# A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification

arXiv: https://arxiv.org/abs/2111.04353 · HTML: https://mlsanity.com/paper/2111.04353/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies](https://mlsanity.com/paper/2102.08414/) (2021)
- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)
- [Deep Learning for Real-Time Crime Forecasting and its Ternarization](https://mlsanity.com/paper/1711.08833/) (2017)

## Built on by (most cited first, 2 of 2)

- [FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging](https://mlsanity.com/paper/2405.13267/) (2024)
- [The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques](https://mlsanity.com/paper/2206.06165/) (2022)

## Abstract (arXiv)

The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this process. As such, this paper offers a comparison of deep learning architectures to determine which is best suited for optical galaxy morphology classification. Adapting the model training method proposed by Walmsley et al in 2021, the Zoobot Python library is used to train models to predict Galaxy Zoo DECaLS decision tree responses, made by volunteers, using EfficientNet B0, DenseNet121 and ResNet50 as core model architectures. The predicted results are then used to generate accuracy metrics per decision tree question to determine architecture performance. DenseNet121 was found to produce the best results, in terms of accuracy, with a reasonable training time. In future, further testing with more deep learning architectures could prove beneficial.