Advancements in Deep Learning Techniques for Image Recognition: A Comprehensive Review

Abstract

Computer vision has been completely transformed by deep learning, especially when it comes to image identification applications. An extensive analysis of many deep learning architectures created for image recognition tasks is presented in this research study. This research examines the development of deep learning models, tracing their strengths, shortcomings, and performance on benchmark datasets from the earliest convolutional neural networks (CNNs) to the most recent state-of-the-art designs. The study also examines the crucial elements and design decisions that have aided in these architectures' success with picture recognition. It also covers the difficulties and potential avenues for further study in this dynamic and quickly developing topic.