ResNet is a deep convolutional neural network used for image classification. It uses a residual neural network (ResNet) architecture to learn powerful representation of the input data and can classify images into distinct classes. It has been widely used for image classification tasks in many applications such as computer vision, object detection, and image segmentation. By using a network architecture called residual learning, ResNet has been able to reduce the number of parameters with comparable accuracy in comparison to other deep learning models. ResNet is able to achieve better accuracy and better generalization performance by taking advantages of advantages of the skip or shortcut connections. ResNet has achieved state-of-the-art performance in image classification tasks such as ImageNet, CIFAR-10, and COCO.