Galaxy morphology classification with deep convolutional neural networks

  • Xiao-Pan Zhu
  • Jia-Ming DaiEmail author
  • Chun-Jiang Bian
  • Yu Chen
  • Shi Chen
  • Chen Hu
Original Article


We propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), is applied to a sample of 28790 galaxy images from the Galaxy Zoo 2 dataset, to classify galaxies into five classes, i.e., completely round smooth, in-between smooth (between completely round and cigar-shaped), cigar-shaped smooth, edge-on and spiral. Various metrics, such as accuracy, precision, recall, F1 value and AUC, show that the proposed network achieves state-of-the-art classification performance among other networks, namely, Dieleman, AlexNet, VGG, Inception and ResNets. The overall classification accuracy of our network on the testing set is 95.2083% and the accuracy of each type is given as follows: completely round, 96.6785%; in-between, 94.4238%; cigar-shaped, 58.6207%; edge-on, 94.3590% and spiral, 97.6953%. Our model algorithm can be applied to large-scale galaxy classification in forthcoming surveys, such as the Large Synoptic Survey Telescope (LSST) survey.


Galaxy morphology classification Deep learning Convolutional neural networks 



We would like to thank galaxy challenge, Galaxy Zoo, SDSS and Kaggle platform for sharing data. We acknowledge the financial support from the National Earth System Science Data Sharing Infrastructure ( We are supported by CAS e-Science Funds (Grant XXH135 03-04).


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.National Space Science CenterChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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