Getting Started with TensorFlow Lite for Microcontrollers on i.MX RT

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This lab will cover how to take an existing TensorFlow Lite model and run it on NXP MCU devices using the TensorFlow Lite for Microcontrollers inference engine. It will use the Flower model generated as part of the eIQ Toolkit lab as an example, but the same process can be used for other TFLite models. eIQ provides examples that incorporate an LCD and camera alongside the inference engine, and so the EVK boards can be used to identify different types of flowers.

This lab can also be used without a camera+LCD, but in that scenario the flowers images will need to be converted to a C array and loaded at compile time. 

Attached to this post you will find:

• Photos to test out the new model
• A lab document on how to do 'transfer learning' on a TensorFlow model and then run that TFLite model on the i.MX RT family using TensorFlow Lite for Microcontrollers. The use of the camera+LCD is optional.

  • If have camera+LCD use: eIQ TensorFlow Lite for Microcontrollers for i.MX RT170 - With Camera.pdf
  • If do not have camera or LCD use: eIQ TensorFlow Lite for Microcontrollers for i.MX RT170 - Without Camera.pdf
  • If using the RT685 use: eIQ TensorFlow Lite for Microcontrollers for i.MX RT685 - Without Camera.pdf

This lab supports the following boards:

FRDM-MCXN947

i.MX RT685-EVK

i.MX RT1050-EVKB

i.MX RT1060-EVK

i.MX RT1064-EVK

i.MX RT1160-EVK

i.MX RT1170-EVK

i.MX RT1180-EVK

Updated November 2024 for MCUXpresso SDK 2.16 and eIQ Toolkit 1.13.1