Difference between revisions of "ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1"
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==Test application #1: fruit classifier== | ==Test application #1: fruit classifier== | ||
| − | This application implements a classifier like the one described [[MISC-TN-011:_Running_an_Azure-generated_TensorFlow_Lite_model_on_Mito8M_SoM_using_NXP_eIQ|here]]. There is one notable difference, however. In this case, the model was created from scratch using TBD. | + | This application implements a classifier like the one described [[MISC-TN-011:_Running_an_Azure-generated_TensorFlow_Lite_model_on_Mito8M_SoM_using_NXP_eIQ|here]]. There is one notable difference, however, with respect to the linked article. In this case, the model was created from scratch using TBD. |
=== Model creation === | === Model creation === | ||
Revision as of 10:59, 8 September 2020
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Contents
History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | September 2020 | First public release |
Introduction[edit | edit source]
This Technical Note (TN for short) is the first one of a series illustrating how machine learning-based inference applications perform across different embedded platforms, which are eligible for building intelligent edge devices.
The idea is to develop one or more applications with the help of well-known open-source frameworks/libraries and to deploy them on such platforms to compare performances, resource utilization, development flow, etc.
In the following sections, these applications are described in more detail.
Test application #1: fruit classifier[edit | edit source]
This application implements a classifier like the one described here. There is one notable difference, however, with respect to the linked article. In this case, the model was created from scratch using TBD.
Model creation[edit | edit source]
Articles in this series[edit | edit source]
The other articles in this series are:
