With the construction of large telescopes and the explosive growth of observed galaxy data, we are facing the problem to improve the data processing efficiency while ensuring the accuracy of galaxy morphology classification. Therefore, this work designed a lightweight deep learning framework, EfficientNet-G3, for galaxy morphology classification. The proposed framework is based on EfficientNet which integrates the Efficient Neural Architecture Search algorithm. Its performance is assessed with the data set from the Galaxy Zoo Challenge Project on Kaggle. Compared with several typical neural networks and deep learning frameworks in galaxy morphology classification, the proposed EfficientNet-G3 model improved the classification accuracy from 95.8% to 96.63% with F1-Score values of 97.1%. Typically, this model uses the least number of parameters, which is about one tenth that of DenseNet161 and one fifth that of ResNet-26, but its accuracy is about one percent higher than them. The proposed EfficientNet-G3 can act as an important reference for fast morphological classification for massive galaxy data in terms of efficiency and accuracy.
methods: data analysis – techniques: image processing – techniques: photometric
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