A fast C++ implementation of TensorFlow Lite on a bare Raspberry Pi 4. Once overclocked to 2000 MHz, the app runs an amazing 17 FPS! Without any hardware accelerator, just you and your Pi.
https://arxiv.org/abs/1611.10012
Training set: COCO
Size: 300x300
Frame rate V1 Lite : 17 FPS (RPi 4 @ 2000 MHz - 32 bits OS)
Frame rate V1 Lite : 24 FPS (RPi 4 @ 1925 MHz - 64 bits OS) see https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits
Special made for a bare Raspberry Pi see: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits/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:
James.mp4
COCO_labels.txt
detect.tflite
TestTensorFlow_Lite.cpb
MobileNetV1.cpp
Run TestTensorFlow_Lite.cpb with Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.
See the movie at: https://youtu.be/uspw6KztkeQ
