MobileNetV3 for Image Classification
Siying Qian, Chenran Ning, Yuepeng Hu
Abstract
Convolution neural network (CNN) is a kind of deep neural networks, which extracts image features through multiple convolution layers and is widely used in image classifications. With the increasing number of image data processed by mobile devices, application of neural network for mobile terminals becomes popular. However, these networks need massive computation and advanced hardware support, making them difficult to adapt to mobile devices. This paper demonstrates that MobileNetV3 can get a superior balance between efficiency and accuracy for real-life image classification tasks on mobile terminals. In our experiments, classification performances are compared among MobileNetV3 and several other commonly used pre-trained CNN models on various image datasets. The chosen datasets are all good representatives of the application scenarios of mobile devices. The result shows that as a lightweight neural network, MobileNetV3 achieved good accuracy performance in an effective manner compared to other large networks. Furthermore, ROC confirmed the advantages of MobileNetV3 over other experimented models. Some conjectures are also brought out about the characteristics of image datasets that are suitable for MobileNetV3.