VoltaVision: A Transfer Learning model for electronic component
  classification
Anas Mohammad Ishfaqul Muktadir Osmani Taimur Rahman Salekul Islam
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.