What Is ResNet-50?

ResNet-50 is a convolutional neural network (CNN) architecture that is part of the Residual Networks (ResNet) family, created to tackle challenges in training deep neural networks, particularly the degradation problem where deeper networks suffer from higher training errors. Developed by Microsoft Research Asia, ResNet-50 utilizes a series of bottleneck residual blocks that feature skip connections to address the vanishing gradient issue, enabling the construction of deeper and more accurate networks for image classification tasks. This architecture is composed of 50 bottleneck residual blocks stacked to form a sophisticated network that integrates conventional convolutional and pooling layers at the initial stages and fully connected layers at the end for precise image categorization. ResNet-50, released in 2015, has been influential in advancing computer vision applications by overcoming training challenges and allowing for the development of efficient models with high accuracy.