Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1401-1408.DOI: 10.11772/j.issn.1001-9081.2022040581
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Bin WANG1,2(), Tian XIANG1, Yidong LYU1, Xiaofan WANG1
Received:
2022-04-24
Revised:
2022-06-19
Accepted:
2022-07-01
Online:
2022-07-26
Published:
2023-05-10
Contact:
Bin WANG
About author:
WANG Bin, born in 1971, M. S., associate professor. His research interests include evolutionary computing, neural network.Supported by:
通讯作者:
王彬
作者简介:
王彬(1971—),男,陕西西安人,副教授,硕士,CCF会员,主要研究方向:进化计算、神经网络 wb@xaut.edu.cn基金资助:
CLC Number:
Bin WANG, Tian XIANG, Yidong LYU, Xiaofan WANG. Adaptive multi-scale feature channel grouping optimization algorithm based on NSGA‑Ⅱ[J]. Journal of Computer Applications, 2023, 43(5): 1401-1408.
王彬, 向甜, 吕艺东, 王晓帆. 基于NSGA‑Ⅱ的自适应多尺度特征通道分组优化算法[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1401-1408.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022040581
结构 | 平均精确度/% | 运行时间/s |
---|---|---|
M_blockNet_v1 | 91.492 5 | 8 176 |
MobileNet_v1 | 80.187 8 | 1 883 |
MobileNet_v2 | 83.333 5 | 2 162 |
ShuffleNet_v2 | 89.328 7 | 1 791 |
DenseNet | 91.734 1 | 4 862 |
SSCA_MblockNet | 92.300 5 | 5 132 |
NSGA2-AMFFNetwork | 92.712 7 | 4 818 |
Tab. 1 Comparison of average test accuracy and running time
结构 | 平均精确度/% | 运行时间/s |
---|---|---|
M_blockNet_v1 | 91.492 5 | 8 176 |
MobileNet_v1 | 80.187 8 | 1 883 |
MobileNet_v2 | 83.333 5 | 2 162 |
ShuffleNet_v2 | 89.328 7 | 1 791 |
DenseNet | 91.734 1 | 4 862 |
SSCA_MblockNet | 92.300 5 | 5 132 |
NSGA2-AMFFNetwork | 92.712 7 | 4 818 |
解序号 | NSGA2-AMFFModule1 | NSGA2-AMFFModule2 | ||||
---|---|---|---|---|---|---|
1×1 点卷积 | 3×3 标准卷积 | 3×3 膨胀卷积 | 1×1 点卷积 | 3×3 标准卷积 | 3×3 膨胀卷积 | |
1 | 27 | 58 | 11 | 175 | 34 | 21 |
2 | 97 | 7 | 10 | 192 | 20 | 28 |
3 | 80 | 5 | 11 | 198 | 13 | 29 |
Tab. 2 Channel grouping parameters of NSGA2-AMFFNetwork modules
解序号 | NSGA2-AMFFModule1 | NSGA2-AMFFModule2 | ||||
---|---|---|---|---|---|---|
1×1 点卷积 | 3×3 标准卷积 | 3×3 膨胀卷积 | 1×1 点卷积 | 3×3 标准卷积 | 3×3 膨胀卷积 | |
1 | 27 | 58 | 11 | 175 | 34 | 21 |
2 | 97 | 7 | 10 | 192 | 20 | 28 |
3 | 80 | 5 | 11 | 198 | 13 | 29 |
LCNN | 模块参数总量 | 模块计算总量/107 | 平均 精确度/% | 运行 时间/s |
---|---|---|---|---|
M_blockNet_v1 | 6 656 | 68.640 8 | 91.492 5 | 8 176 |
NSGA2-AMFFNetwork | 48 312 | 42.135 6 | 92.712 7 | 4 818 |
Tab. 3 Comparison results of complexity and accuracy before and after LCNN optimization
LCNN | 模块参数总量 | 模块计算总量/107 | 平均 精确度/% | 运行 时间/s |
---|---|---|---|---|
M_blockNet_v1 | 6 656 | 68.640 8 | 91.492 5 | 8 176 |
NSGA2-AMFFNetwork | 48 312 | 42.135 6 | 92.712 7 | 4 818 |
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