The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's ...
May 21, 2021 · The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this ...
Distinguishing artefacts: evaluating the saturation point of convolutional neural networks · Ric Real, J. Gopsill, +2 authors. B. Hicks · Published in arXiv.org ...
Jun 3, 2021 · The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this ...
The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's ...
This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the- ...
Distinguishing artefacts: Evaluating the saturation point of convolutional neural networks. Procedia CIRP, 100, 385-390. https://doi.org/10.1016/j.procir ...
Distinguishing artefacts: evaluating the saturation point of convolutional neural networks. Ric Real*, James A Gopsill, David Edward Jones, Chris M Snider ...
Distinguishing artefacts: Evaluating the saturation point of convolutional neural networks. Ricardo Real, James Gopsill, David Jones, Chris Snider, Ben Hicks.
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Distinguishing artefacts: evaluating the saturation point of convolutional neural networks · no code implementations • 21 May 2021 • Ric Real, James Gopsill ...