Table Of Contents
Table Of Contents

2. GluonCV C++ Inference Demo

This is a demo tutorial which illustrates how to use existing GluonCV models in c++ environments given exported JSON and PARAMS files.

Please checkout Export Network for instructions of how to export pre-trained models.

Demo usage

# with yolo3_darknet53_voc-0000.params and yolo3_darknet53-symbol.json on disk
./gluoncv-detect yolo3_darknet53_voc demo.jpg
demo

demo

Usage:

SYNOPSIS
        ./gluoncv-detect <model file> <image file> [-o <outfile>] [--class-file <classfile>] [-e
                         <epoch>] [--gpu <gpu>] [-q] [--no-disp] [-t <thresh>]

OPTIONS
        -o, --output <outfile>
                    output image, by default no output

        --class-file <classfile>
                    plain text file for class names, one name per line

        -e, --epoch <epoch>
                    Epoch number to load parameters, by default is 0

        --gpu <gpu> Which gpu to use, by default is -1, means cpu only.
        -q, --quite Quite mode, no screen output
        --no-disp   Do not display image
        -t, --thresh <thresh>
                    Visualize threshold, from 0 to 1, default 0.3.

Source Code and Build Instructions

The C++ demo code and build instructions are available in our repository scripts.

Hint

Prebuilt binaries are available for Linus, Mac OS and Windows.

And you can also build MXNet from source to support C++ inference demo.

Total running time of the script: ( 0 minutes 0.000 seconds)

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