Difference between revisions of "MISC-TN-011: Running an Azure-generated TensorFlow Lite model on Mito8M SoM using NXP eIQ"
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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 imaging classification application written in C++ running on Mito8M SoM and that makes use of eIQ software stack. | 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 imaging classification application written in C++ running on Mito8M SoM and that makes use of eIQ software stack. | ||
| + | ==Workflow and resulting block diagram== | ||
Revision as of 09:18, 25 March 2020
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This technical note was validated against specific versions of hardware and software. What is described here may not work with other versions. |
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History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | March 2020 | First public release |
Introduction[edit | edit source]
In this Technical Note (SBCX-TN-005) (TN for short), a simple image classifier was implemented on the Axel Lite SoM.
In this TN (MISC-TN-010), it is illustrated how to run NXP eIQ Machine Learning software on i.MX8M-powered 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 imaging classification application written in C++ running on Mito8M SoM and that makes use of eIQ software stack.
