Classification of Electronics Components using Deep Learning

Abstract

In this study, we present an advanced electronic component classification system with an exceptional classification accuracy exceeding 99% using state-of-the-art deep learning architectures. We employed EfficientNetV2B3, EfficientNetV2S, EfficientNetB0, InceptionV3, MobileNet, and Vision Transformer (ViT) models for the classification task. The system demonstrates the remarkable potential of these deep learning models in handling complex visual recognition tasks, specifically in the domain of electronic components. Our dataset comprises a diverse set of electronic components, and we meticulously curated and labeled it to ensure high-quality training data. We conducted extensive experiments to fine-tune and optimize the models for the given task, leveraging data augmentation techniques and transfer learning. The high classification accuracy achieved by our system indicates its readiness for real-world deployment, marking a significant step towards advancing automation and efficiency in the electronics industry.