HMobile:一种基于MobileNet用于垃圾识别分类的改进网络
HMobile : An Improved Network For Garbage Classification Based on MobileNet
随着深度学习的快速发展，越来越多的图像识别模型被应用到日常生活中。对于现有的神经网络模型，大模型的识别精度越来越高，但需要更多的资源。神经网络模型的轻量级更有利于在生活中的应用。本文基于MobileNet提出了一种尺寸更小、精度更高的THMobile模型。在自制的垃圾数据集上，分类准确率达到91.2%，取得了比MobileNet更好的性能。而且它在CIFAR-10上的性能也比MobileNet好。
With the rapid development of deep learning, more and more image recognition models are applied to daily life. For the current neural network model, the recognition accuracy of large model is higher and higher, but the more resources are needed. The lightweight of neural network model is more conducive to the application in life. In this paper, a THMobile model with smaller size and higher accuracy is proposed based on MobileNet. On the self-made garbage dataset, the classification accuracy of it reaches 91.2%, obtaining better performance than MobileNet. And it also performs better on CIFAR-10 than MobileNet.
周嘉澜
电子技术应用
图像识别、 MobileNet、垃圾分类、 THMobile
image recognition MobileNet garbage classification THMobile
周嘉澜.HMobile:一种基于MobileNet用于垃圾识别分类的改进网络[EB/OL].(2022-03-01)[2025-08-21].http://www.paper.edu.cn/releasepaper/content/202203-8.点此复制
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