Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification

TL;DR

This study evaluates twelve ImageNet pre-trained models for electronic component classification, demonstrating high accuracy and practical benefits in manufacturing, with MobileNet-V2 achieving near-perfect results.

Contribution

It provides a comparative analysis of multiple pre-trained models, highlighting their effectiveness in classifying electronic components with limited data.

Findings

MobileNet-V2 achieved 99.95% accuracy

All models showed respectable performance

Pre-trained models are effective in electronics manufacturing

Abstract

Electronic component classification and detection are crucial in manufacturing industries, significantly reducing labor costs and promoting technological and industrial development. Pre-trained models, especially those trained on ImageNet, are highly effective in image classification, allowing researchers to achieve excellent results even with limited data. This paper compares the performance of twelve ImageNet pre-trained models in classifying electronic components. Our findings show that all models tested delivered respectable accuracies. MobileNet-V2 recorded the highest at 99.95%, while EfficientNet-B0 had the lowest at 92.26%. These results underscore the substantial benefits of using ImageNet pre-trained models in image classification tasks and confirm the practical applicability of these methods in the electronics manufacturing sector.