Deep Neural Networks for Computer Vision from Scratch

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Artificial Intelligence

Deep Neural Networks for Computer Vision from Scratch

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

Large pre-trained deep neural networks (dnns) have revolutionized the field of computer vision (cv), but application in industry is often precluded for three reasons : 1) large pre-trained dnns are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained dnns raise legal issues for companies.

As a remedy, we study neural networks for cv that we train from scratch.

We find that our neural networks achieve similar performance as pre-trained dnns, even though they consist of far fewer parameters and do not rely on third-party datasets.

Authors

Maximilian Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus

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