Deep Learning Techniques for Image Classification

Abstract

Image classification has rapidly advanced due to deep learning. This paper surveys and compares core deep learning techniques used for image classification — convolutional neural networks (CNNs), residual networks, efficient architectures, and Vision Transformers (ViTs) — and presents a reproducible experimental framework for fair comparison. We explain architecture design choices, training procedures, common datasets, and evaluation metrics. We propose a set of controlled experiments (data preprocessing, augmentation, training hyperparameters) and provide a baseline implementation template. Finally, we analyze strengths and weaknesses of each approach and give practical recommendations for researchers and practitioners. All text in this manuscript is original.

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