Author(s) :

Avinash Kumar.

Volume/Issue :

Abstract :

Image classification and recognition are integral components of computer vision, with applications spanning healthcare diagnostics, autonomous vehicles, facial recognition, and retail analytics. The confluence of data science and deep learning has dramatically enhanced the accuracy and scalability of these tasks, marking a paradigm shift from traditional machine learning approaches that relied heavily on handcrafted features and shallow models. Data science plays a pivotal role in managing the data pipeline—spanning data acquisition, preprocessing, augmentation, and analysis—while deep learning leverages advanced architectures like Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models to automate feature extraction and decision-making. This paper explores the interplay between data science and deep learning in building robust image classification and recognition systems. We highlight key advancements such as transfer learning, real-time object detection, and self-supervised learning, which have enabled these systems to excel in handling complex real-world challenges. Despite these advancements, significant challenges remain, including the need for large annotated datasets, high computational requirements, and addressing ethical issues like bias and fairness. Finally, the paper discusses emerging trends such as explainable AI, edge computing, and lightweight models, which are set to shape the next generation of image recognition technologies.

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