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MobileNetV1_SSD Caffe with OpenCV on RaspBerry Pi

Home Page: https://qengineering.eu/deep-learning-software-for-raspberry-pi-and-alternatives.html

License: BSD 3-Clause "New" or "Revised" License

C++ 100.00%
mobilenet-ssd opencv caffe raspberry-pi dnn mobilenetv1-ssd-caffe testopencv-caffe cpp

mobilenetv1_ssd_opencv_caffe's Introduction

MobileNetV1_SSD for the Deep Learning module of OpenCV

output image

A example of OpenCV dnn framework working on a bare Raspberry Pi with Caffe models.

License

Paper: https://arxiv.org/pdf/1612.08242.pdf

Special made for a bare Raspberry Pi 4 see Q-engineering deep learning examples


Training set: VOC2007
Size: 300x300
Frame rate: 3.66 FPS (RPi 4)


Dependencies.

To run the application, you have to:

  • A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
  • OpenCV 64 bit installed. Install OpenCV 4.5
  • Code::Blocks installed. ($ sudo apt-get install codeblocks)

Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md

Your MyDir folder must now look like this:
04545.jpg
MobileNetSSD_deploy.caffemodel
MobileNetSSD_deploy.prototxt
TestOpenCV_Caffe.cpb
MobileNet.cpp


Running the app.

To run the application load the project file TestOpenCV_Caffe.cbp in Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.


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