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- W4367016420 abstract "At present, the optimization of multi-label classification algorithm based on deep learning is not perfect. Based on this, this paper studies the multi-label classification algorithm based on detection. Based on YOLOv5s, an improved algorithm for image multi- label classification is proposed, which integrates SimAM attention- free module and GAM attention-mechanism effectively. Retain channel and spatial information to enhance cross-dimensional interaction, and improve the flexibility and effectiveness of the module. The experimental results show that the mean average accuracy (mAP) of the improved algorithm on the COCO dataset reaches 54.8%, which is 2.2% higher than that of the original YOLOv5s algorithm, and achieves good results in multi-label classification tasks." @default.
- W4367016420 created "2023-04-27" @default.
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- W4367016420 date "2023-02-24" @default.
- W4367016420 modified "2023-10-17" @default.
- W4367016420 title "An improved multi-label classification algorithm based on YOLOV5s" @default.
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- W4367016420 doi "https://doi.org/10.1109/nnice58320.2023.10105674" @default.
- W4367016420 hasPublicationYear "2023" @default.
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