Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification
This work provides practical guidance for using pre-trained models in electronics manufacturing to reduce labor costs, but it is incremental as it applies existing methods to a new dataset.
The paper compared twelve ImageNet pre-trained models for classifying electronic components, finding that all achieved respectable accuracies, with MobileNet-V2 reaching 99.95% and EfficientNet-B0 the lowest at 92.26%.
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.