Accurate and efficient galaxy classification based on mobile vision transformer

Research Article
Open access

Accurate and efficient galaxy classification based on mobile vision transformer

Xinrui Tan 1*
  • 1 School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, 4072, Australia    
  • *corresponding author xinrui.tan@uqconnect.edu.au
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/33/20230245
ACE Vol.33
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-291-6
ISBN (Online): 978-1-83558-292-3

Abstract

Understanding the formation and evolution of galaxies in observational cosmology heavily relies on galaxy morphological classification. Nevertheless, the continuously growing volume of astronomical data has surpassed human capacity for manual classification. In this context, deep learning presents a promising approach to enhancing classifying galaxies. In this paper, the Mobile Vision Transformer (MobileViT) is introduced to construct an efficient and accurate galaxy classifier. Transfer learning is introduced to assist in model fine-tuning. MobileViT combines the features of MobileNet and Visual Transformer (ViT). A lightweight model is used to effectively analyse the relationships between sequences for efficient and accurate classification. Experiments are built on Galaxy10 DECals dataset. Excellent performance is achieved in identifying galaxy types compared to other lightweight models. The model achieves an accuracy of over 87% and maintains a high speed of inference of less than 50 milliseconds per step. Experimental results show that the introduction of MobileViT is the best solution for efficient galaxy classification. The model can be deployed on any portable device for instant observation and classification.

Keywords:

morphological classification, mobile vision transformer, transfer learning, lightweight

Tan,X. (2024). Accurate and efficient galaxy classification based on mobile vision transformer. Applied and Computational Engineering,33,118-125.
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References

[1]. Naim A Lahav O Sodre Jr L Storrie-Lombardi M C 1995 Automated morphological classification of APM galaxies by supervised artificial neural networks Monthly Notices of the Royal Astronomical Society 275(3): pp 567-590

[2]. Owens E A Griffiths R E Ratnatunga K U 1996 Using oblique decision trees for the morphological classification of galaxies Monthly Notices of the Royal Astronomical Society 281(1): pp 153-157

[3]. Dieleman S Willett K W Dambre J 2015 Rotation-invariant convolutional neural networks for galaxy morphology prediction Monthly notices of the royal astronomical society 450(2): pp 1441-1459

[4]. Kim E J Brunner R J 2016 Star-galaxy classification using deep convolutional neural networks Monthly Notices of the Royal Astronomical Society 464(4): pp 4463–4475

[5]. Zhu X P Dai J M Bian C J Chen Y Chen S Hu C 2019 Galaxy morphology classification with deep convolutional neural networks Astrophysics and Space Science 364: pp 1-15

[6]. Lin J Y Y Liao S M Huang H J Kuo W T Ou O H M 2021 Galaxy Morphological Classification with Efficient Vision Transformer arXiv:2110.01024

[7]. Mehta S Rastegari M 2021 Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer arXiv:2110.02178

[8]. Leung H W Bovy J 2019 Deep learning of multi-element abundances from high-resolution spectroscopic data Monthly Notices of the Royal Astronomical Society 483(3): pp 3255-3277

[9]. Willett K W Lintott C J Bamford S P et al 2013 Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey Monthly Notices of the Royal Astronomical Society 435(4): pp 2835-2860

[10]. Loshchilov I Hutter F 2017 Decoupled weight decay regularization arXiv:1711.05101

Cite this article

Tan,X. (2024). Accurate and efficient galaxy classification based on mobile vision transformer. Applied and Computational Engineering,33,118-125.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-291-6(Print) / 978-1-83558-292-3(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.33
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Naim A Lahav O Sodre Jr L Storrie-Lombardi M C 1995 Automated morphological classification of APM galaxies by supervised artificial neural networks Monthly Notices of the Royal Astronomical Society 275(3): pp 567-590

[2]. Owens E A Griffiths R E Ratnatunga K U 1996 Using oblique decision trees for the morphological classification of galaxies Monthly Notices of the Royal Astronomical Society 281(1): pp 153-157

[3]. Dieleman S Willett K W Dambre J 2015 Rotation-invariant convolutional neural networks for galaxy morphology prediction Monthly notices of the royal astronomical society 450(2): pp 1441-1459

[4]. Kim E J Brunner R J 2016 Star-galaxy classification using deep convolutional neural networks Monthly Notices of the Royal Astronomical Society 464(4): pp 4463–4475

[5]. Zhu X P Dai J M Bian C J Chen Y Chen S Hu C 2019 Galaxy morphology classification with deep convolutional neural networks Astrophysics and Space Science 364: pp 1-15

[6]. Lin J Y Y Liao S M Huang H J Kuo W T Ou O H M 2021 Galaxy Morphological Classification with Efficient Vision Transformer arXiv:2110.01024

[7]. Mehta S Rastegari M 2021 Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer arXiv:2110.02178

[8]. Leung H W Bovy J 2019 Deep learning of multi-element abundances from high-resolution spectroscopic data Monthly Notices of the Royal Astronomical Society 483(3): pp 3255-3277

[9]. Willett K W Lintott C J Bamford S P et al 2013 Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey Monthly Notices of the Royal Astronomical Society 435(4): pp 2835-2860

[10]. Loshchilov I Hutter F 2017 Decoupled weight decay regularization arXiv:1711.05101

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