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

Source: [Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification](<https://www.sota2.com/research/paper/68fa7a581e209131cea82a05>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:19.922Z

Authors: Yidi Shao, Longfei Zhou, Fangshuo Tang, Xinyi Shi, Dalang Chen, Shengtao Xia

Published: 2025-06-24T05:48:39.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.19330>)

[PDF](<https://arxiv.org/pdf/2506.19330.pdf>)