Boost VS Dlib

git clone https://github.com/boostorg/boost.git -b ${{ inputs.branch }} "${{ inputs.boost-dir }}" --depth 1 git submodule update --depth 1 -q --init tools/boostdep python tools/boostdep/depinst/depinst.py --include library_i_want"

Any thoughts on why it has exactly the same name as a popular source available project that's been around since the 1990s?

earliest archive: https://web.archive.org/web/19991011120524/http://www.boost....

latest commit (today): https://github.com/boostorg/boost/commit/7727baea944c6365301...

naming in 2023: "The Boost project provides free peer-reviewed portable C++ source libraries"

naming in 1999: "The Boost web site provides a repository for free C++ libraries"

Hmm...

include(FetchContent) FetchContent_Declare(boost URL https://github.com/boostorg/boost/releases/download/boost-1.81.0/boost-1.81.0.tar.xz ) FetchContent_MakeAvailable(boost) ...

set(BOOST_INCLUDE_LIBRARIES system thread) # enabled libraries set(BOOST_ENABLE_CMAKE ON) # CMake support FetchContent_Declare(boost GIT_REPOSITORY https://github.com/boostorg/boost.git ...

In fact, boost has cmake files now — pretty recent addition and I haven’t got around to testing, but no reason to think it doesn’t work. https://github.com/boostorg/boost. Also, boost is getting more modular with every release — more and more libraries can be pulled independently with mostly only depending on boost.core. Asio has been like this forever, but Boost.math is a recent example to the bandwagon. You can find an independent release package for math on GitHub now.

Do you build boost from sources? If so, maybe get the tar balls from their Github repository?

dlib

The apparent gender estimates from photos are using dlib, and I really ought to get what I'm doing cleaned up in such a way that other people can use it easily.

I really like dlib's code https://github.com/davisking/dlib

Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.

If you know C++, you don't need anything else. Go and learn APIs for C++ libraries. If you're into DSP, why not study Dlib?.

The data is mostly in this spreadsheet. The apparently facial gender estimates are made with Dlib. The mental health assessments are from Beck's Depression Inventory and the Snaith-Hamilton Pleasure Scale. The graph is made with gnuplot.

The plugin uses dlib library with a very fast HOG detector for both face recognition and detector following the relative examples.

The dlib facial recognition model thinks that I am now a distance of about 0.3 from where I started, which is far enough to start getting many false positive matches, but still within the design intent that different pictures of the same individual will be within 0.6 of each other.

Dlib's face recognition module thinks that I am about 0.25 units away from where I started; its design intent is that distinct individuals will be 0.6 or more apart, although in practice other people start showing up around 0.3.