Design and Implementation of a Lightweight GoogLeNet Image Classification Model Integrating Depthwise Separable Convolutions

With the rapid growth of artificial intelligence applications on embedded devices and mobile platforms, traditional convolutional neural network models are often difficult to meet the real-time and power consumption requirements of embedded devices due to their large number of parameters. This paper presents a lightweight improvement to the GoogLeNet model by replacing the standard convolution in the Inception module with Depthwise Separable Convolutions and simplifying the network structure. This approach effectively reduces the model’s parameter count and computational complexity. Experimental results show that the improved model reduces the parameter count by approximately $54 \%$ and the computational complexity by about $42 \%$. The training speed is significantly improved, disturbance resistance is enhanced, and the model is suitable for deployment on low-computational-power devices.