![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)
上QQ阅读APP看本书,新人免费读10天
设备和账号都新为新人
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_1.jpg?sign=1739299435-rJwNluG7Sq3J5fEGvg39oTDO60xPta04-0-f327bdceca6f64c720c6c4debf6469f3)
图1.7 灰度图与彩色图
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_2.jpg?sign=1739299435-1PXtpn92nSJZQktJAhOokwQtUbm6ltWY-0-eda7124622ac6bd92dd10c14d21dd4f6)
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=1739299435-vPSx4Hp0fKwJKASpOfgoYM0fDOFoBoS9-0-de651794737c98b32a70f956c079ec58)
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=1739299435-5ZwyTlEGJy0gC3Oj0KQE8xxtBqOOjNL7-0-269459504790d74732650fc21c7f4136)
图4.3 TDNN示意
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_1.jpg?sign=1739299435-IYgZjFgeXCIzVOsQdtASVnKl1ptBb8D2-0-b8cb7b9a5b6159eeb6d69c33406f8794)
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=1739299435-RjzanLfMsal5qhQBaH7GbG1iIB7fUW0b-0-7fcfeadc3244f44c3670d2f0c5d73950)
图7.13 Allconv5_SEG训练结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_3.jpg?sign=1739299435-B57lCLKmpBglH0i4M4A2INfDErwTPebY-0-e26b20dd977dae8c02ffd572601098c5)
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=1739299435-RhVjkiUOcbs48qe18oovztdPM2t79AoB-0-7ac00988a1e0833197ec407cb3454aa1)
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=1739299435-el0KCpuQLnALQJUgxH0n4Hp1T740GMAb-0-5addaf3041b8c6c924ba75a595163e81)
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=1739299435-TwXAJSuU4wZVzrsY6bFH67yd7cP9d0cw-0-90bbe8458d6e552faadee727dc94a124)
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=1739299435-LLpxdETdNlj8UoW5ivdwovby8wahbQ5z-0-6ecb755f8475466969f25cae106b310a)
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=1739299435-OQUX1rAeWd1i4MtBvTPUzPfPTQCE6pnp-0-15ca8f500c5d472c70db6afe3a5f913b)
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=1739299435-01GVXEbh7HIO3NIGXZ8icYoFeV14QdG2-0-aa67aedb5f6f9bff2fda5aaa3ac66d39)
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=1739299435-043PVtWXWJqC25jJv9fv2yDWtLHFHrLZ-0-13bc15df34e2d0afae6ec94fd5aa6e57)
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=1739299435-yNklYS6DPeNoE7xRoxR50U3AX5IGLnoh-0-343ae350863d1864d2c4b0692c76f43a)
图12.12 不同比例下的训练集和测试集精度