Artificial intelligence on the Raspberry Pi
Conclusions
AI applications with TensorFlow Lite and OpenCV have long been considered established tools and are suitable for production use. However, installing the individual libraries and frameworks on the Raspberry Pi involves a fair amount of time and overhead – especially because the documentation is often either outdated or lacking. For this reason alone, it makes sense to check out recent tutorials and examples to familiarize yourself gradually with AI applications on the Raspberry Pi.
Infos
- TensorFlow: https://www.tensorflow.org/
- TensorFlow Lite: https://www.tensorflow.org/lite
- Raspberry Pi OS: https://www.raspberrypi.com/software/operating-systems/#raspberry-pi-os-64-bit
- FlatBuffers: https://flatbuffers.dev/
- Model conversion: https://www.tensorflow.org/lite/models/convert
- OpenCV: https://opencv.org/
- Q-engineering: https://qengineering.eu/
- Code::Blocks IDE: https://www.codeblocks.org/
- Code examples: https://github.com/qengineering
- Tutorial: https://qengineering.eu/opencv-c-examples-on-raspberry-pi.html
- Teachable Machine image model: https://teachablemachine.withgoogle.com/train/image
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