Loss function curve and detection precision experimented on NEU-DET dataset.

Loss function curve and detection precision experimented on NEU-DET dataset.

<p>Loss function curve and detection precision experimented on NEU-DET dataset.</p>

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Bibliographic Details
Main Author:	Shouluan Wu (22601074) (author)
Other Authors:	Hui Yang (91136) (author), Liefa Liao (12098633) (author), Chao Song (379006) (author), Yating Fang (5483327) (author), Yang Yang (45629) (author)
Published:	2025
Subjects:	Medicine Neuroscience Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified steel surfaces constitutes pose significant difficulties incorporating resnet18 along feature extraction functionality different convolutional groups detailed ablation studies deep learning framework convolutional neural networks channel random coding scale feature fusion low recognition accuracy de datasets demonstrate model &# 8217 attention mechanism associated scale features de dataset model registers model capitalizes classification accuracy xlink "> vital area transformer architecture thus minimizing parameter redundancy novel improvement conventional models considerable potential computer vision combined improvement cnns ), bring forth backbone network accurate identification 73 %, 72 %, 46 %. 03 %,
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Summary:	<p>Loss function curve and detection precision experimented on NEU-DET dataset.</p>

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