ResNet (Residual Network) is a type of Convolutional Neural Network (CNN) that uses residual connections to improve the training of deep networks. These residual connections help mitigate the vanishing gradient problem, allowing the network to learn more complex features. ViT (Vision Transformers) is an approach that applies the transformer architecture to image classification tasks. Unlike ResNet, ViT divides images into smaller patches and processes them in parallel using self-attention mechanisms, which can lead to improved performance and efficiency compared to traditional CNNs.