YOLOv3_SpringEdition

YOLOv3 C++ Windows and Linux interface library. (Train,Detect both)

• Remove pthread,opencv dependency.
• You need only 1 files for YOLO deep-learning.
• Support windows, linux as same interface.

Do you want train YOLOv3 as double click? and detect using YOLOv3 as below?

YOLOv3 detector;
detector.Create("coco.weights", "coco.cfg", "coco.names");
cv::Mat img=cv::imread("a.jpg");
std::vector<BoxSE> boxes = detector.Detect(img, 0.5F);

• Then you've come to the right place.

1. Setup for train.

You need only 2 files for train that are YOLOv3SE_Train.exe and cudnn64_5.dll on Windows. If you are on Linux, then you need only YOLOv3SE_Train. This files are in YOLOv3_SpringEdition/bin.

The requirement interface not changed. Same as pjreddie/darknet.

There is a example training directory Yolov3_SpringEdition_Train/. You can start training using above files.

Actually, all the interfaces are same with YOLOv2. So you can easily train your own data.

The YOLOv3SE_Train.exe's arguments are [base directory],[data file path] and [cfg file path].

And YOLOv3SE_Train.exe is automatically choosing multi-gpu training. and select latest backup weights file.

2. Setup for detect

Just include YOLOv3SE.h and use it. See YOLOv3_SpringEdition_Test/.

Reference

The class YOLOv3 that in YOLOv3SE.h has 3 methods.

void Create(std::string weights,std::string cfg,std::string names);

This method load trained model(weights), network configuration(cfg) and class naming file(names)

• Parameter
  • weights : trained model path(e.g. "obj.weights")
  • cfg : network configuration file(e.g. "obj.cfg")
  • names : class naming file(e.g. "obj.names")

std::vector<BoxSE> Detect(cv::Mat img, float threshold);
std::vector<BoxSE> Detect(std::string file, float threshold);
std::vector<BoxSE> Detect(IplImage* img, float threshold);

This method is detecting objects of file,cv::Mat or IplImage.

• Parameter
  • file : image file path
  • img : 3-channel image.
  • threshold : It removes predictive boxes if there score is less than threshold.

void Release();

Release loaded network.

Technical issue

Original YOLOv3(darknet) is linux version. And AlexeyAB already made YOLOv3 Windows version. But, his detection method is too slow on Windows. I don't know why exactly. Maybe it has bottleneck. So, I converted darknet(YOLOv3 only) again.

Incompatible with yolo v2(darknet19, densenet201, resnet50). It works only on darknet53.

Software requirement

• CMake
• CUDA 8.0(Maybe it works on CUDA9)
• OpenCV
• Visual Studio

Hardware requirement

• NVIDIA GPU