Briefly introduce the Google Net network model

The Google Net network model, also known as Inception-v1, is a deep convolutional neural network architecture designed by researchers at Google for image classification. It uses a combination of convolutional layers, pooling layers, and fully connected layers to extract features from input images and make predictions about their class labels. The network model is characterized by its use of "Inception modules," which are designed to increase the network's representational power while minimizing the number of parameters required. The Google Net model achieved state-of-the-art performance on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, and has since been used as a benchmark for other deep learning models