Comparison of VGG and MobileNet Models for Grass Seed Dataset

Convolutional neural networks (CNN) have transformed the computer vision research area with tremendous success over the traditional machine learning approaches in the last decade. Here, we report the results of an investigation of seed classification problem by using two widely used CNNs, mobile cen...

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Bibliographic Details
Published in:	2021 5th International Conference on Informatics and Computational Sciences (ICICoS) pp. 255 - 259
Main Authors:	Eryigit, Recep, Tugrul, Bulent
Format:	Conference Proceeding
Language:	English
Published:	IEEE 24-11-2021
Subjects:	Computational modeling Computer vision Convolutional neural networks Deep Learning Image resolution Informatics Machine learning MobileNet Seed Classification Training VGG
Online Access:	Get full text
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Summary:	Convolutional neural networks (CNN) have transformed the computer vision research area with tremendous success over the traditional machine learning approaches in the last decade. Here, we report the results of an investigation of seed classification problem by using two widely used CNNs, mobile centric MobileNet and a parameter rich VGG19. We have found that the classification accuracy for both nets strongly depends on the resolution of the images used in the training.
ISSN:	2767-7087
DOI:	10.1109/ICICoS53627.2021.9651865