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{{WarningMessage|text=This technical note was validated against specific versions of hardware and software. What is described here may not work with other versions.}}
In [[SBCX-TN-005: Using TensorFlow to implement a Deep Learning image classifier based on Azure Custom Vision-generated model|this Technical Note]] (TN for short), a simple image classifier was implemented on the [[:Category:AxelLite|Axel Lite SoM]].
In [[MISC-TN-010: Using NXP eIQ Machine Learning Development Environment with Mito8M SoM|this otherdocument]], it is illustrated how to run [https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ NXP eIQ Machine Learning software] on i.MX8M-powered [[:Category:Mito8M|Mito8M SoM]].
This article combines the results shown in the TN's just mentioned. In other words, it describes how to run the same image classifier used in SBCX-TN-005 with the eIQ software stack. The outcome is an optimized C++ imaging classification application running on Mito8M SoM, which makes use of the eIQ software stack. In terms of hardware and software, the testbed used is the same described [[MISC-TN-010: Using NXP eIQ Machine Learning Development Environment with Mito8M SoM|here]].