This paper introduces a deep residual learning framework to address the problem of training very deep neural networks. The key idea is to reformulate the learning problem so that each layer learns a residual function rather than directly learning the desired mapping. This approach allows deeper networks to be trained more effectively, as the residual function is easier to optimize. The residual learning framework is implemented using shortcut connections, which allow the network to learn residual mappings while maintaining the same input and output dimensions. The authors evaluate their residual networks on the ImageNet dataset, achieving a top-5 error of 3.57% on the test set, which won first place in the ILSVRC 2015 classification task. They also show that their residual networks can achieve significant accuracy improvements with increased depth, even when the depth is much larger than previous models. For example, a 152-layer residual network is presented, which is deeper than VGG nets but has lower complexity. The residual learning framework is also shown to be effective for other tasks such as object detection and segmentation. The authors demonstrate that their residual networks outperform previous models on the COCO dataset, achieving a 28% relative improvement on the COCO object detection dataset. The residual learning principle is shown to be general and applicable to various vision and non-vision problems. The paper also discusses the degradation problem in deep networks, where increasing depth leads to a decrease in accuracy. The authors show that this problem can be mitigated by using residual learning, which allows the network to learn residual functions that are easier to optimize. The residual learning framework is implemented using shortcut connections, which enable the network to learn residual mappings while maintaining the same input and output dimensions. The authors also present experiments on the CIFAR-10 dataset, showing that their residual networks can achieve significant accuracy improvements with increased depth. They also explore models with over 1000 layers, demonstrating that the residual learning framework can be applied to extremely deep networks without significant optimization difficulties. Overall, the paper presents a novel approach to training deep neural networks that allows for significantly deeper models to be trained more effectively. The residual learning framework is shown to be effective for a wide range of tasks, including image classification, object detection, and segmentation. The results demonstrate that the residual learning principle is a powerful tool for improving the performance of deep neural networks.This paper introduces a deep residual learning framework to address the problem of training very deep neural networks. The key idea is to reformulate the learning problem so that each layer learns a residual function rather than directly learning the desired mapping. This approach allows deeper networks to be trained more effectively, as the residual function is easier to optimize. The residual learning framework is implemented using shortcut connections, which allow the network to learn residual mappings while maintaining the same input and output dimensions. The authors evaluate their residual networks on the ImageNet dataset, achieving a top-5 error of 3.57% on the test set, which won first place in the ILSVRC 2015 classification task. They also show that their residual networks can achieve significant accuracy improvements with increased depth, even when the depth is much larger than previous models. For example, a 152-layer residual network is presented, which is deeper than VGG nets but has lower complexity. The residual learning framework is also shown to be effective for other tasks such as object detection and segmentation. The authors demonstrate that their residual networks outperform previous models on the COCO dataset, achieving a 28% relative improvement on the COCO object detection dataset. The residual learning principle is shown to be general and applicable to various vision and non-vision problems. The paper also discusses the degradation problem in deep networks, where increasing depth leads to a decrease in accuracy. The authors show that this problem can be mitigated by using residual learning, which allows the network to learn residual functions that are easier to optimize. The residual learning framework is implemented using shortcut connections, which enable the network to learn residual mappings while maintaining the same input and output dimensions. The authors also present experiments on the CIFAR-10 dataset, showing that their residual networks can achieve significant accuracy improvements with increased depth. They also explore models with over 1000 layers, demonstrating that the residual learning framework can be applied to extremely deep networks without significant optimization difficulties. Overall, the paper presents a novel approach to training deep neural networks that allows for significantly deeper models to be trained more effectively. The residual learning framework is shown to be effective for a wide range of tasks, including image classification, object detection, and segmentation. The results demonstrate that the residual learning principle is a powerful tool for improving the performance of deep neural networks.