C++ Interface for classification

1. classification: Reference : demo
2. detection: yolo3 interface

./detect [model_name_prefix] [image]

Build libmxnet.so from source

• For all platforms, the first step is to build MXNet from source, with USE_CPP_PACKAGE = 1. Details are available on MXNet website.
• Build cpp inference demo with mxnet cpp-package support.
• mkdir lib in project and make install libmxnet.so there

We will go through with cpu versions, gpu versions of mxnet are similar but requires USE_CUDA=1 and USE_CUDNN=1 (optional). See MXNet website if interested.

Linux (cpu + openblas)

We use Ubuntu as example in Linux section.

1. Install build tools and git

sudo apt-get update
sudo apt-get install -y build-essential git
# install openblas
sudo apt-get install -y libopenblas-dev
# install opencv
sudo apt-get install -y libopencv-dev
# install cmake
sudo apt-get install -y cmake

2. Download MXNet source and build shared library

cd ~
git clone --recursive https://github.com/apache/incubator-mxnet.git
cd incubator-mxnet
make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CPP_PACKAGE=1

3. (optional) Add MXNet to LD_LIBRARY_PATH

export LD_LIBRARY_PATH=~/incubator-mxnet/lib

4. Build demo application

cd ~
git clone https://github.com/dmlc/gluon-cv.git
cd gluon-cv/scripts/deployment/cpp-inference
mkdir build
cd build
cmake .. -DMXNET_ROOT=~/incubator-mxnet
make -j $(nproc)

5. (optional) Copy app to install directory

make install
# gluoncv-detect and libmxnet.so will be available at ~/gluon-cv/scripts/deployment/cpp-inference/install/
# you may want to add libmxnet.so to LD_LIBRARY_PATH