In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch. Our work introduces a lightweight CNN, coined as VoltaVision, and compares its performance against more complex models. We test the hypothesis that transferring knowledge from a similar task to our target domain yields better results than state-of-the-art models trained on general datasets. Our dataset and code for this work are available at https://github.com/AnasIshfaque/VoltaVision.
@article{arxiv.2404.03898,
title = {VoltaVision: A Transfer Learning model for electronic component classification},
author = {Anas Mohammad Ishfaqul Muktadir Osmani and Taimur Rahman and Salekul Islam},
journal= {arXiv preprint arXiv:2404.03898},
year = {2024}
}