ResNet
ResNet stands for Residual Network, a deep learning computer vision architecture which can be used to solve image processing and computer vision problems. It was developed by researchers at Microsoft Research and has been adopted by many computer vision experts as one of the most efficient methods to address computer vision tasks. The main idea behind this neural network architecture is that it helps in reducing the vanishing gradient problem which arises while training deep networks. This makes it possible to train deeper networks without sacrificing accuracy or performance. In addition, ResNet also provides better accuracy when compared to other computer vision architectures because of its skip connections that create shortcuts between layers during the forward pass. As a result, ResNet is often preferred over other computer vision models due to its improved accuracy and efficiency. In summary, ResNet is a deep learning computer vision architecture used to solve computer vision problems and provides improved accuracy over other computer vision models due to its skip connections that create shortcuts between layers during the forward pass.
ResNet is based on convolutional neural networks (CNNs) and revolutionized the computer vision field by dramatically reducing the error rate for image recognition tasks such as object detection and classification. ResNet was first described in 2015, where it achieved an unprecedented performance of 3.57% top-5 error on ImageNet, a large visual database used to benchmark computer vision models. Since then, ResNet has become one of the most widely adopted computer vision architectures, with many successful applications in various computer vision tasks including image segmentation and video analysis. Its effectiveness lies in its ability to learn complex patterns from large data sources while maintaining low computational requirements. This makes ResNet an ideal choice for computer vision applications when accuracy and efficiency are paramount. In addition, ResNet is highly modular and can be easily adapted to different tasks and datasets, allowing computer vision engineers to directly transfer the learned weights from one task to another. Thus, ResNet has become a go-to computer vision architecture for many computer vision practitioners.
