[LEGACY] Converting a TensorFlow Lite Model


The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Converting a TensorFlow Lite Model article.

To convert a TensorFlow Lite model, use the mo script and specify the path to the input .tflite model file:

mo --input_model <INPUT_MODEL>.tflite

TensorFlow Lite models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the inference example for more details. Using convert_model is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.


The convert_model() method returns ov.Model that you can optimize, compile, or save to a file for subsequent use.

Supported TensorFlow Lite Layers

For the list of supported standard layers, refer to the Supported Operations page.

Supported TensorFlow Lite Models

More than eighty percent of public TensorFlow Lite models are supported from open sources TensorFlow Hub and MediaPipe. Unsupported models usually have custom TensorFlow Lite operations.