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

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 pretrained models in classifying electronic components. Our findings show that all models tested delivered respectable accuracies. MobileNet-V2 recorded the highest at $\mathbf{9 9. 9 5 \%}$, while EfficientNetB0 had the lowest at $\mathbf{9 2. 2 6 \%}$. 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.