DEEP LEARNING IN IMAGE RECOGNITION: A COMPARATIVE REVIEW OF ARCHITECTURES AND MODELS
Keywords:
Deep Learning, Image Recognition, Convolutional Neural Networks (CNNs), Residual Networks (ResNets), Vision Transformers (ViTs), Transfer Learning, Performance Metrics, Data Augmentation, Regularization Techniques, Benchmark DatasetsAbstract
Deep learning has revolutionized image recognition, providing state-of-the-art performance across various applications, from medical diagnostics to autonomous vehicles. This comparative review explores the evolution of deep learning architectures and models used in image recognition. We categorize and analyze prominent architectures, including Convolutional Neural Networks (CNNs), Residual Networks (ResNets), Inception Networks, and more recent developments like Vision Transformers (ViTs). The review highlights key features, strengths, and limitations of each architecture while discussing their performance metrics in standard benchmark datasets such as ImageNet, CIFAR-10, and MNIST. Additionally, we examine the impact of transfer learning, data augmentation, and regularization techniques on model performance. By synthesizing current research, this review aims to provide insights into selecting appropriate architectures for specific image recognition tasks and identifies future research directions to enhance the capabilities of deep learning models in this domain.
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