GoogLeNet, a profound deep neural network unveiled by Google researchers in 2014, has garnered attention for its innovative Inception module structure. This architectural feature allows the network to adeptly learn features of varying sizes concurrently. The modular design of GoogLeNet enhances training and inference efficiency, leading to remarkable achievements in image classification competitions during its debut. Noteworthy is GoogLeNet's ability to elevate model depth and width while minimizing parameters, thereby amplifying accuracy. Additionally, the integration of regularization techniques like Dropout and L2 regularization serves to mitigate overfitting risks. Collectively, these attributes position GoogLeNet as a pioneering model in the realm of image classification.