qengineering/tensorflow_lite_segmentation_rpi_32-bit

TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 4.2 FPS

TensorFlow_Lite_Segmentation_RPi_32

TensorFlow Lite Segmentation on a bare Raspberry Pi 32-bit OS.

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

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Papers: https://arxiv.org/abs/1606.00915
Training set: VOC2017
Size: 257x257

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Benchmark.

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)

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Dependencies.

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)

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Installing the app.

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

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Running the app.

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.

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