An improved residual learning model and its application to hardware image classification

Some hardware is similar in color and shape between different classes, and some hardware varies within a class, thereby decreasing the accuracy of classification models. The baseline of image classification depends on network architectures. To solve this problem, this paper proposes an improved residual network architecture that achieves high accuracy in hardware classification. This paper proposes a hardware dataset that fills the blank of hardware images. The improved residual network architectures are based on depthwise over-parameterized convolution and pyramidal convolution. This model could obtain information from different levels and channels. Moreover, compared with the novel architectures, our model does not require increasing the computation. Experiments on the hardware dataset show that our neural network architectures achieved better accuracy compared with notable architectures such as ResNet50 and DenseNet121. The classification accuracy of the proposed network is 94.6% and the f1 score is 90.26%. To verify the proposed network architecture, we applied our model in fine-grained datasets; our model achieved the best performance in the same training setting.