There are several popular deep learning architectures for image classification. Some of them include:

  1. Convolutional Neural Networks (CNNs or ConvNets): CNNs are the most widely used architecture for image classification tasks. They are designed to automatically learn and extract meaningful features from images through layers of convolutional and pooling operations. Some famous CNN architectures include AlexNet, VGGNet, GoogLeNet (Inception), and ResNet.

  2. Recurrent Neural Networks (RNNs): RNNs are often used for sequential data, but they can also be applied to image classification by treating the image as a sequence of patches or by using attention mechanisms. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are commonly used variants of RNNs.

  3. Transfer Learning: Transfer learning is a technique where a pre-trained model is used as a starting point for a new image classification task. By leveraging the knowledge learnt from large-scale datasets such as ImageNet, transfer learning can significantly improve performance and reduce the need for training from scratch.

  4. Generative Adversarial Networks (GANs): While initially developed for generating new images, GANs can also be used for image classification. The discriminator network in a GAN can learn to classify images, while the generator network can be used to generate new examples for data augmentation.

  5. Residual Networks (ResNet): ResNet is a specific type of CNN architecture that introduced the concept of skip connections. Skip connections allow the network to effectively propagate gradients during training and mitigate the vanishing gradient problem. This architecture has achieved state-of-the-art performance on various image classification benchmarks.

  It is worth noting that these architectures are not mutually exclusive, and researchers often combine elements from different architectures or propose novel variations to improve performance or address specific challenges in image classification tasks. The choice of architecture depends on the specific requirements of the task, computational resources available, and other factors.