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Faster_RCNN_ncnn for the ncnn framework

output image

Faster RCNN ncnn with the ncnn framework.

License

Paper: https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf

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


Training set: VOC2007
Size: 600x600 (!)
Prediction time: 26042 mSec(!) (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
  • The Tencent ncnn framework installed. Install ncnn
  • 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/Faster_RCNN_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
ZF_faster_rcnn_final.bin (download this file from: https://drive.google.com/open?id=1w3F4PL03SVtvoS_ux_GfCkY0YLMGH-yA )
ZF_faster_rcnn_final.proto
Faster_rcnn.cpb
fasterrcnn.cpp


Running the app.

To run the application load the project file YoloV2.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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