A Comprehensive Review on Different CNN Architectures

Convolutional Neural Networks (CNNs) have rev- olutionized the field of computer vision, transforming tasks such as image classification, segmentation, and object detection with remarkable efficacy. This review provides a comprehensive overview of the evolution, architectures, and applications of CNNs. This paper begins with a historical perspective, tracing the development of CNNs from their inception to the present day. The paper discusses the fundamental components of CNN archi- tectures, including convolutional layers, pooling layers, activation functions, and normalization techniques. In addition, it explores the advances in network design, such as the introduction of deep and residual networks, and innovative strategies such as attention mechanisms. This paper also discusses the impact of transfer learning and pre-trained models on practical applications. The purpose of this paper is to offer a nuanced understanding of their capabilities and potential future directions by highlighting the strengths and limitations of current CNN architectures. In addition, this paper also addresses emerging trends and challenges in the field, offering insights into how CNNs can continue to evolve and adapt to new domains and applications.