Classification And Storage of Images Using ML

The rapid growth in the number of images captured on mobile devices has created challenges in effectively organizing and managing large photo collections. Traditional image sorting methods are largely manual, time-consuming, and inefficient when handling unstructured data at scale. This paper presents an automated image classification and storage system based on a lightweight deep learning architecture, MobileNetV2, designed for efficient deployment in resource-constrained environments. The proposed system analyzes the visual content of images, classifies them into predefined categories, and automatically organizes them into a structured folder hierarchy for easy access and retrieval. By integrating efficient deep learning–based classification with automated storage management, the system significantly reduces user effort, improves image organization, and enables faster access to visual information, making it well suited for mobile and embedded applications.