This paper presents new deep convolutional network architectures, Inception-ResNet-v1, Inception-ResNet-v2, and Inception-v4, which build upon the Inception architecture and incorporate residual connections. The Inception architecture has been successful in image recognition due to its efficiency and performance. The introduction of residual connections, which allow for easier training of deep networks, has led to state-of-the-art results in image recognition. The authors show that adding residual connections to Inception networks significantly accelerates training and slightly improves performance. They also present new streamlined architectures for both residual and non-residual Inception networks, which improve performance on the ILSVRC 2012 classification task. The authors also demonstrate that proper activation scaling stabilizes the training of very wide residual Inception networks. Using an ensemble of three residual and one Inception-v4 networks, they achieve a top-5 error of 3.08% on the ImageNet classification challenge. The paper also discusses the impact of residual connections on training speed and performance, and concludes that while residual connections improve training speed, they do not necessarily lead to better performance. The authors also evaluate the performance of their models on various benchmarks and find that their models outperform previous networks. The paper concludes that the Inception architecture, with or without residual connections, is a powerful tool for image recognition.This paper presents new deep convolutional network architectures, Inception-ResNet-v1, Inception-ResNet-v2, and Inception-v4, which build upon the Inception architecture and incorporate residual connections. The Inception architecture has been successful in image recognition due to its efficiency and performance. The introduction of residual connections, which allow for easier training of deep networks, has led to state-of-the-art results in image recognition. The authors show that adding residual connections to Inception networks significantly accelerates training and slightly improves performance. They also present new streamlined architectures for both residual and non-residual Inception networks, which improve performance on the ILSVRC 2012 classification task. The authors also demonstrate that proper activation scaling stabilizes the training of very wide residual Inception networks. Using an ensemble of three residual and one Inception-v4 networks, they achieve a top-5 error of 3.08% on the ImageNet classification challenge. The paper also discusses the impact of residual connections on training speed and performance, and concludes that while residual connections improve training speed, they do not necessarily lead to better performance. The authors also evaluate the performance of their models on various benchmarks and find that their models outperform previous networks. The paper concludes that the Inception architecture, with or without residual connections, is a powerful tool for image recognition.