Darknet-cpp project is a bug-fixed and C++ compilable version of darknet, an open source neural network framework written in C and CUDA. Darknet-cpp builds on Linux, Windows and also tested on Mac by users.
Features
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Uses same code-base as original darknet (ie same .c files are used). Modification is done only for runtime bug-fixes, compile time fixes for c++, and the build system itself. For list of bugs fixed, refer to this thread - https://groups.google.com/forum/#!topic/darknet/4Hb159aZBbA, and https://github.com/prabindh/darknet/issues
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The Linux build system supports 3 targets -
- original darknet (with gcc compiler),
- darknet-cpp (with g++ compiler and Visual Studio compiler), and
- Shared library (libdarknet-cpp-shared.so)
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Can use bounding boxes directly from Euclid object labeller (https://github.com/prabindh/euclid)
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C++ API - arapaho, that works in conjunction with libdarknet-cpp-shared.so, and a test wrapper that can read images or video files, and show detected regions in a complete C++ application.
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darknet-cpp supports OpenCV3. Tested on Ubuntu 16.04 and windows, with CUDA 8.x
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Note: darknet-cpp requires a C++11 compiler for arapaho builds.
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Note - LSTM changes in mainline, are in the branch "merge_lstm_gru_jun17". Master always is stable.
Usage
Using the Makefile in the root directory of the darknet source repository,
make darknet- only darknet (original code), with OPENCV=0make darknet-cpp- only the CPP version, with OPENCV=1make darknet-cpp-shared- build the shared-lib version (without darknet.c calling wrapper), OPENCV=1make arapaho- build arapaho and its test wrapper (from within arapaho folder)
Steps to train (Yolov2)
- Download latest commit of darknet-cpp, ex
git clone https://github.com/prabindh/darknet
- Create Yolo compatible training data-set. I use this to create Yolo compatible bounding box format file, and training list file.
https://github.com/prabindh/euclid
This creates a training list file (train.txt) that will be needed in next step of training.
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Change the files per below:
- yolo-voc.cfg - change line classes=20 to suit desired number of classes
- yolo-voc.cfg - change the number of filters in the CONV layer above the region layer - (#classes + 4 + 1)*(5), where 4 is '#of coords', and 5 is 'num' in the cfg file.
- voc.data - change line classes=20, and paths to training image list file
- voc.names - number of lines must be equal the number of classes
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Place label-images corresponding to name of classes in data/labels, ex - data/labels/myclassname1.png
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Download http://pjreddie.com/media/files/darknet19_448.conv.23
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Train as below
./darknet-cpp detector train ./cfg/voc-myclasses.data ./cfg/yolo-myconfig.cfg darknet19_448.conv.23- Atleast for the few initial iterations, observe the log output, and ensure all images are found and being used. After convergence, detection can be performed using standard steps.
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Testing with Arapaho C++ API for detection
Arapaho needs the darknet-cpp shared library (.so file on Linux, .dll on Windows). This can be built as below on Linux.
make darknet-cpp-sharedOn Windows port, the .dll is built by default.
Refer the file https://github.com/prabindh/darknet/blob/master/arapaho/arapaho_readme.txt for more details.
If there is a need to report an issue with the darknet-cpp port, use the link - https://github.com/prabindh/darknet/issues.
Information required for filing an issue:
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Output of
git log --format="%H" -n 1 -
Options enabled in Makefile (GPU,CUDNN)
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If using Arapaho C++ wrapper, what options were used to build
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Platform being used (OS version, GPU type, CUDA version, and OpenCV version)
Currently tested with VS2013, CUDA8.0 on Win10.
The solution file requires the below repository.
https://github.com/prabindh/darknet-cpp-windows
The Windows port does not require any additional downloads (like pthreads), and builds the same darknet code-base for Windows, to generate the darknet.dll. Building the Arapaho C++ API and test wrapper, creates arapaho.exe, that works exactly the same way as arapaho on Linux.
Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
For more information see the Darknet project website.
For questions or issues please use the Google Group.