The paper introduces a deep convolutional neural network architecture named Inception, which significantly improved the state-of-the-art for image classification and detection in the 2014 ILSVRC competition. The architecture is designed to efficiently utilize computational resources while increasing network depth and width. Key features include the use of "Inception modules" that combine convolutional and pooling operations, and the application of dimension reduction techniques to manage computational complexity. The GoogLeNet, a 22-layer deep version of Inception, achieved top-5 error rates of 6.67% in classification and competitive results in object detection, demonstrating the effectiveness of the architecture in both tasks. The paper also discusses the training methodology, ensemble techniques, and the impact of various hyperparameters on performance. Overall, the Inception architecture provides a robust and efficient solution for computer vision tasks, highlighting the importance of both depth and width in neural networks.The paper introduces a deep convolutional neural network architecture named Inception, which significantly improved the state-of-the-art for image classification and detection in the 2014 ILSVRC competition. The architecture is designed to efficiently utilize computational resources while increasing network depth and width. Key features include the use of "Inception modules" that combine convolutional and pooling operations, and the application of dimension reduction techniques to manage computational complexity. The GoogLeNet, a 22-layer deep version of Inception, achieved top-5 error rates of 6.67% in classification and competitive results in object detection, demonstrating the effectiveness of the architecture in both tasks. The paper also discusses the training methodology, ensemble techniques, and the impact of various hyperparameters on performance. Overall, the Inception architecture provides a robust and efficient solution for computer vision tasks, highlighting the importance of both depth and width in neural networks.