Practical test: first ML experiences with Google Coral and TensorFlow Lite


Some Raspberry Pi projects use machine learning to recognize objects, words or gestures, or to process data using neural networks. If you want to run “AI applications” in the form of deep or machine learning (DL, ML) in the field of IoT, for example on an old laptop, Raspberry Pi or other “computers single boards ”(SBC), you come across but quickly to the limits of computing power. Face or object recognition via a camera, for example, runs in slow motion on the Raspi. This may be sufficient for experiments in this area and a “proof of concept”, but not for practical use.

But there is a remedy: Raspi, PC & Co. can be extended with special chips for ML applications. The Google Coral USB3 key used in the suite (from € 69.90) speeds up object recognition on a Raspberry Pi with a camera with a lameness of 3 to 5 at more than 30 images per second. In this article, we take a look at the inner technical workings of the stick and use a sample project to determine what the stick can do.

But hardware alone isn’t enough, you need the right software too. The list of ML applications is long, usually PyTorch, Scikit, for example and especially TensorFlow. We are using the latter in the light version for this article. TensorFlow is a framework developed by Google for data flow oriented programming. The term “tensor” could be seen as a generalization of scalar, vector and matrix. Google uses TensorFlow for speech recognition, GMail, Google Translate, Google Photos, Google Maps and of course Google search. TensorFlow is written in Python and C ++ and besides these two programming languages ​​also supports Go, Java / JavaScript, Haskel, Rust and a few others.

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