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| − | __FORCETOC__
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| − | == History ==
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| − | {| class="wikitable" border="1"
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| − | !Version
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| − | !Date
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| − | !Notes
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| − | |-
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| − | |1.0.0
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| − | |October 2021
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| − | |First public release | |
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| − | |} | |
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| − | =Introduction=
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| − | Classification of materials is another interesting use case where Machine Learning (ML for short) opens the doors for promising developments related to smart edge devices. In this Technical Note (TN), different implementations of ML-based material classification applications are compared in terms of performance and development workflow. The classification process makes use of spectroscopy: the model consists of a neural network (NN) fed with measurement data produced by a spectrometer.
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| − | In the rest of the document, we use the expression ''baseline model'' to refer to the model retrieved from TBD and that was used as starting point.
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| − | =Platform: Xilinx Zynq UltraScale+ MPSoC=
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| − | | |
| − | ==Baseline model==
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| − | The tests detailed in this section make use of the baseline model.
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| − | ===NN accelerator: DPU===
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| − | =References=
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| − | * [1] Hadi Parastara, Geert van Kollenburgb, Yannick Weesepoelc, André van den Doelb, Lutgarde Buydensb, Jeroen Jansen, ''Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity'', 2020
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| − | * [2] N. Salamati, C. Fredembach, S. Susstrunk, ''Material Classification Using Color and NIR Images'', 2009
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| − | * [3] Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp, ''Zackory Erickson, Nathan Luskey, Sonia Chernova, and Charles C. Kemp'', 2019
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