ResNet is a type of deep neural network architecture that addresses the problem of vanishing gradients in very deep neural networks. It does this by introducing skip connections, which allow the network to learn residual functions. ResNet has been extremely successful in various computer vision tasks, such as image classification, object detection, and semantic segmentation. For example, in the ImageNet Large Scale Visual Recognition Challenge, ResNet architectures have achieved state-of-the-art results. Its design has enabled the training of much deeper networks, leading to significant improvements in the accuracy of visual recognition systems.
