Inference Using the TF Lite Model

The process of on-device inference involves running a TF Lite model to make predictions based on unknown input data. TF inference APIs support common mobile and embedded platforms such as Android. TF Lite models run through an interpreter to infer from the input data. The interpreter is optimized for resource-constrained devices. It uses a custom memory allocator that results in low initialization and execution latency. Let’s explore the use of the TF Lite interpreter to perform inference in an Android app.

Main steps

The following figure explains the main steps to perform inference using a TF Lite model interpreter.