Bureaucrats, dave_user, Administrators
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{{AppliesTo Machine Learning TN}}
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__FORCETOC__
|September 2020
|First public release
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|1.1.0
|November 2020
|Added new articles in the series
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The following block shows its architecture:
<syntaxhighlightpre>
Model: "sequential"
_________________________________________________________________
Trainable params: 4,822,886
Non-trainable params: 0
</syntaxhighlightpre>
The training was done in the cloud using an AWS EC2 server set up ad hoc.
The dataset was created collecting 240 images of 6 different fruits. 75% of the images were used for the training (''training dataset'') and the rest was used for test/validation purposes (''test dataset'', ''validation dataset''). Training Of course, training the model with a greater number of images would have led to better accuracy, but '''it wouldn't have changed the inference time'''. As the primary goal of the applications built upon this model is to benchmark different platforms, this is acceptable. Obviously, this would not be if this were a real-world application.
Several measures were taken to counter the high overfitting tendency due to the small number of images. For instance, new images were synthesized from the existing ones to simulate a larger dataset (''data augmentation''), as shown below:
*[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_3|Part 3: testing application #1 on Xilinx Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit]]
*[[ML-TN-001_-_AI_at_the_edge:_comparison_of_different_embedded_platforms_-_Part_4|Part 4: testing application #1 on NXP i.MX8M Plus EVK]]
