Visual studio project files for Darknet-cpp
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arapaho Update for yolov3 and CUDA8 May 2, 2018
darknet Update for yolov3 and CUDA8 May 2, 2018 Support for yolov3 May 2, 2018


Windows support for Darknet-cpp. This repository provides visual studio project files for Darknet-cpp. This repository does not require additional libraries like pthread, and provides all requirements integrated. It supports Yolo v3.

  • For 2015 Visual Studio
  • Needs CUDA 8.0 (and its environment settings correctly defined), and OpenCV3

Steps to build darknet-cpp-windows:

  • Download the darknet-cpp port, from

  • At the same level as the darknet folder, clone or download the darknet-cpp-windows sources

  • Copy the "common" folder from Nvidia GPU Computing Toolkit into darknet-cpp-windows\darknet\ folder (in the same location as the darknet.vcxproj file).

  • Open the below solution file in Visual Studio


  • Change the OpenCV folder path if needed (default expected to be 2 levels above, ....\opencv3\build\x64\vc12\lib)

  • Build the project arapaho, which will also build the darknet project dependency

  • Copy the required data, weight, cfg and input image file for detection into the arapaho folder. All the files need to be named as {input.cfg,, input.jpg or input.mp4, input.weights}. The names can be changed in the darknet\arapaho\test.cpp file.

  • Run the generated binary arapaho.exe, for detection, from


  • This will run the arapaho C++ wrapper, and generate output for the provided image, and show each frame with detected regions in a Window. Example output below:

.... Image data = 000001362EF87060, w = 992, h = 620 Detect: Resizing image to match network l.softmax_tree = 0000000000000000, nms = 0.400000 Detected 1 objects Box #0: x,y,w,h = [0.406386, 0.283149, 0.384096, 0.509924]

The detected regions will be overlaid and shown in the Window that will open showing each frame.

For a brief about Arapaho C++ API, refer to