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【24h】Sex Classification of Salmon Using Convolutional Neural Network

机译卷积神经网络对鲑鱼的性别分类

【摘要】 In this study, we attempted to classify the sex of salmon using a convolutional neural network. We collected labeled(male/female) images of salmon and other fishes, and trained the VGG16 type neural network by implementing several data argumentation techniques, cropping the bounding box of salmon. In evaluation, the F value of the classification result was found to be satisfactory by more than 99%. We demonstrated the focus point of the neural network using Grad-CAM and the classification ability depending on the background processing.

【摘要机译】在这项研究中,我们尝试使用卷积神经网络对鲑鱼的性别进行分类。我们收集了鲑鱼和其他鱼类的带标签(男性/女性)图像,并通过实施几种数据论证技术来训练VGG16型神经网络,裁剪了鲑鱼的边界框。在评估中,发现分类结果的F值令人满意,超过99%。我们证明了使用Grad-CAM的神经网络的焦点和取决于背景处理的分类能力。

【作者】Takumi Kuramoto; Shuji Abe; Hiroaki Ishihata;

【作者单位】Tokyo University of Technology Hachioji School of Computer Science Tokyo Japan; Tokyo University of Technology Hachioji School of Bioscience and Biotechnology Tokyo Japan;

【年(卷),期】2020(),

【年度】2020

【页码】1-4

【总页数】4

【正文语种】

【中图分类】;

【关键词】Convolutional neural networks; Animals; Training data; Conferences; Machine learning; Biomedical imaging;

机译 卷积神经网络动物;培训数据;会议;机器学习;生物医学影像;
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