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.
@article{arxiv.2506.19330,
title = {Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification},
author = {Yidi Shao and Longfei Zhou and Fangshuo Tang and Xinyi Shi and Dalang Chen and Shengtao Xia},
journal= {arXiv preprint arXiv:2506.19330},
year = {2025}
}
Comments
Due to issues related to author order and some problems in the current version regarding methodology, we would like to withdraw the preprint to avoid potential conflicts