# MoYO-ncnn

**Repository Path**: lijiong16/moyo-ncnn

## Basic Information

- **Project Name**: MoYO-ncnn
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-01-14
- **Last Updated**: 2021-03-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MobileNetV2_YOLOV3 for the ncnn framework
Papers <br/>
https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html <br/>
https://arxiv.org/pdf/1506.02640.pdf <br/>
Training set: VOC2007 <br/>
Size: 352x352 <br/>
Prediction time: 335 mSec (RPi 4) <br/>
<br/>
Special made for a bare Raspberry Pi see: https://qengineering.eu/opencv-c-examples-on-raspberry-pi.html <br/>
<br/>
To extract and run the network in Code::Blocks <br/>
$ mkdir *MyDir* <br/>
$ cd *MyDir* <br/>
$ wget https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn/archive/master.zip <br/>
$ unzip -j master.zip <br/>
Remove master.zip and README.md as they are no longer needed. <br/> 
$ rm master.zip <br/>
$ rm README.md <br/> <br/>
Your *MyDir* folder must now look like this: <br/> 
dog.jpg <br/>
mobilenet_yolov3.bin <br/>
mobilenet_yolov3.param <br/>
MobiYO.cpb <br/>
MobiYO.cpp <br/>
 <br/>
Run MobiYO.cpb with Code::Blocks. Remember, you also need a working OpenCV 4 on your Raspberry. <br/>

![output image]( https://qengineering.eu/images/outcome.png )