lines:

• For tiny-model: darknet yolo train yolo-tiny.cfg darknet.conv.weights
• For small-model: darknet yolo train yolo-small.cfg extraction.conv.weights
• For full-model: darknet yolo train yolo.cfg extraction.conv.weights

To train for a different number of objects (not 20) or for different number of sides (not 7), you should change your .cfg-file 3 lines:

• output= (5 x 2 + C) x S x S
• classes= C
• side= S

Where:

• C = number of objects (by default 20)
• S = number of sides (by default 7)

More details: http://pjreddie.com/darknet/yolov1/

How to compile:

1. If you have CUDA 7.5, OpenCV 2.4.9 (C:\opencv_2.4.9) and MSVS 2013 (or 2015) then start MSVS, open yolo-windows\build\darknet\darknet.sln and do the: Build -> Build darknet

2. If you have other version of CUDA (not 7.5) then open yolo-windows\build\darknet\darknet\darknet.vcxproj by using Notepad, find 2 places with "CUDA 7.5" and change it to your CUDA-version, then do step 1

3. If you have other version of OpenCV 2.4.x (not 2.4.9) then you should change pathes after \darknet.sln is opened

3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories

3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories

4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.

How to compile (custom):

Also, you can to create your own darknet.sln & darknet.vcxproj, this example for CUDA 7.5 and OpenCV 2.4.9

Then add to your created project:

• (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:

C:\opencv_2.4.9\opencv\build\include;..\..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir)

• right click on project -> Build dependecies -> Build Customizations -> set check on CUDA 7.5 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
• add to project all .c & .cu files from yolo-windows\src
• (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:

C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);%(AdditionalLibraryDirectories)

• (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:

..\..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies)

• (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions

OPENCV;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)

• compile to .exe (X64 & Release) and put .dll`s near with .exe:

pthreadVC2.dll, pthreadGC2.dll from yolo-windows\3rdparty\dll\x64

cusolver64_75.dll, curand64_75.dll, cudart64_75.dll, cublas64_75.dll - 75 for CUDA 7.5 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5\bin