Paper Title
Automated Waste Categorization Using Transfer Learning With Mobilenetv2

Abstract
In the fast-growing world to address escalating environmental concerns, efficient waste management[3] stands as a pivotal challenge for communities worldwide. The differentiation between recyclable, organic, and non-recyclable waste not only necessitates meticulous manual labor but also embodies a substantial operational cost for waste management facilities. Leveraging the advancements in artificial intelligence, this project introduces an innovative approach to waste classification through the deployment of MobileNetV2, a lightweight and efficient deep learning model optimized for mobile and embedded vision applications. This methodology capitalizes on the strengths of convolutional neural networks (CNNs)[11][14][20], specifically MobileNetV2[10], to automate the process of waste identification and categorization directly from images. The core objective is to streamline waste sorting processes by integrating a high-accuracy, real-time classification system capable of distinguishing between various categories of waste, thereby enhancing recycling rates and contributing to environmental sustainability[3]. By employing transfer learning techniques, through fine-tuning MobileNetV2 on a meticulously annotated dataset encompassing a broad spectrum of waste types. This approach optimizes the model for high precision and efficiency on edge devices, facilitating on-site waste classification. Keywords - Waste Learning, Transfer Learning, Data augmentation, MobileNetV2,CNN ,VGG16, fine tuning.