Changes

Jump to: navigation, search

MISC-TN-011: Running an Azure-generated TensorFlow Lite model on Mito8M SoM using NXP eIQ

35 bytes removed, 09:57, 25 March 2020
→‎Running the application
==Running the application==
The follwoing following block shows the execution of the classifier on the embedded platform:
<pre class="board-terminal">
root@mito8m:~/devel/image_classifier_eIQ# ./image_classifier_cv converted_model.tflite labels.txt testdata/red-apple1.jpg
0.00214239 Green Apple
</pre>
the The prediction time is cut by about 88% compared to [[[[SBCX-TN-005: Using TensorFlow to implement a Deep Learning image classifier based on Azure Custom Vision-generated model|this Technical Note (SBCX-TN-005)|this implementation]]. Of course, this is due to several factors. The more most relevant ones are:
* i.MX8M is faster than i.MX6Q
* The application is written in C++ and not in Python
* The TF model was replaced with a TFL model, which is inherently more suited for ARM-based devices
* The middleware provided by NXP eIQ is optimized for their SoC's.
U0001
Bureaucrats, dave_user, Administrators
4,650
edits
Retrieved from "https://wiki.dave.eu/index.php/Special:MobileDiff/9679"

Navigation menu