Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.19330 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912512704774144 |
|---|---|
| author | Shao, Yidi Zhou, Longfei Tang, Fangshuo Shi, Xinyi Chen, Dalang Xia, Shengtao |
| author_facet | Shao, Yidi Zhou, Longfei Tang, Fangshuo Shi, Xinyi Chen, Dalang Xia, Shengtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification Shao, Yidi Zhou, Longfei Tang, Fangshuo Shi, Xinyi Chen, Dalang Xia, Shengtao Computer Vision and Pattern Recognition 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. |
| title | Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.19330 |