Paper: https://arxiv.org/pdf/1605.06409.pdf

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

Training set: VOC2007+VOC2012
Size: 224x224
Prediction time: 2484 mSec (RPi 4)

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
  
• The Tencent ncnn framework installed. Install ncnn
  
• OpenCV 64 bit installed. Install OpenCV 4.5
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/Rfcn_ncnn/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:
Traffic.jpg
rfcn.bin (download this file from: https://drive.google.com/open?id=1_MO5ipSymIxlBAF2elD3AOJyboIdiKEa )
rfcn.param
Rfcn.cpb
rfcn.cpp

Run Rfcn.cpb with Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.