What Is ResNet?

ResNet (residual network) is a family of convolutional neural networks introduced by Microsoft Research. It uses skip connections to combine learned residuals with features from earlier layers. This made networks with 100+ layers easier to train. ResNet remains a practical baseline for image classification and a backbone for object detection and segmentation. You can train and deploy ResNet models on your own data in Roboflow. Imagine a student following a more detailed study plan but getting lower scores, even on practice tests. Deep neural networks once faced a similar problem. Adding more layers should have helped them learn richer features. Yet plain networks showed higher training error (opens in a new tab) as their depth increased. A plain network simply stacks layers with no skip connections. ResNet addressed this problem with a simple idea called residual learning. A skip connection carries the input around a block's layers. The block adds the input back to the learned residual. This made much deeper networks easier to train. The researchers trained a 152-layer network on ImageNet and explored a 1,202-layer network on CIFAR-10. The idea also appears in transformers (opens in a new tab), which add a residual connection around their multi-head self-attention…