A fast C++ implementation of TensorFlow Lite Unet on a bare Raspberry Pi 4.
Once overclocked to 1900 MHz, the app runs at 4.0 FPS!
Special made for a bare Raspberry Pi 4 see Q-engineering deep learning examples
Papers: https://arxiv.org/abs/1606.00915
Training set: VOC2017
Size: 257x257
Frame rate Unet Lite : 4.0 FPS (RPi 4 @ 1900 MHz - 32 bits OS)
Frame rate Unet Lite : 7.2 FPS (RPi 4 @ 1850 MHz - 64 bits OS)
To run the application, you have to:
- TensorFlow Lite framework installed. Install TensorFlow Lite
- OpenCV 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/TensorFlow_Lite_Segmentation_RPi_32/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:
cat.jpg.mp4
deeplabv3_257_mv_gpu.tflite
TestUnet.cpb
Unet.cpp
Run TestUnet.cpb with Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.