![深度学习之模型设计:核心算法与案例实践](https://wfqqreader-1252317822.image.myqcloud.com/cover/822/33114822/b_33114822.jpg)
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![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_1.jpg?sign=1739277671-kNvaBnPsLuKQaBmkvlke5Z9n30M5XYeI-0-50e665b3495b967f18523507376dbaa9)
图1.7 灰度图与彩色图
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_2.jpg?sign=1739277671-m5xSLIDyZiOrdIoPLOhZq2peclNqRRrS-0-ec650ac13c21a30c738404627d9ca43b)
图1.8 灰度图的直方图与彩色图的直方图
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_3.jpg?sign=1739277671-eXJ3EQhVPRFdZu2kAeKWZMqFwC7XSoD0-0-effd0d0f262a11a01f3f20409f628158)
图2.15 基于动量项的SGD示意
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_4.jpg?sign=1739277671-Ry2w8wFNuj7pYpBHn2Yhf7npm5j2iLmh-0-b3cb2438cf327885a6baaa95c84d6cf6)
图4.3 TDNN示意
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_1.jpg?sign=1739277671-MQYvXoBF0I43gjjfcdg1Xor75qZpWEXj-0-21b309528de98e6de92eb0efd969a438)
图6.1 AlexNet第一个卷积层的96个通道的可视化结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_2.jpg?sign=1739277671-oQk1Bv3iKsOJEjeu7uI8NSQu9NQftJ4e-0-da4735359786c4e4401af3aa514bb8f9)
图7.13 Allconv5_SEG训练结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_3.jpg?sign=1739277671-CcMWUzCSuk9QkpLr8vFfN92W0rvsH24i-0-54753486c0267812f72039ba5003b942)
图7.14 Allconv5_SEG使用224×224的分辨率进行测试的分割结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_4.jpg?sign=1739277671-gXQoutaUvIixi86bwWu3byRYtiZhPsJe-0-424cefd465a03966a06594485e5bc3af)
图7.16 Allconv5_SEG与Allconv5_Skip_SEG的训练结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_4_1.jpg?sign=1739277671-6oXoEZogbeF9xku9z1teA8bVupPpiyrH-0-47060be6fbcafa1b6101f7b6e839d2c4)
图7.17 Allconv5_Skip_SEG使用224×224的分辨率进行测试的分割结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_4_2.jpg?sign=1739277671-Fxo6Eh61N883mLY8AVw96qIBZw2OYY4h-0-052508e087d5baab2e12b419a5d07b15)
图7.18 Allconv5_SEG与Allconv5_Skip_SEG使用448×448的分辨率进行测试的分割结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_4_3.jpg?sign=1739277671-Yrk9Mwhyc3aXvD5E2Po8k5wRMARZX4MT-0-29ce1ace235905c732c69e74c1ffeb5d)
图8.11 嘴唇图像与标注示意
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_4_4.jpg?sign=1739277671-8MucfO4iWkqe0FLHmExMYPXfHGqhbirX-0-a35d9c3a10ea3333f626a257997befd2)
图8.13 MobileSegNet_MOUTH160精度曲线和损失曲线
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_5_1.jpg?sign=1739277671-xNWvuwAuCvNFt0yqsQdcHAhylXeNNbd3-0-336c7bd2dabc841237bbf4a897f2fffd)
图9.16 可变形卷积的感受野示意(使用大小为3×3的卷积核)
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_5_2.jpg?sign=1739277671-ShvCADWlW1pSXinLIPAq7YrIcUGFuSL0-0-a8e5061ad614dd473210a3ffad15b8ea)
图12.3 简单的三维卷积网络
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_5_3.jpg?sign=1739277671-6HqiBNPoNMu05CCEh3BU1FGkEeJrJ9LK-0-dc422af7d829436944fbf44c8ba85d86)
图12.12 不同比例下的训练集和测试集精度