论文标题
使用高效网络体系结构的星系形态分类
Galaxy Morphology Classification using EfficientNet Architectures
论文作者
论文摘要
我们研究了有效网络的用法及其在星系形态分类中的应用。我们探讨了有效网络在预测Kaggle的Galaxy Zoo 2挑战中的79,975次测试图像的投票部分中的用法。我们使用标准竞赛度量（即RMSE得分）评估了这一模型，并以0.07765的公共得分在公共排行榜上排名前三。我们提出了一个使用EdicticnEtB5进行微调的体系结构，将星系分类为七个类 - 完全光滑，光滑，光滑，光滑，凸状的，禁止的螺旋，螺旋状和不规则的中间。该网络与其他流行的卷积网络一起用于对29,941个星系图像进行分类。不同的指标，例如准确性，回忆，精度，F1分数用于评估模型的性能以及对其他最先进的卷积模型的比较研究，以确定哪个表现最佳。我们的分类模型的精度为93.7％，F1分数为0.8857。在未来的光学空间调查中，有效网络可以应用于大规模的星系分类，该分类将提供大量数据，例如大型的概要空间望远镜。
We study the usage of EfficientNets and their applications to Galaxy Morphology Classification. We explore the usage of EfficientNets into predicting the vote fractions of the 79,975 testing images from the Galaxy Zoo 2 challenge on Kaggle. We evaluate this model using the standard competition metric i.e. rmse score and rank among the top 3 on the public leaderboard with a public score of 0.07765. We propose a fine-tuned architecture using EfficientNetB5 to classify galaxies into seven classes - completely round smooth, in-between smooth, cigarshaped smooth, lenticular, barred spiral, unbarred spiral and irregular. The network along with other popular convolutional networks are used to classify 29,941 galaxy images. Different metrics such as accuracy, recall, precision, F1 score are used to evaluate the performance of the model along with a comparative study of other state of the art convolutional models to determine which one performs the best. We obtain an accuracy of 93.7% on our classification model with an F1 score of 0.8857. EfficientNets can be applied to large scale galaxy classification in future optical space surveys which will provide a large amount of data such as the Large Synoptic Space Telescope.
