ResNet, or Residual Network, is a type of deep learning architecture designed to improve the training of neural networks. It introduces the concept of "residual learning," which allows the model to learn the difference between the input and output, making it easier to optimize. This is achieved through the use of skip connections that bypass one or more layers, helping to prevent issues like vanishing gradients in very deep networks. Developed by Kaiming He and his team in 2015, ResNet has become a foundational model in the field of computer vision. It has been widely adopted for various tasks, including image classification and object detection, due to its ability to achieve high accuracy while maintaining efficiency in training.