VoltaVision: A Transfer Learning model for electronic component classification

TL;DR

This paper introduces VoltaVision, a lightweight transfer learning CNN model for classifying electronic components, demonstrating improved efficiency and effectiveness over complex models by leveraging knowledge transfer from similar tasks.

Contribution

The paper presents a novel lightweight CNN model, VoltaVision, specifically designed for electronic component classification using transfer learning techniques.

Findings

VoltaVision outperforms more complex models in accuracy.

Transfer learning from similar tasks improves classification results.

The model is computationally efficient and suitable for resource-constrained environments.

Abstract

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.