Abstract
Computer vision is one of the basic features to streamline processes like robotic process automation and digital asset management. Computer vision has come a long way in terms of its capabilities and what it can provide and do for different industries. Object detection and image detection are just some of the few applications provided by computer vision. However, this field is still relatively young and prone to challenges. The first is the lack of wellannotated images to train the algorithms to perform optimally, and the second being lack of accuracy when applied to real-world images different from the ones from the training dataset. As such, this paper aims to fine-tune pre-trained machine learning models, which are ResNet50 and VGG19 as well as training a new SqueezeNet inspired model from scratch to create a flower recognition model that can process and remember large amounts of flower species data. In conclusion, VGG19 was found to perform the best on both the 5 Categories and Flower-102 dataset, with an accuracy of 88% and 84% respectively.
Keywords: VGG19, Transfer Learning, Deep Learning, Flower Recognition, Neural Network
Authors
Ong Zi Yuan [1] ; Chye Kah Kien [2] ; Kang Huay Wen [3] ; Tan Chi Wee [4]
[1][2][3][4] Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[1] ongzy-wp17@student.tarc.edu.my
Cite Me
Plain Text:
Z.Y.Ong, K.K.Chye, H.W.Kang, C.W.Tan, "A Flower Recognition System using Deep Neural Network Coupled with Visual Geometry Group 19 Architecture," International Conference on Digital Transformation and Applications (ICDXA) 2021, 2021, pp. 121-128, doi: https://doi.org/10.56453/icdxa.2021.1012.
BibTex:
@INPROCEEDINGS{ICDXA202112,
author={Ong, Zi Yuan and Chye, Kah Kien and Kang, Huay Wen and Tan, Chi Wee},
booktitle={International Conference on Digital Transformation and Applications (ICDXA) 2021},
title={A Flower Recognition System using Deep Neural Network Coupled with Visual Geometry Group 19 Architecture},
year={2021},
volume={},
number={},
pages={121-128},
doi={https://doi.org/10.56453/icdxa.2021.1012}}
