ResNet-50 is a specific architecture within the family of residual networks designed for image classification and visual recognition tasks. The "50" refers to its depth consisting of 50 layers. It utilizes skip connections, or shortcuts, to jump over some layers. This helps prevent problems like the vanishing gradient issue that can occur in deep neural networks. ResNet-50 is often compared to VGG and AlexNet, both being other architecture models for image tasks.