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/
yolo.cfg
(1 GB) - require 4 GB GPU-RAM: https://drive.google.com/open?id=0BwRgzHpNbsWBLU04Znc4U1BMZ0kyolo-small.cfg
(359 MB) - require 2 GB GPU-RAM: https://drive.google.com/open?id=0BwRgzHpNbsWBQzlHeVpZRnhlY2Myolo-tiny.cfg
(172 MB) - require 1 GB GPU-RAM: https://drive.google.com/open?id=0BwRgzHpNbsWBQlMxcXNBMWtkaFkOthers: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
darknet.cmd
- initialization with 1 GB model yolo.weights & yolo.cfg and get command prompt: enter the jpg-image-filenamedarknet_demo.cmd
- initialization with 1 GB model yolo.weights & yolo.cfg and play your video file which you must rename to: test.mp4darknet_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.mp4darknet_net_cam.cmd
- initialization with 1 GB model, play video from network video-camera mjpeg-stream and store result to: test_dnn_out.aviDownload 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
Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
Start Smart WebCam on your phone
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
To training for your 20 objects download pre-trained convolutional layers:
Then use the following command lines:
darknet yolo train yolo-tiny.cfg darknet.conv.weights
darknet yolo train yolo-small.cfg extraction.conv.weights
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:
More details: http://pjreddie.com/darknet/yolov1/
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
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
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
If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.
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:
C:\opencv_2.4.9\opencv\build\include;..\..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir)
C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);%(AdditionalLibraryDirectories)
..\..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies)
OPENCV;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)
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