The investigation of transferability related to the ImageNet-based transfer learning model in galaxy classification

Abstract

The phenomenon of transfer learning, specifically the transferability of ImageNet, in the context of galaxy datasets has been relatively under-researched. This study seeks to address this gap by employing the Galaxy10 DECals dataset, which is a 10-classification dataset. Three classic models, namely MobileNet, VGG, and ResNet, were developed and customized to the current dataset by modifying the number of neurons in the last layer. The experimental phase is divided into three parts, including the comparison between the use of ImageNet and non-ImageNet, model performance comparison, and confusion matrix analysis. The results demonstrate that the utilization of ImageNet produces better outcomes, with the MobileNet model exhibiting the highest performance. Further analysis revealed that the inclusion of ImageNet weights can enhance the classification accuracy of some data. Although the present study was successful in achieving its objectives, future research should focus on exploring and elucidating the underlying mechanisms driving these findings.