A Comparative Analysis of State-of-The-Art Deep Learning Architectures for Image Recognition Tasks

Abstract

A thorough assessment of many cutting-edge deep learning architectures for image recognition tasks is presented in this research study. The accuracy and efficiency of photo identification have significantly increased because deep learning, which has completely changed computer vision. However, choosing the best architecture for specific image identification tasks has become difficult for researchers and practitioners due to the quick development of many deep learning models. We analyze and compare the performance of popular models such as Transformer-based models, more recent designs such as EfficientNet, and Convolutional Neural Networks (CNNs) such as ResNet, VGG, and DenseNet. Important features like accuracy, computational efficiency, parameter efficiency, and robustness to changes in input data are the main focus of our investigation. We emphasize each architecture's suitability for a variety of photo recognition tasks and offer insights into its advantages and disadvantages based on thorough testing on benchmark datasets like ImageNet and CIFAR. The results of this study can help practitioners and researchers choose the best deep learning architecture according to particular needs and limitations, improving the state-of-the-art in image recognition technology.