Difference between revisions of "SBCX-TN-005: Using TensorFlow to implement a Deep Learning image classifier based on Azure Custom Vision-generated model"
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Revision as of 10:22, 25 October 2019
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This technical note was validated against specific versions of hardware and software. It may not work with other versions. |
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History[edit | edit source]
| Version | Date | Notes |
|---|---|---|
| 1.0.0 | Ocotber 2019 | First public release |
Introduction[edit | edit source]
Nowadays, Machine Learning (ML) and Deep Learning (DL) technologies are getting popular in the embedded world as well.
Several different approaches are available to deploy such technologies on embedded devices. This Technical Note (TN) describes such an approach, which makes use of a Tensor Flow model generated with Microsoft Azure Custom Vision service.
Testbed configuration[edit | edit source]
The testbed consists of an SBCX Single Board Computer equipped with an i.MX6Q-powered Axel Lite system-on-module (SoM).
Regarding the software, the board runs the Armbian Buster GNU/Linux distribution, which is described in [[SBCX-TN-004:_Running_Armbian_Buster_(Debian_10)|this TN].
