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.

翻译：我们研究高效网络的使用情况及其应用于银河道德分类。我们探索高效网络的使用情况,以预测79,975个测试银河动物2号对卡格勒的挑战中的79,975个图像的投票分数。我们使用标准竞争衡量标准(即:rmse分和公分为0.07765)来评估这一模型,在公共领导板上排名前3位,公共得分为0.07765。我们建议使用一个微调结构,使用高效网络B5来将星系分为七个等级――完全平稳地,在平滑、雪茄状平滑、扁豆状、封闭螺旋、无阻隔的螺旋、无阻螺旋和不规则之间。网络与其他流行的共生网络一起用于对29,941个星系图像进行分类。各种指标,如准确性、精确性、F1分,用来评价模型的性能,同时对艺术革命模型的其他状态进行比较研究,以确定谁最优秀的。我们获得了我们分类模型的准确度为93.7%的准确度,F1分为0.8857。高效的网络可以应用于未来光学空间望远镜的大规模空间观测中的大规模数据。