Feedback I received after training neural networks for clients to embed into their commercial C++ applications:
Client's Feedback To You
Pure Genius. Stephane and his tools DarkHelp and
DarkMark are the best thing that happened to us
and helped us take a leap into deep learning .. He
knows the details and he documents things in a
crisp and clear manner. Very wonderful to work
with, always available and informative ... He went
beyond the scope of the project to make sure we
got everything working on the hardware we
wanted... Highly recommended!
Very knowledgeable and responsive AI/ML engineer, very
professional, a pleasure to work with. Would love to work
with him again!
The tools I use to train and embed artificial neural networks into C++ applications are
All of these neural networks run in C++ using the DarkHelp C++ neural network wrapper library.
Image markup to train these networks was done using DarkMark, an open-source tool I designed and wrote specifically to help train artificial neural networks.
The libdarknet.so C library is 1.1 MiB in size, and the C++ wrapper libdarkhelp.so is 109 KiB in size.
At a cost of just over 1 MiB, any C++ application can easily make use of an artificial neural network trained with Darknet.
This makes it extremely trivial to use in a desktop application, and relatively simple to use even within an embedded device with limited resources, such as a low-cost Raspberry Pi or NVIDIA Jetson Nano.
Using a YOLOv4-Tiny neural network to take apart a Sudoku, and then extracting the parts from the image using OpenCV to find the solution.
Counting wooden dowels on a conveyer belt.
A detailed stop sign tutorial I wrote on how to get started with Darknet, DarkHelp, and DarkMark.
To help people get started with Darknet and DarkMark, I also recorded a video showing how to do the stop sign tutorial.
Click on the image above to view the gallery, or click below to view on Youtube.
Recognizing vehicles, bicycles, and pedestrians.
DarkHelp has new support for annotating videos, not just JPEG and PNG files.
This video was done as a demonstration of applying a neural network to video files. All from within C++ using a dozen lines of code and a library that is ~300 KiB in size!