The paper introduces a residual learning framework to address the challenges of training deeper neural networks. By reformulating layers as learning residual functions relative to the layer inputs, the authors show that these residual networks are easier to optimize and can achieve higher accuracy with increased depth. On the ImageNet dataset, they evaluate residual nets up to 152 layers, which is 8 times deeper than VGG nets but still has lower complexity. An ensemble of these residual nets achieves a 3.57% error rate on the ImageNet test set, winning the 1st place in the ILSVRC 2015 classification task. The authors also present results on the CIFAR-10 dataset, demonstrating similar phenomena. The deep residual learning framework is further applied to object detection and localization tasks, achieving significant improvements over previous methods.The paper introduces a residual learning framework to address the challenges of training deeper neural networks. By reformulating layers as learning residual functions relative to the layer inputs, the authors show that these residual networks are easier to optimize and can achieve higher accuracy with increased depth. On the ImageNet dataset, they evaluate residual nets up to 152 layers, which is 8 times deeper than VGG nets but still has lower complexity. An ensemble of these residual nets achieves a 3.57% error rate on the ImageNet test set, winning the 1st place in the ILSVRC 2015 classification task. The authors also present results on the CIFAR-10 dataset, demonstrating similar phenomena. The deep residual learning framework is further applied to object detection and localization tasks, achieving significant improvements over previous methods.