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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.
==Workflow and resulting block diagram==
The following picture shows the block diagram of the resulting application and part of the workflow used to build it.
First of all, the TensorFlow (TF) model generated with Microsoft Azure Custom Vision was converted into the TensorFlow Lite (TFL) format.
Then, a new C++ application was written, using the examples provided by TFL as starting points. After debugging this application on a host PC, it was migrated to the edge device (a Mito8M-powered platform, in our case) where it was natively built. The root file system for eIQ, in fact, provides the native C++ compiler as well.
==Running the application==
