Bureaucrats, dave_user, Administrators
4,650
edits
Changes
no edit summary
The testbed consists of an [[:Category:SBC-AXEL|SBCX Single Board Computer]] equipped with an i.MX6Q-powered [[:Category:AxelLite|Axel Lite]] system-on-module (SoM).
Regarding the software, the board runs Armbian Buster GNU/Linux distribution, which is described in [[SBCX-TN-004:_Running_Armbian_Buster_(Debian_10)|this TN]].
TensorFlow release is 2.0.0. TensorFlow was built with the proper configuration for the i.MX6 SoC. For more details, please refer to [https://github.com/lhelontra/tensorflow-on-arm this project] by [https://github.com/lhelontra Leonardo Lontra].
==Test application==
The test application is a classical image classifier. The following classes are supported:
<pre>
Avocado
Banana
Green Apple
Hand
Orange
Red Apple
</pre>
The following image shows the application's architecture.
TBD
It mainly consists of the following blocks:
* The top-level application code (Python)
* The TensorFlow platform
* The TensorFlow model
* The OpenCV library.
As stated in the introduction, the classifier is based on a model that was generated with Azure Custom Vision. In particular, the model was retrieved from [https://dev.to/azure/creating-an-image-recognition-solution-with-azure-iot-edge-and-azure-cognitive-services-4n5i this project] by [https://dev.to/gloveboxes Dave Glover]. Glover's project is extremely useful to understand how Custom Vision—and, in general, Azure Cognitive Services—work.
Glover followed the approach suggested by Azure, which makes use of containers. For the sake of simplicity, this Technical Note is based on a simpler strategy, which is closer to the usual approach used in the embedded world. As such, it doesn't make use of any container.
==Performances==
<pre class="board-terminal">
</pre>
