What Is ResNet?

ResNet is a family of convolutional neural networks introduced by Microsoft Research that solved the degradation problem in deep plain networks through residual learning, in which skip connections add a block’s input to its learned transformation and help gradients flow during training. This approach enabled effective training of networks with more than 100 layers, contributed to major ImageNet and COCO competition results, and has made ResNet a durable baseline for image classification, feature extraction, object detection, and segmentation. Standard variants range from ResNet-18 and ResNet-34, which use simpler basic blocks for speed and limited hardware, to ResNet-50, ResNet-101, and ResNet-152, which use computationally efficient bottleneck blocks for greater accuracy at higher compute cost. Although vision transformers and newer CNNs such as ConvNeXt can outperform ResNet in some large-scale settings, ResNet remains widely used as a backbone in systems including Faster R-CNN, DeepLabv3, and DETR, particularly when pretrained weights and transfer learning are useful. Roboflow supports running pretrained ImageNet ResNet models, training custom classifiers with several ResNet sizes, uploading externally trained weights, and deploying models through cloud APIs, self-hosted inference, edge devices, or visual workflows for applications such as product-defect detection and image-level quality classification.

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