对比12个ImageNet预训练模型在电子元器件分类中的表现
Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification
- 用12个ImageNet预训练模型做电子元器件分类实验
- 准确率最高达99.95%（MobileNet-V2），最低92.26%（EfficientNet-B0）
- 适合工业质检场景，尤其关注小样本图像分类的工程师
电子元器件的分类与检测在制造业中至关重要，能显著降低人力成本并推动技术进步。基于ImageNet预训练的模型在图像分类任务中表现出色，即使数据量有限也能取得优异效果。本文比较了12种ImageNet预训练模型在电子元器件分类中的性能。实验结果表明，所有模型均达到较高准确率：MobileNet-V2表现最佳，准确率为99.95%；EfficientNet-B0表现最差，准确率为92.26%。这些结果验证了使用ImageNet预训练模型在图像分类任务中的巨大优势，并确认其在电子制造领域的实际应用价值。
原文摘要 · Abstract (English)
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.
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