润色：Classic CNN architecture networks include ResNet and DenseNet which have excellent performance in computer vision image processing tasks

Classic CNN architecture networks, such as ResNet and DenseNet, have achieved remarkable performance in various computer vision image processing tasks. These networks have revolutionized the field of computer vision and have become the go-to choices for many researchers and practitioners.

ResNet, short for Residual Network, introduced the concept of residual learning, which significantly improved network training and performance. It tackled the problem of vanishing gradients by introducing skip connections that enabled the network to learn residual mappings. This architecture allowed for the training of much deeper networks, resulting in improved accuracy and performance in tasks like image classification, object detection, and semantic segmentation.

DenseNet, on the other hand, introduced the idea of densely connected layers. Unlike traditional CNN architectures, where feature maps are passed only to subsequent layers, DenseNet establishes direct connections between all layers. This dense connectivity enhances feature propagation and encourages feature reuse, leading to improved gradient flow, reduced vanishing gradient problems, and better information flow throughout the network. DenseNet has demonstrated superior performance in tasks like image classification, semantic segmentation, and image generation.

Both ResNet and DenseNet have proven to be highly effective in various computer vision tasks and have set new benchmarks in performance. They have become the backbone architectures for many state-of-the-art models and have paved the way for significant advancements in the field of computer vision