Classification of marine garbage image based on ResNet50 network

Miao Dai, Youfu Jiang, Bei Pan
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Abstract

In order to improve the efficiency of marine garbage classification, this study first enhances and processes the data set, then uses ResNet50 network model and modifies its lowest layer of network, and finally obtains the accuracy in different cycles through training and verification. The results show that the accuracy of the network model trained in this study is as high as 96% under the most stable cycle.
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基于ResNet50网络的海洋垃圾图像分类
为了提高海洋垃圾分类的效率,本研究首先对数据集进行增强和处理,然后使用ResNet50网络模型并对其最低层网络进行修改,最后通过训练和验证得到不同周期的准确率。结果表明,在最稳定周期下,本文训练的网络模型准确率高达96%。
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