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A toolkit for making real world machine learning and data analysis applications

Project description

dlib C++ library

Dlib is a modern C++ toolkit containing machine learning algorithms and tools
for creating complex software in C++ to solve real world problems. See
http://dlib.net for the main project documentation and API reference.



COMPILING DLIB C++ EXAMPLE PROGRAMS
Go into the examples folder and type:
mkdir build; cd build; cmake .. ; cmake --build .
That will build all the examples. If you have a CPU that supports AVX
instructions then turn them on like this:
mkdir build; cd build; cmake .. -DUSE_AVX_INSTRUCTIONS=1; cmake --build .
Doing so will make some things run faster.

COMPILING DLIB Python API
Before you can run the Python example programs you must compile dlib. Type:
python setup.py install
or type
python setup.py install --yes USE_AVX_INSTRUCTIONS
if you have a CPU that supports AVX instructions, since this makes some
things run faster.

RUNNING THE UNIT TEST SUITE
Type the following to compile and run the dlib unit test suite:
cd dlib/test
mkdir build
cd build
cmake ..
cmake --build . --config Release
./dtest --runall

Note that on windows your compiler might put the test executable in a
subfolder called Release. If that's the case then you have to go to that
folder before running the test.

This library is licensed under the Boost Software License, which can be found
in dlib/LICENSE.txt. The long and short of the license is that you can use
dlib however you like, even in closed source commercial software.

Dlib Sponsors:
This research is based in part upon work supported by the Office of the
Director of National Intelligence (ODNI), Intelligence Advanced Research
Projects Activity (IARPA) under contract number 2014-14071600010. The
views and conclusions contained herein are those of the authors and
should not be interpreted as necessarily representing the official policies
or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S.
Government.

Version: 19.1
Date: Sat Aug 13 14:09:22 EDT 2016
Mercurial Revision ID: 680e88eb8f29

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