This paper presents a new approach to scaling up convolutional networks for computer vision tasks, focusing on improving efficiency and performance. The authors propose several design principles, including avoiding representational bottlenecks, using higher dimensional representations, spatial aggregation, and balancing network width and depth. They also introduce factorized convolutions, which allow for more efficient computation by breaking down large convolutions into smaller ones. This approach significantly reduces computational cost and parameter count while maintaining high performance. The authors also explore the use of auxiliary classifiers, which help improve convergence and regularization. Additionally, they discuss efficient grid size reduction techniques that maintain performance while reducing computational cost. The proposed architecture, Inception-v2, achieves state-of-the-art results on the ILSVRC 2012 classification benchmark, with a top-1 error of 21.2% and top-5 error of 5.6% for single crop evaluation. When using an ensemble of four models and multi-crop evaluation, the top-5 error is reduced to 3.5% and the top-1 error to 17.3%. The authors also demonstrate that high-quality results can be achieved with relatively low-resolution inputs, such as 79x79, which may be useful for detecting small objects. The paper concludes that their approach provides a more efficient and effective way to scale up convolutional networks for computer vision tasks.This paper presents a new approach to scaling up convolutional networks for computer vision tasks, focusing on improving efficiency and performance. The authors propose several design principles, including avoiding representational bottlenecks, using higher dimensional representations, spatial aggregation, and balancing network width and depth. They also introduce factorized convolutions, which allow for more efficient computation by breaking down large convolutions into smaller ones. This approach significantly reduces computational cost and parameter count while maintaining high performance. The authors also explore the use of auxiliary classifiers, which help improve convergence and regularization. Additionally, they discuss efficient grid size reduction techniques that maintain performance while reducing computational cost. The proposed architecture, Inception-v2, achieves state-of-the-art results on the ILSVRC 2012 classification benchmark, with a top-1 error of 21.2% and top-5 error of 5.6% for single crop evaluation. When using an ensemble of four models and multi-crop evaluation, the top-5 error is reduced to 3.5% and the top-1 error to 17.3%. The authors also demonstrate that high-quality results can be achieved with relatively low-resolution inputs, such as 79x79, which may be useful for detecting small objects. The paper concludes that their approach provides a more efficient and effective way to scale up convolutional networks for computer vision tasks.