ResNet-50 (Image Class.)

ResNet-50 is a landmark image classification model from Microsoft Research, introduced in 2015 as part of the Residual Learning framework. It uses 50 layers with skip connections (residual connections) that enable training of much deeper networks by allowing gradients to flow directly through shortcut paths.

The model classifies images into 1,000 ImageNet categories with high accuracy, serving as a foundational architecture in computer vision. ResNet-50's residual connection design solved the degradation problem that limited deep network training, enabling the construction of much deeper and more accurate models.

ResNet-50 remains widely used as a backbone for transfer learning, feature extraction, and as a component in more complex vision systems, making it one of the most cited and deployed architectures in deep learning.