# Deep Residual Network

ResNet-50 is a deep residual network developed by Microsoft Research, widely used for image classification tasks.

Computer vision model based on ResNet-50 architecture for image classification tasks

ResNet26 is an image classification model based on the deep residual learning architecture, a variant in the ResNet series.

A deep residual network model pre-trained on the ImageNet dataset for image classification tasks.

ResNet152 is an image classification model based on deep residual learning, which solves the gradient vanishing problem in deep network training through residual connections.

ResNet26d is an image classification model based on deep residual learning, a variant (d) version of ResNet, optimized for stem structure and shortcut connections.

ResNet18 is an image classification model based on deep residual learning, which solves the difficulty of training deep networks through residual connections.

ResNet50 is an image classification model based on deep residual learning, which solves the vanishing gradient problem in deep neural networks through residual connections.