![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=1739669617-2kGuqlChMSFvDCIvwadwr6nIsBDRat8g-0-e236bb99cb7e9fee0edc6f08f0b3a323)
图1.7 灰度图与彩色图
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_2_2.jpg?sign=1739669617-0YvUmp5xmpotCCeZVxtFET6gsHS9aV2o-0-8aaf082ddaf3897c2bfedb2044f39721)
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=1739669617-xVVn3lu4JZl7hUPAXq1zhbzyyM7NFr7e-0-fd6e6b770f29e83d0f11bffaecfee5d3)
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=1739669617-NmPymZEurUJuIgoco1swDXsywSMeo1nI-0-36921d57d22bd909781326c54254db9d)
图4.3 TDNN示意
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_1.jpg?sign=1739669617-k5TXWZ8aGkjRtvC5YCg663vTXJkQHSmA-0-614c10c2649a70193e8e1f968d14da5f)
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=1739669617-Dbw6TXcL14hfbrpo4D4Ad0LTyPacUVSv-0-c284b0a8d8130e43a80ec50deb3f8c7b)
图7.13 Allconv5_SEG训练结果
![](https://epubservercos.yuewen.com/60C819/17725770607802806/epubprivate/OEBPS/Images/39030_3_3.jpg?sign=1739669617-R0kh3BmKAW0yQa1WCESIpUX62RSGo2QN-0-a53c55f5b89d8340de2a5a7942025c62)
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=1739669617-pwMFt9hACcsObp3Vcu8JbnZogKd8dcGu-0-52ad2bca8d576921b3c7171c13c7bdb6)
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=1739669617-yAPpUfnAVtZTRLQSrh2Gl6GA1U8vxWCZ-0-b72255ba97e01610cdfcb138d7d1996a)
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=1739669617-oY6EdEH6gyVuwmg6LwOkvCEPdsRG7ORl-0-49c91b18eb82351ed9036a1bf12a7360)
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=1739669617-rZQfuScYtq3MkKA3mO8tYnye7Ta8j6vg-0-d4dfc5041d9e14822db279bd13b87f39)
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=1739669617-nbUitdIuR9dnHQ5fnnPrIE797LHpAGXB-0-e51c24c0ec819c9e2074df67bf904f2f)
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=1739669617-Ht3AbfdHnzSta1fImOAYGh1j3EbKm5xk-0-7f5341c38a6790a93e87e61f1cef7ba4)
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=1739669617-a4b0dubZ6EvvZwyTxvC1iuYo6uGXh8NS-0-00f9d672088c46860dc6335e7e08fb31)
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=1739669617-8Kxu1G7qR7arx4mTNY4kJhXfb2gdvrR6-0-c0cc2fa83296f9cca80300e20f18e83e)
图12.12 不同比例下的训练集和测试集精度