Automate testing of TensorFlow Lite model implementation

Testing TensorFlow Lite model with Espresso and instrumentation tests on Android

Making sure that your ML model works correctly on mobile app (part 2)

This is the 2nd article about testing machine learning models created for mobile. In the previous post – Testing TensorFlow Lite image classification model, we built a notebook that exports TensorFlow model to TensorFlow Lite and compares them side by side. But because the conversion process is mostly automatic, there are not many places to break something. We can find differences between quantized and non-quantized models or ensure that TensorFlow Lite works similarily to TensorFlow, but the real issues can come up somewhere else – on the client side implementation.
In this article, I will suggest some solutions for testing TensorFlow Lite model with Android instrumentation tests.

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