The Mount Wilson magnetic classification of sunspot groups is thought to be meaningful to forecast flares’ eruptions. In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. It includes three stages, generating object locations, detecting objects, and merging detections. The key technologies consist of the backbone as Hourglass-54, the attention mechanism, and the key points’ mechanism including the top-left corners and the bottom-right corners of the object by corner pooling layers. These technologies improve the efficiency of detecting the objects without sacrificing accuracy. A dataset is built by a total of 2486 composited images which are composited with the continuum images and the corresponding magnetograms from HMI and MDI. After training the network, the sunspot groups in a composited solar full image are detected and classified in 3 seconds on average. The test results show that this method has a good performance, with the accuracy, precision, recall, and mAP as 0.94, 0.93, 0.94, and 0.90, respectively. Moreover, the flare productivities of different types of sunspot groups from 2011 to 2020 are calculated. As   1, the flare productivities of , and sunspot groups are 0.14, 0.28, 0.61, 0.71, and 0.87, respectively. As   10, the flare productivities are 0.02, 0.07, 0.27, 0.45, and 0.65, respectively. It means that the , and types are indeed very closely related to the eruption of solar flares, especially the type. Based on the reliability of this method, the sunspot groups of the HMI solar full images from 2011 to 2020 are detected and classified, and the detailed data are shared on the website (https://61.166.157.71/MWMCSG.html).

中文翻译：

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基于深度学习的威尔逊山磁分类研究

太阳黑子群的威尔逊山磁分类被认为对预测耀斑的爆发很有意义。在本文中，我们采用深度学习方法 CornerNet-Saccade 对太阳黑子群进行 Mount Wilson 磁分类。它包括三个阶段，生成对象位置、检测对象和合并检测。关键技术包括Hourglass-54作为主干，注意力机制，以及通过角池化层获得对象左上角和右下角的关键点机制。这些技术在不牺牲精度的情况下提高了检测对象的效率。一个数据集由总共 2486 张合成图像组成，这些图像与来自 HMI 和 MDI 的连续图像和相应的磁图合成。训练完网络后，合成太阳全幅图像中的太阳黑子群平均检测和分类时间为 3 秒。测试结果表明，该方法具有良好的性能，准确率、准确率、召回率和mAP分别为0.94、0.93、0.94和0.90。此外，计算了2011-2020年不同类型太阳黑子群的耀斑生产力。作为  1、火炬产能 ，和黑子群分别为0.14，0.28，0.61，0.71，和0.87。为 10，火炬生产率分别为 0.02、0.07、0.27、0.45 和 0.65。这意味着 ，和类型确实非常密切相关的太阳耀斑爆发，尤其是类型。基于该方法的可靠性，对2011-2020年HMI太阳全像的太阳黑子群进行了检测和分类，详细数据在网站（https://61.166.157.71/MWMCSG.html）上共享。