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GitHub - Charmve/computer-vision-in-action: A computer vision closed-loop learning platform where code can be run interactively online. 学习闭环《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（持续更新中 ...） 📘 在线电子书 https://charmve.github.io/computer-vision-in-action/ 👇项目主页
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10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"code_search","context":"product","tag":"link","label":"code_search_link_product_navbar"}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 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href=\"https://github.com/Charmve\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/ecb1a7cccdfba231b654a8d1f956749524459fd9da5bc6493830a6b4f2d0a162/68747470733a2f2f6261646765732e737472726c2e6465762f7669736974732f436861726d76652f636f6d70757465722d766973696f6e2d696e2d616374696f6e3f636f6c6f723d626c61636b266c6f676f3d676974687562\" data-canonical-src=\"https://badges.strrl.dev/visits/Charmve/computer-vision-in-action?color=black\u0026amp;logo=github\" style=\"max-width: 100%;\"\u003e\n\t\n\u003c/a\u003e\n\u003c/p\u003e\n\n\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\"如果你只是看了这个项目的在线文档，那么你并没有利用好这个项目。太可惜！\"\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003ca href=\"https://charmve.github.io/computer-vision-in-action\" rel=\"nofollow\"\u003e\n \u003cimg src=\"/Charmve/computer-vision-in-action/raw/main/res/ui/mutil-platform.png\" width=\"=\u0026quot;\u0026quot;\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e✨ 以用促学，先会后懂 ✨\u003c/h2\u003e\u003ca id=\"user-content--以用促学先会后懂-\" class=\"anchor\" aria-label=\"Permalink: ✨ 以用促学，先会后懂 ✨\" href=\"#-以用促学先会后懂-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eL0CV \u003csup\u003e\u003ca href=\"https://charmve.github.io/L0CV-web/\" rel=\"nofollow\"\u003e\u003ccode\u003e🔊\u003c/code\u003e\u003c/a\u003e\u003c/sup\u003e 一种结合了代码、图示和HTML的在线学习媒介\u003c/h3\u003e\u003ca id=\"user-content-l0cv---一种结合了代码图示和html的在线学习媒介\" class=\"anchor\" aria-label=\"Permalink: L0CV 一种结合了代码、图示和HTML的在线学习媒介\" href=\"#l0cv---一种结合了代码图示和html的在线学习媒介\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cbr\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable align=\"center\"\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003e\n\u003ccode\u003e全面\u003c/code\u003e \u003ccode\u003e前沿\u003c/code\u003e \u003ccode\u003e免费\u003c/code\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e 计算机视觉实战演练：算法与应用 \u003csup\u003e 📌\u003c/sup\u003e\n\u003cbr\u003e\u003cem\u003eComputer Vision in Action\u003c/em\u003e\u003c/h1\u003e\u003ca id=\"user-content--计算机视觉实战演练算法与应用--computer-vision-in-action\" class=\"anchor\" aria-label=\"Permalink: 计算机视觉实战演练：算法与应用 Computer Vision in Action\" href=\"#-计算机视觉实战演练算法与应用--computer-vision-in-action\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" 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100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e跨平台，只需一个浏览器即可！\n\u003cbr\u003e\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e👉 在线阅读（优先更新实战篇和进阶篇）\u003c/h4\u003e\u003ca id=\"user-content---在线阅读优先更新实战篇和进阶篇\" class=\"anchor\" aria-label=\"Permalink: 👉 在线阅读（优先更新实战篇和进阶篇）\" href=\"#--在线阅读优先更新实战篇和进阶篇\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 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1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n- 地址：\u003ca href=\"https://github.com/charmve/computer-vision-in-action/releases\"\u003ehttps://github.com/charmve/computer-vision-in-action/releases\u003c/a\u003e \u003cb\u003e(马上来 ...)\u003c/b\u003e\n\u003c/blockquote\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\n\u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv align=\"right\" dir=\"auto\"\u003e\n\u003cdetails\u003e\u003csummary\u003e分享 \u003ccode\u003e\u003ca href=\"https://user-images.githubusercontent.com/29084184/126729326-cad7a288-e424-4f69-a47a-90f61e7d9879.png\" rel=\"nofollow\"\u003e\u003cimg 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id=\"user-content-index\" class=\"anchor\" aria-label=\"Permalink: Index\" href=\"#index\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/Charmve/computer-vision-in-action/blob/main/res/ui/L0CV.png\"\u003e\u003cimg align=\"right\" src=\"/Charmve/computer-vision-in-action/raw/main/res/ui/L0CV.png\" width=\"63%\" alt=\"L0CV architecture\" title=\"L0CV architecture\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#-%E5%85%A8%E4%B9%A6%E7%BB%84%E7%BB%87\"\u003e💠 全书组织\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-%E6%84%BF%E6%99%AF\"\u003e🌈 愿景\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-%E6%9C%AC%E4%B9%A6%E7%9B%AE%E5%BD%95\"\u003e📘 本书目录\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-%E6%96%87%E4%BB%B6%E6%B5%8F%E8%A7%88\"\u003e🔍 文件浏览\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#l0cv-demoday\"\u003e🌼 L0CV-DemoDay\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-%E5%A6%82%E4%BD%95%E9%A3%9F%E7%94%A8\"\u003e🔑 如何食用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98\"\u003e❓ 常见问题\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#-%E7%A4%BE%E5%8C%BA%E4%BA%92%E5%8A%A9\"\u003e👥 社区互助\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E8%87%B4%E8%B0%A2\"\u003e❤️ 致谢\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#license\"\u003e📄 LICENSE\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contribution\"\u003e👐 参与项目\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E5%85%B3%E6%B3%A8%E6%88%91%E4%BB%AC\"\u003e🤝 关注我们\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003e📎 Citation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"right\" dir=\"auto\"\u003e\n\t🏷️ \u003ccode\u003esec_code\u003c/code\u003e \u003cb\u003e图1 L0CV 结构图\u003c/b\u003e\n\u003c/p\u003e \n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e💠 全书组织\u003c/h2\u003e\u003ca id=\"user-content--全书组织\" class=\"anchor\" aria-label=\"Permalink: 💠 全书组织\" href=\"#-全书组织\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e🏷️ \u003ccode\u003efig_book_org\u003c/code\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/Charmve/computer-vision-in-action/blob/main/res/ui/book_org.png\"\u003e\u003cimg src=\"/Charmve/computer-vision-in-action/raw/main/res/ui/book_org.png\" alt=\"book_org.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e \n\u003cp align=\"right\" dir=\"auto\"\u003e\n \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/res/计算机视觉实战演练：算法与应用_思维导图.pdf\"\u003e全书详细思维导图\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e本书详细介绍，请移步 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/book_preface.md\"\u003e\u003cb\u003e序言\u003c/b\u003e\u003c/a\u003e。\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e第一部分包括基础知识和预备知识。提供深度学习的入门课程，然后在理论篇中，将快速向你介绍实践计算机视觉所需的前提条件，例如如何存储和处理数据，以及如何应用基于线性代数、微积分和概率基本概念的各种数值运算，涵盖了深度学习的最基本概念和技术，例如线性回归、多层感知机和正则化。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e第二部分是本书涉及的计算机视觉基础理论，核心部分为神经网络模型，包括神经网络、卷积神经网络、循环神经网络理论讲解，以图像分类、模型拟合与优化作为其代码的实战项目。在模型拟合和优化章节中，着重分享梯度下降、随机梯度下降、动量法、AdaBoost等方法。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e接下来的七章集中讨论现代计算机视觉技术实战，也是本书的核心部分。围绕这样的组织逻辑：什么是计算机视觉？计算机视觉解决什么问题，都是怎么解决的？传统方法——以卷积神经网络为中心的神经网络；现代方法——Transformer、强化学习、迁移学习、生成对抗等。各种方法是如何实现的，用到了什么框架？在第7章中，描述了计算机视觉的经典卷积神经网络PyTorch实现，并为我们随后实现更复杂的模型奠定了基础。在随后的几个章节中，我们主要解决图像分类、目标检测、语义分割、3D重建等实际问题，并给出实战项目。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e该部分以项目为实战指导，给出详细的项目指导书和代码实现，更为特别的是，给出了\u003cstrong\u003enotebook\u003c/strong\u003e可以直接在线运行，跑通结果，免去了本地运行环境的搭建复杂性。于此同时，为了方便读者在本地调试，作者建立了一个名为 \u003ccode\u003eL0CV\u003c/code\u003e 的第三方包，可以直接在代码中 \u003ccode\u003eimport L0CV\u003c/code\u003e 后使用。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e第三部分讨论最近几年出现的\u003cb\u003e“网红”模型\u003c/b\u003e，诸如：Transformer、Attention、知识蒸馏、迁移学习、生成对抗模型等。这部分也是此份资料的力挺之作。最后，在 \u003ccode\u003echap_optimization\u003c/code\u003e 中，我们讨论了用于训练深度学习模型的几种常用优化算法，如：模型压缩、模型剪枝、微调、蒸馏等。\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🌈 愿景\u003c/h2\u003e\u003ca id=\"user-content--愿景\" class=\"anchor\" aria-label=\"Permalink: 🌈 愿景\" href=\"#-愿景\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e本开源项目代表了我们的一种尝试：我们将教给读者概念、背景知识和代码；我们将在同一个地方阐述剖析问题所需的批判性思维、解决问题所需的数学知识，以及实现解决方案所需的工程技能。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e我们的目标是创建一个为实现以下目标的统一资源：\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e所有人均可在网上免费获取；\u003c/li\u003e\n\u003cli\u003e提供足够的技术深度，从而帮助读者实际成为计算机视觉应用科学家：既理解数学原理，又能够实现并不断改进方法；\u003c/li\u003e\n\u003cli\u003e包含可运行的代码，为读者展示如何在实际中解决问题。这样不仅直接将数学公式对应成实际代码，而且可以修改代码、观察结果并及时获取经验；\u003c/li\u003e\n\u003cli\u003e允许我们和整个\u003ca href=\"https://github.com/Charmve/computer-vision-in-action/discussions\"\u003e社区\u003c/a\u003e不断快速迭代内容，从而紧跟仍在高速发展的计算机视觉领域；\u003c/li\u003e\n\u003cli\u003e由包含有关技术细节问答的论坛作为补充，使大家可以相互答疑并交换经验。\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📘 本书目录\u003c/h2\u003e\u003ca id=\"user-content--本书目录\" class=\"anchor\" aria-label=\"Permalink: 📘 本书目录\" href=\"#-本书目录\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e👉 在线阅读（优先更新实战篇和进阶篇）\u003c/h4\u003e\u003ca id=\"user-content---在线阅读优先更新实战篇和进阶篇-1\" class=\"anchor\" aria-label=\"Permalink: 👉 在线阅读（优先更新实战篇和进阶篇）\" href=\"#--在线阅读优先更新实战篇和进阶篇-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n- 地址：\u003ca href=\"https://charmve.github.io/computer-vision-in-action/\" rel=\"nofollow\"\u003ehttps://charmve.github.io/computer-vision-in-action/\u003c/a\u003e\n\u003c/blockquote\u003e\n\u003cdetails\u003e\u003csummary\u003e📘 详细目录(点击展开)\u003c/summary\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable align=\"center\"\u003e\n\u003ctbody\u003e\u003ctr\u003e\n\u003ctd\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/book_preface.md\"\u003e序言\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-序言\" class=\"anchor\" aria-label=\"Permalink: 序言\" href=\"#序言\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e主要符号表\u003c/h4\u003e\u003ca id=\"user-content-主要符号表\" class=\"anchor\" aria-label=\"Permalink: 主要符号表\" href=\"#主要符号表\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e绪论篇\u003c/h4\u003e\u003ca id=\"user-content-绪论篇\" class=\"anchor\" aria-label=\"Permalink: 绪论篇\" href=\"#绪论篇\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e第 0 章 \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/chapter0/chapter0\" rel=\"nofollow\"\u003e计算机视觉概述\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e0.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.1_%E6%A6%82%E8%BF%B0.md\"\u003e概述\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e0.1.1 什么是计算机视觉\u003c/li\u003e\n\u003cli\u003e0.1.2 计算机视觉解决什么问题\u003c/li\u003e\n\u003cli\u003e0.1.3 行业应用\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e0.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.2_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5.md\"\u003e计算机视觉基本概念\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e0.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.3_%E5%8F%91%E5%B1%95%E5%8E%86%E5%8F%B2%E5%9B%9E%E9%A1%BE.md\"\u003e发展历史回顾\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e0.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.4_%E5%85%B8%E5%9E%8B%E7%9A%84%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E4%BB%BB%E5%8A%A1.md\"\u003e典型的计算机视觉任务\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e图像分类\u003c/li\u003e\n\u003cli\u003e目标识别与目标检测\u003c/li\u003e\n\u003cli\u003e实例分割与语义分割\u003c/li\u003e\n\u003cli\u003e3D 建模\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e0.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.5_%E5%9B%BD%E5%86%85%E5%A4%96%E4%BC%98%E7%A7%80%E7%9A%84%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%9B%A2%E9%98%9F%E6%B1%87%E6%80%BB.md\"\u003e国内外优秀的计算机视觉团队汇总\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小练习\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e理论篇\u003c/h4\u003e\u003ca id=\"user-content-理论篇\" class=\"anchor\" aria-label=\"Permalink: 理论篇\" href=\"#理论篇\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e第 1 章 \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/chapter2/chapter2\" rel=\"nofollow\"\u003e神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e1.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.1_line-regression.md\"\u003e线性回归\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e1.1.1 基本原理\u003c/li\u003e\n\u003cli\u003e1.1.2 从零实现线性回归\u003c/li\u003e\n\u003cli\u003e1.1.3 线性回归的简洁实现\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e1.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter1.2_Softmax%E5%9B%9E%E5%BD%92.md\"\u003eSoftmax 回归\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e1.2.1 softmax回归模型\u003c/li\u003e\n\u003cli\u003e1.2.2 从零开始实现softmax回归\u003c/li\u003e\n\u003cli\u003e1.2.3 softmax回归的简洁实现\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e1.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.3_%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E5%99%A8MLP.md\"\u003e多层感知器\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e1.3.1 基本原理\u003c/li\u003e\n\u003cli\u003e1.3.2 从零开始实现多层感知器\u003c/li\u003e\n\u003cli\u003e1.3.3 多层感知器的简洁实现\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e1.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.4_Back-Propagation.md\"\u003e反向传播算法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e1.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.5_neural-networks.md\"\u003e神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e1.5.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.5.1_%E7%A5%9E%E7%BB%8F%E5%AD%A6%E8%A7%82%E7%82%B9.md\"\u003e神经学观点\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e1.5.2 \u003ca href=\"https://cs231n.github.io/neural-networks-1/\" rel=\"nofollow\"\u003e神经网络1-建立神经网络架构\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e1.5.3 \u003ca href=\"https://cs231n.github.io/neural-networks-2/\" rel=\"nofollow\"\u003e神经网络2-设置数据和损失\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e1.5.4 \u003ca href=\"https://cs231n.github.io/neural-networks-3/\" rel=\"nofollow\"\u003e神经网络3-学习和评估\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e1.5.5 \u003ca href=\"https://cs231n.github.io/neural-networks-case-study/\" rel=\"nofollow\"\u003e案例分析-最小神经网络案例研究\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e1.6 \u003ca href=\"https://blog.csdn.net/Charmve/article/details/108531735\" rel=\"nofollow\"\u003e实战项目 1 - 手写字分类\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 2 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md\"\u003e卷积神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#21-%E4%BB%8E%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%B0%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C\"\u003e从神经网络到卷积神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2.1.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#211-%E5%AE%9A%E4%B9%89\"\u003e定义\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.1.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#212-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E6%9E%B6%E6%9E%84\"\u003e卷积神经网络的架构\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#22-%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%E7%9A%84%E5%B1%82%E7%BA%A7%E7%BB%93%E6%9E%84\"\u003e卷积网络的层级结构\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2.2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#221-%E6%95%B0%E6%8D%AE%E8%BE%93%E5%85%A5%E5%B1%82\"\u003e数据输入层\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#222-%E5%8D%B7%E7%A7%AF%E8%AE%A1%E7%AE%97%E5%B1%82\"\u003e卷积计算层\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.2.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#223-%E9%9D%9E%E7%BA%BF%E6%80%A7%E5%B1%82%E6%88%96%E6%BF%80%E6%B4%BB%E5%B1%82\"\u003e非线性层（或激活层）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.2.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#224-%E6%B1%A0%E5%8C%96%E5%B1%82\"\u003e池化层\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.2.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#225-%E5%85%A8%E8%BF%9E%E6%8E%A5%E5%B1%82\"\u003e全连接层\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#23-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%87%A0%E7%82%B9%E8%AF%B4%E6%98%8E\"\u003e卷积神经网络的几点说明\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md\"\u003e实战项目 2 - 动手搭建一个卷积神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e2.4.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#271-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD\"\u003e卷积神经网络的前向传播\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.4.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#272-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD\"\u003e卷积神经网络的反向传播\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2.4.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#273-%E6%89%8B%E5%86%99%E4%B8%80%E4%B8%AA%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C\"\u003e手写一个卷积神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#1-%E5%AE%9A%E4%B9%89%E4%B8%80%E4%B8%AA%E5%8D%B7%E7%A7%AF%E5%B1%82\"\u003e1. 定义一个卷积层\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#2-%E6%9E%84%E9%80%A0%E4%B8%80%E4%B8%AA%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0\"\u003e2. 构造一个激活函数\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#3-%E5%AE%9A%E4%B9%89%E4%B8%80%E4%B8%AA%E7%B1%BB%E4%BF%9D%E5%AD%98%E5%8D%B7%E7%A7%AF%E5%B1%82%E7%9A%84%E5%8F%82%E6%95%B0%E5%92%8C%E6%A2%AF%E5%BA%A6\"\u003e3. 定义一个类，保存卷积层的参数和梯度\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#4-%E5%8D%B7%E7%A7%AF%E5%B1%82%E7%9A%84%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD\"\u003e4. 卷积层的前向传播\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#5-%E5%8D%B7%E7%A7%AF%E5%B1%82%E7%9A%84%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD\"\u003e5. 卷积层的反向传播\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#6-MaxPooling%E5%B1%82%E7%9A%84%E8%AE%AD%E7%BB%83\"\u003e6. MaxPooling层的训练\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e2.4.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#274-PaddlePaddle%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%BA%90%E7%A0%81%E8%A7%A3%E6%9E%90\"\u003ePaddlePaddle卷积神经网络源码解析\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#%E5%B0%8F%E7%BB%93\"\u003e小结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 3 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification\"\u003e图像分类\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e3.1 \u003ca href=\"https://cs231n.github.io/classification/\" rel=\"nofollow\"\u003e数据驱动方法\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e3.1.1 语义上的差别\u003c/li\u003e\n\u003cli\u003e3.1.2 图像分类任务面临着许多挑战\u003c/li\u003e\n\u003cli\u003e3.1.3 数据驱动的方法\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e3.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter3.2_knn.md\"\u003ek 最近邻算法\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e3.2.1 k 近邻模型\u003c/li\u003e\n\u003cli\u003e3.2.2 k 近邻模型三个基本要素\u003c/li\u003e\n\u003cli\u003e3.2.3 KNN算法的决策过程\u003c/li\u003e\n\u003cli\u003e3.2.4 k 近邻算法Python实现\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e3.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter3.3_%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA.md\"\u003e支持向量机\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e3.3.1 概述\u003c/li\u003e\n\u003cli\u003e3.3.2 线性支持向量机\u003c/li\u003e\n\u003cli\u003e3.3.3 从零开始实现支持向量机\u003c/li\u003e\n\u003cli\u003e3.3.4 支持向量机的简洁实现\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e3.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter3.4_Logistic-Regression.md\"\u003e逻辑回归 LR\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e3.4.1 逻辑回归模型\u003c/li\u003e\n\u003cli\u003e3.4.2 从零开始实现逻辑回归\u003c/li\u003e\n\u003cli\u003e3.4.3 逻辑回归的简洁实现\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e3.5 \u003ca href=\"https://blog.csdn.net/charmve/category_9754344.html\" rel=\"nofollow\"\u003e实战项目 3 - 表情识别\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e3.6 \u003ca href=\"http://mp.weixin.qq.com/s?__biz=MzIxMjg1Njc3Mw%3D%3D\u0026amp;chksm=97bef597a0c97c813e185e1bbf987b93d496c6ead8371364fd175d9bac46e6dcf7059cf81cb2\u0026amp;idx=1\u0026amp;mid=2247487293\u0026amp;scene=21\u0026amp;sn=89684d1c107177983dc1b4dca8c20a5b#wechat_redirect\" rel=\"nofollow\"\u003e实战项目 4 - 使用卷积神经网络对CIFAR10图片进行分类\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/README.md#%E5%B0%8F%E7%BB%93\"\u003e小结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/README.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 4 章 循环神经网络\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.1_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md\"\u003e4.1 循环神经网络 RNN\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.1_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md\"\u003e4.2 循环神经网络的从零开始实现\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.1_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md\"\u003e4.3 循环神经网络的简洁实现\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.4_%E9%95%BF%E7%9F%AD%E6%9C%9F%E8%AE%B0%E5%BF%86%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9CLSTM.md\"\u003e4.4 长短期记忆人工神经网络 LSTM\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.5_%E9%97%A8%E6%8E%A7%E5%BE%AA%E7%8E%AF%E5%8D%95%E5%85%83.md\"\u003e4.5 门控循环单元（GRU）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e练习\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 5 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md\"\u003e图神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md#51-%E5%8E%86%E5%8F%B2%E8%84%89%E7%BB%9C\"\u003e历史脉络\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2 \u003ca href=\"https://www.cnblogs.com/SivilTaram/p/graph_neural_network_1.html\" rel=\"nofollow\"\u003e图神经网络(Graph Neural Network)\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md#51-%E5%B8%B8%E8%A7%81%E6%95%B0%E6%8D%AE%E9%9B%86\"\u003e状态更新与输出\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#812-pytorch%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E8%AF%BB%E5%8F%96%E6%96%B9%E6%B3%95%E7%AE%80%E4%BB%8B\"\u003e不动点理论\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#813-%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA%E7%AE%80%E4%BB%8B\"\u003e具体实现\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e模型学习\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.2_%E5%9F%BA%E5%87%86BenchMark.md\"\u003eGNN与RNN\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003eGNN的局限\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e5.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e门控图神经网络(Gated Graph Neural Network)\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.3.1 状态更新\u003c/li\u003e\n\u003cli\u003e5.3.2 实例1:到达判断\u003c/li\u003e\n\u003cli\u003e5.3.3 实例2:语义解析\u003c/li\u003e\n\u003cli\u003e5.3.4 GNN与GGNN\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e5.4 \u003ca href=\"https://www.cnblogs.com/SivilTaram/p/graph_neural_network_2.html\" rel=\"nofollow\"\u003e图卷积神经网络(GCNN)\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.4.1 图卷积缘起\u003c/li\u003e\n\u003cli\u003e5.4.2 图卷积框架(Framework)\u003c/li\u003e\n\u003cli\u003e5.4.3 再谈卷积\u003c/li\u003e\n\u003cli\u003e5.4.4 空域卷积(Spatial Convolution)\u003c/li\u003e\n\u003cli\u003e5.4.5 消息传递网络(Message Passing Neural Network)\u003c/li\u003e\n\u003cli\u003e5.4.6 图采样与聚合(Graph Sample and Aggregate)\u003c/li\u003e\n\u003cli\u003e5.4.7 图结构序列化(PATCHY-SAN)\u003c/li\u003e\n\u003cli\u003e5.4.8 频域卷积(Spectral Convolution)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e5.5 \u003ca href=\"https://www.cnblogs.com/SivilTaram/p/graph_neural_network_3.html\" rel=\"nofollow\"\u003e生成图表示\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.5.1 图读出操作(ReadOut)\u003c/li\u003e\n\u003cli\u003e5.5.2 基于统计的方法(Statistics Category)\u003c/li\u003e\n\u003cli\u003e5.5.3 基于学习的方法(Learning Category)\u003c/li\u003e\n\u003cli\u003e5.5.4 其他方法\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e5.6 \u003ca href=\"https://www.cnblogs.com/SivilTaram/p/graph_neural_network_3.html\" rel=\"nofollow\"\u003e图神经网络在计算机视觉上的应用\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.6.1 \u003ca href=\"https://arxiv.org/abs/1904.03751\" rel=\"nofollow\"\u003e点云分割\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.6.2 \u003ca href=\"https://openreview.net/forum?id=SJeXSo09FQ\" rel=\"nofollow\"\u003e点云生成\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.6.3 \u003ca href=\"https://www.cs.toronto.edu/~urtasun/publications/qi_etal_iccv17.pdf\" rel=\"nofollow\"\u003eRGBD图像分割\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.6.4 \u003ca href=\"https://visualqa.org/\" rel=\"nofollow\"\u003e视觉问答VQA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.6.5 \u003ca href=\"https://arxiv.org/pdf/1803.08035.pdf\" rel=\"nofollow\"\u003e零次学习ZSL\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 5 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks\"\u003e模型选择、欠拟合和过拟合\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks\"\u003e训练误差和泛化误差\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks\"\u003e模型选择\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks\"\u003e欠拟合和过拟合\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks\"\u003e多项式函数拟合实验\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e5.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks\"\u003e数值稳定性和模型初始化\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 6 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization\"\u003e模型拟合与优化算法\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e6.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.1_optimization-intro.ipynb\"\u003e优化与深度学习\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.2_gd-sgd.ipynb\"\u003e梯度下降和随机梯度下降\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.3_minibatch-sgd.ipynb\"\u003e小批量随机梯度下降\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.4_momentum.ipynb\"\u003e动量法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.5_adagrad.ipynb\"\u003eAdaGrad算法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.6_rmsprop.ipynb\"\u003eRMSProp算法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.7 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.7_adadelta.ipynb\"\u003eAdaDelta算法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.8 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.8_adam.ipynb\"\u003eAdam算法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e实战篇\u003c/h4\u003e\u003ca id=\"user-content-实战篇\" class=\"anchor\" aria-label=\"Permalink: 实战篇\" href=\"#实战篇\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e第 6 章 \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/chapter6/chapter6\" rel=\"nofollow\"\u003e软件环境搭建与工具使用\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e6.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.1_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA%E6%8C%87%E5%8D%97.md\"\u003e深度学习环境搭建指南\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md\"\u003ePytorch 基础使用介绍\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e6.2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#621-tensors\"\u003eTensors\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#622-operations\"\u003eOperations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.2.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#623-numpy%E6%A1%A5%E6%A2%81\"\u003eNumpy桥梁\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.2.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#624-cuda-tensors\"\u003eCUDA Tensors\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e6.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/02_Python.ipynb\"\u003ePython\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/03_NumPy.ipynb\"\u003eNumpy 基础使用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/04_Pandas.ipynb\"\u003ePandas 基础使用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/OpenCV-ImageStitching.ipynb\"\u003eOpenCV 安装及基础使用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.7 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/01_Notebooks.ipynb\"\u003eJupyter Notebook 配置及基础使用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.8 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md\"\u003e基本的图像操作和处理\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e6.8.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#781-pil-python%E5%9B%BE%E5%83%8F%E5%A4%84%E7%90%86%E7%B1%BB%E5%BA%93\"\u003ePIL：Python图像处理类库\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.8.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#782-matplotlib\"\u003eMatplotlib\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.8.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#783-numpy\"\u003eNumPy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.8.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#784-scipy\"\u003eSciPy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6.8.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#785-%E9%AB%98%E7%BA%A7%E7%A4%BA%E4%BE%8B-%E5%9B%BE%E5%83%8F%E5%8E%BB%E5%99%AA\"\u003e高级示例：图像去噪\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e6.9 \u003ca href=\"https://blog.csdn.net/Charmve/article/details/107897468\" rel=\"nofollow\"\u003e实战项目 5 - 使用OpenCV进行图像全景拼接\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 7 章 \u003ca href=\"https://github.com/Charmve/Semantic-Segmentation-PyTorch\"\u003e经典卷积神经网络架构：原理与PyTorch实现\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e7.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.1%20%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88LeNet%EF%BC%89.md\"\u003e卷积神经网络（LeNet）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.2%20%E6%B7%B1%E5%BA%A6%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88AlexNet%EF%BC%89.md\"\u003e深度卷积神经网络（AlexNet）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.3%20%E4%BD%BF%E7%94%A8%E9%87%8D%E5%A4%8D%E5%85%83%E7%B4%A0%E7%9A%84%E7%BD%91%E7%BB%9C%EF%BC%88VGG%EF%BC%89.md\"\u003e使用重复元素的网络（VGG）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.4%20%E5%90%AB%E5%B9%B6%E8%A1%8C%E8%BF%9E%E7%BB%93%E7%9A%84%E7%BD%91%E7%BB%9C%EF%BC%88GoogLeNet%EF%BC%89.md\"\u003e含并行连结的网络（GoogLeNet）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.6_%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C-ResNet.md\"\u003e残差网络（ResNet）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.7_%E4%BA%8C%E9%98%B6%E7%BD%91%E7%BB%9C%E7%BC%96%E7%A0%81%E8%A7%A3%E7%A0%81-UNet.md\"\u003e二阶网络编码解码（U-Net）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.7 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.8_%E7%A8%A0%E5%AF%86%E8%BF%9E%E6%8E%A5%E7%BD%91%E7%BB%9C-DenseNet.md\"\u003e稠密连接网络（DenseNet）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.8 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.9_%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E7%BD%91%E7%BB%9C-SegNet.md\"\u003e语义分割网络（SegNet）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.9 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.9_%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2%E7%BD%91%E7%BB%9C-Mask-RCNN.md\"\u003e实例分割网络（Mask-RCNN）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.10 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.10_%E5%8C%BA%E5%9F%9F%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C-RCNN.md\"\u003e区域卷积神经网络（R-CNN）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.11 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.11_%E5%85%A8%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C-FCN.md\"\u003e全卷积网络（FCN）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e7.12 \u003ca href=\"https://pjreddie.com/darknet/yolo/\" rel=\"nofollow\"\u003eYOLO: 实时目标检测\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 8 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86\"\u003e著名数据集及基准\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e8.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md\"\u003e数据集\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e8.1.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#811-%E5%B8%B8%E8%A7%81%E6%95%B0%E6%8D%AE%E9%9B%86\"\u003e常见数据集\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e8.1.1.1 \u003ca href=\"https://image-net.org/\" rel=\"nofollow\"\u003eImageNet\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.1.1.2 \u003ca href=\"http://yann.lecun.com/exdb/mnist/\" rel=\"nofollow\"\u003eMNIST\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.1.1.3 \u003ca href=\"https://cocodataset.org/\" rel=\"nofollow\"\u003eCOCO\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.1.1.4 \u003ca href=\"http://www.cs.toronto.edu/~kriz/cifar.html\" rel=\"nofollow\"\u003eCIFAR-10\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e8.1.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#812-pytorch%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E8%AF%BB%E5%8F%96%E6%96%B9%E6%B3%95%E7%AE%80%E4%BB%8B\"\u003ePytorch数据集及读取方法简介\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.1.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#813-%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA%E7%AE%80%E4%BB%8B\"\u003e数据增强简介\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#%E6%80%BB%E7%BB%93\"\u003e总结\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e8.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.2_%E5%9F%BA%E5%87%86BenchMark.md\"\u003e基准测试\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/chapter8.3_%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87.md\"\u003e评价指标\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.12%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%EF%BC%88CIFAR-10%EF%BC%89.md\"\u003e实战项目 6 - Kaggle比赛：图像分类（CIFAR-10）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.13%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E7%8B%97%E7%9A%84%E5%93%81%E7%A7%8D%E8%AF%86%E5%88%AB%EF%BC%88ImageNet%20Dogs%EF%BC%89.md\"\u003e实战项目 7 - Kaggle比赛：狗的品种识别（ImageNet Dogs）\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 9 章 \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/chapter9/chapter9\" rel=\"nofollow\"\u003e检测与分割实战项目\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e9.1 语义分割\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e9.1.1 \u003ca href=\"https://github.com/Charmve/Semantic-Segmentation-PyTorch\"\u003e语义分割 PyTorch 版\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e9.1.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e实战项目 8 - 基于PolarNet的点云端到端语义分割项目实战\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e9.2 目标检测\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e9.2.1 常用网络\u003c/li\u003e\n\u003cli\u003e9.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e实战项目 9 - 基于PyTorch的YOLO5目标检测项目实战\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e9.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md\"\u003e实例分割\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e9.3.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md#931-%E5%B8%B8%E7%94%A8%E7%BD%91%E7%BB%9C\"\u003e常用网络\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e9.3.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md#932-%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE-8-%E5%AE%9E%E6%97%B6%E9%AB%98%E5%88%86%E8%BE%A8%E7%8E%87%E8%83%8C%E6%99%AF%E6%8A%A0%E5%9B%BE\"\u003e实战项目 10 - 实时高分辨率背景抠图\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e9.3.3 新方法：\u003ca href=\"https://blog.csdn.net/Charmve/article/details/108915225\" rel=\"nofollow\"\u003e滑动窗口\u003c/a\u003e, \u003ca href=\"https://blog.csdn.net/Charmve/article/details/108892076\" rel=\"nofollow\"\u003ePointRend\u003c/a\u003e, \u003ca href=\"https://github.com/xieenze/PolarMask\"\u003ePolarMask\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 10 章 \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/chapter10/chapter10\" rel=\"nofollow\"\u003e计算机视觉课题研究初探\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e10.1 \u003ca href=\"https://blog.csdn.net/Charmve/article/details/108531735\" rel=\"nofollow\"\u003e手写字识别\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e10.2 \u003ca href=\"https://github.com/Charmve/Scene-Text-Detection\"\u003e文本检测\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e10.3 \u003ca href=\"https://github.com/Charmve/Awesome-Lane-Detection\"\u003e车道线检测\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e10.3.1 \u003ca href=\"https://github.com/Charmve/Awesome-Lane-Detection\"\u003e常用网络\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e10.3.2 \u003ca href=\"https://blog.csdn.net/Charmve/article/details/116678477\" rel=\"nofollow\"\u003e实战项目 11 - 车道线检测项目实战\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e10.4 \u003ca href=\"https://github.com/Charmve/Mirror-Glass-Detection\"\u003e镜面检测\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e10.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter10_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AF%BE%E9%A2%98%E7%A0%94%E7%A9%B6%E5%88%9D%E6%8E%A2/charpter10_5-%E5%9B%BE%E5%83%8F%E6%8A%A0%E5%9B%BE.md\"\u003e图像抠图 Matting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e10.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter10_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AF%BE%E9%A2%98%E7%A0%94%E7%A9%B6%E5%88%9D%E6%8E%A2/charpter10_6-%E5%9B%BE%E5%83%8F%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87.md\"\u003e图像超分辨率\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e10.7 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter10_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AF%BE%E9%A2%98%E7%A0%94%E7%A9%B6%E5%88%9D%E6%8E%A2/charpter10_7-3D%E9%87%8D%E5%BB%BA.md\"\u003e3D 重建\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e进阶篇\u003c/h4\u003e\u003ca id=\"user-content-进阶篇\" class=\"anchor\" aria-label=\"Permalink: 进阶篇\" href=\"#进阶篇\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e第 11 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3\"\u003e可视化和理解卷积神经网络\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e11.1 特征可视化\u003c/li\u003e\n\u003cli\u003e11.2 倒置\u003c/li\u003e\n\u003cli\u003e11.3 可视化数据梯度\u003c/li\u003e\n\u003cli\u003e11.4 \u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter13_Understanding-and-Visualizing/Embeddings.ipynb\" rel=\"nofollow\"\u003eEmbeddings\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.5 对抗样本\u003c/li\u003e\n\u003cli\u003e11.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3.3_neural-style.md\"\u003eDeepDream 和风格迁移\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.7 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md\"\u003e实战项目 12: PyTorch 如何使用TensorBoard\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e11.4.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1141-%E5%88%9B%E5%BB%BA-tensorboard\"\u003e创建 TensorBoard\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.4.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1142-%E5%86%99%E5%85%A5-tensorboard\"\u003e写入 TensorBoard\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.4.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1143-%E4%BD%BF%E7%94%A8-tensorboard-%E6%A3%80%E6%9F%A5%E6%A8%A1%E5%9E%8B\"\u003e使用 TensorBoard 检查模型\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.4.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1144-%E5%90%91-tensorboard-%E6%B7%BB%E5%8A%A0-projector\"\u003e向 TensorBoard 添加 \"Projector\"\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.4.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1145-%E4%BD%BF%E7%94%A8-tensorboard-%E8%B7%9F%E8%B8%AA%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83\"\u003e使用 TensorBoard 跟踪模型训练\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.4.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1146-%E4%BD%BF%E7%94%A8-tensorboard-%E8%AF%84%E4%BC%B0%E8%AE%AD%E7%BB%83%E5%A5%BD%E7%9A%84%E6%A8%A1%E5%9E%8B\"\u003e使用 TensorBoard 评估训练好的模型\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e11.4.7 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#%E5%B0%8F%E7%BB%93\"\u003e案例总结\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 12 章 \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/chapter6/chapter6\" rel=\"nofollow\"\u003e生成对抗模型\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e12.1 Pixel RNN/CNN\u003c/li\u003e\n\u003cli\u003e12.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_2-%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8Auto-encoder.md\"\u003e自编码器 Auto-encoder\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md\"\u003e生成对抗网络 GAN\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e12.3.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1231-%E6%A6%82%E8%BF%B0\"\u003e概述\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.3.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1232-gan%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%80%9D%E6%83%B3\"\u003eGAN的基本思想\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.3.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1233-gan%E6%B5%85%E6%9E%90\"\u003eGAN浅析\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e12.3.3.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#12331-gan%E7%9A%84%E5%9F%BA%E6%9C%AC%E7%BB%93%E6%9E%84\"\u003eGAN的基本结构\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.3.3.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#12332-gan%E7%9A%84%E8%AE%AD%E7%BB%83%E6%96%B9%E5%BC%8F\"\u003eGAN的训练方式\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%85%B3%E4%BA%8E%E7%94%9F%E6%88%90%E5%99%A8\"\u003e关于生成器\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%85%B3%E4%BA%8E%E5%88%A4%E5%88%AB%E5%99%A8\"\u003e关于判别器\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%A6%82%E4%BD%95%E8%AE%AD%E7%BB%83\"\u003e如何训练\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e12.3.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1234%E8%AE%AD%E7%BB%83%E7%9B%B8%E5%85%B3%E7%90%86%E8%AE%BA%E5%9F%BA%E7%A1%80\"\u003e训练相关理论基础\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.3.5 项目实战案例StyleGAN\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Charmve/VOGUE-Try-On\"\u003eStyleGAN\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://blog.csdn.net/Charmve/article/details/115315353\" rel=\"nofollow\"\u003eStyleGAN 2.0\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e12.3.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3.3_neural-style.md\"\u003e实战项目11 - 图像风格迁移\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%B0%8F%E7%BB%93\"\u003e小结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e12.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md\"\u003e变分自编码器 Variational Auto-encoder, VAE\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e12.4.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1241-%E6%A6%82%E8%BF%B0\"\u003e概述\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.4.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1242-%E5%9F%BA%E6%9C%AC%E5%8E%9F%E7%90%86\"\u003e基本原理\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e12.4.2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1-%E5%AE%9A%E4%B9%89\"\u003e定义\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.4.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#2-%E7%90%86%E8%AE%BA%E5%9F%BA%E7%A1%80%E4%B8%89%E8%A6%81%E7%B4%A0\"\u003e理论基础：三要素\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.4.2.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#3-%E6%8E%A8%E5%AF%BC%E8%BF%87%E7%A8%8B\"\u003e推导过程\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e12.4.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1243-vae-vs-ae-%E5%8C%BA%E5%88%AB%E4%B8%8E%E8%81%94%E7%B3%BB\"\u003eVAE v.s. AE 区别与联系\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.4.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1244-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8%E7%9A%84%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0\"\u003e变分自编码器的代码实现\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.4.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1245-%E5%8D%B7%E7%A7%AF%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8%E7%9A%84%E5%AE%9E%E7%8E%B0%E4%B8%8E%E7%AE%80%E5%8D%95%E5%BA%94%E7%94%A8\"\u003e卷积变分自编码器的实现与简单应用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e12.4.6 \u003ca href=\"https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life\"\u003e实战项目 13 - 旧照片修复\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 13 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md\"\u003e深度增强学习\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e13.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#141-%E5%BC%95%E8%A8%80-%E5%A6%82%E4%BD%95%E8%A7%A3%E5%86%B3%E9%80%9A%E7%94%A8%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E7%9A%84%E9%9A%BE%E7%82%B9\"\u003e引言-如何解决通用人工智能的难点\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e13.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#142-%E4%BB%80%E4%B9%88%E6%98%AF%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0\"\u003e什么是深度增强学习\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e13.2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1421-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0\"\u003e深度学习\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e13.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1422-%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0\"\u003e增强学习\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e13.2.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1423-%E4%BA%8C%E8%80%85%E7%9A%84%E8%9E%8D%E5%90%88\"\u003e二者的融合\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e13.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#143-%E6%80%8E%E4%B9%88%E5%88%A9%E7%94%A8%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0%E8%A7%A3%E5%86%B3%E9%97%AE%E9%A2%98\"\u003e怎么利用深度增强学习解决问题\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e13.3.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1431-policy-based-drl\"\u003ePolicy-based DRL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e13.3.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1432-value-based-drl\"\u003eValue-based DRL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e13.3.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1433-model-based-drl\"\u003eModel-based DRL\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e13.4 \u003ca href=\"#144-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0%E5%9C%A8%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E4%B8%AD%E7%9A%84%E5%BA%94%E7%94%A8\"\u003e深度增强学习在计算机视觉中的应用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#%E5%B0%8F%E7%BB%93\"\u003e小结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 14 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md\"\u003e视频理解\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e14.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#141-%E6%A6%82%E8%BF%B0\"\u003e概述\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e14.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#142-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3%E5%9C%BA%E6%99%AF%E4%B8%AD%E7%9A%84%E4%B8%BB%E8%A6%81%E9%97%AE%E9%A2%98\"\u003e视频理解场景中的主要问题\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e14.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#143-%E5%B8%B8%E7%94%A8%E6%95%B0%E6%8D%AE%E9%9B%86\"\u003e常用数据集\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e14.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#144-%E4%B8%BB%E6%B5%81%E6%96%B9%E6%B3%95%E4%B8%8E%E6%A8%A1%E5%9E%8B%E6%9E%B6%E6%9E%84\"\u003e主流方法与模型架构\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e14.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#145-%E6%8C%87%E6%A0%87-metrics\"\u003e指标 METRICS\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e14.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#146-%E5%8F%AF%E8%83%BD%E7%9A%84%E6%9C%AA%E6%9D%A5%E6%96%B9%E5%90%91\"\u003e可能的未来方向\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#%E5%B0%8F%E7%BB%93\"\u003e小结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 15 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0\"\u003e迁移学习\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e15.1 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#151-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0\"\u003e概述\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e15.1.1 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#1511-%E8%83%8C%E6%99%AF\"\u003e背景\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.1.2 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#1512-%E5%AE%9A%E4%B9%89%E5%8F%8A%E5%88%86%E7%B1%BB\"\u003e定义及分类\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.1.3 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#1513-%E5%85%B3%E9%94%AE%E7%82%B9\"\u003e关键点\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e15.2 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#152-%E5%9F%BA%E4%BA%8E%E5%AE%9E%E4%BE%8B%E7%9A%84%E8%BF%81%E7%A7%BB\"\u003e基于实例的迁移\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.3 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#153-%E5%9F%BA%E4%BA%8E%E7%89%B9%E5%BE%81%E7%9A%84%E8%BF%81%E7%A7%BB\"\u003e基于特征的迁移\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.4 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#154-%E5%9F%BA%E4%BA%8E%E5%85%B1%E4%BA%AB%E5%8F%82%E6%95%B0%E7%9A%84%E8%BF%81%E7%A7%BB\"\u003e基于共享参数的迁移\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.5 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#155-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%92%8C%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%BB%93%E5%90%88\"\u003e深度学习和迁移学习结合\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.7 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md\"\u003e实战项目 14 - 蚂蚁和蜜蜂的分类问题\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e15.7.1 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md#1571-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E5%9C%A8%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E9%A2%86%E5%9F%9F%E7%9A%84%E5%BA%94%E7%94%A8\"\u003e迁移学习在计算机视觉领域的应用\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e15.7.2 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md#1572-%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE-%E8%9A%82%E8%9A%81%E5%92%8C%E8%9C%9C%E8%9C%82%E7%9A%84%E5%88%86%E7%B1%BB%E9%97%AE%E9%A2%98\"\u003e实战项目: 蚂蚁和蜜蜂的分类问题\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E5%B0%8F%E7%BB%93\"\u003e小结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 16 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/notebooks/chapter16_Attention/1_Attention.ipynb\"\u003e计算机视觉中的注意力机制 Attention is All You Need\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e16.1 概述\u003c/li\u003e\n\u003cli\u003e16.2 Attention with RNNs\u003c/li\u003e\n\u003cli\u003e16.3 \u003ca href=\"https://mp.weixin.qq.com/s/nUd7YtCci1_AwQ4nOwK9bA\" rel=\"nofollow\"\u003eSelf-attention 自注意力\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e16.4 软注意力（soft-attention）\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e16.4.1 空间域注意力\u003c/li\u003e\n\u003cli\u003e16.4.2 通道注意力\u003c/li\u003e\n\u003cli\u003e16.4.3 Positional encoding\u003c/li\u003e\n\u003cli\u003e16.4.4 混合域模型\u003c/li\u003e\n\u003cli\u003e16.4.5 Masked attention\u003c/li\u003e\n\u003cli\u003e16.4.6 Multi-head attention\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e16.5 强注意力（hard attention）\u003c/li\u003e\n\u003cli\u003e16.6 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter16-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6%20Attention%20is%20All%20You%20Need/chapter16_Attention-is-All-You-Need.md\"\u003eAttention九层塔 - 注意力机制的九重理解\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e参考文献\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 17 章 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md\"\u003e跨界模型 Transformer\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e17.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%B8%80%E6%80%9D%E6%83%B3%E5%92%8C%E6%A1%86%E5%9B%BE\"\u003e思想和框图\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e17.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%BA%8C%E5%AE%9E%E7%8E%B0%E7%BB%86%E8%8A%82\"\u003e实现细节\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-1-Encoder\"\u003e17.2.1 Encoder\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-2-Decoder\"\u003e17.2.2 Decoder\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-3-Self-Attention\"\u003e17.2.3 Self-Attention\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-4-Multi-Headed-Attention\"\u003e17.2.4 Multi-Headed Attention\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-5-Positional-Encoding\"\u003e17.2.5 Positional Encoding\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e17.3 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%B8%89-%E5%BA%94%E7%94%A8%E4%BB%BB%E5%8A%A1%E5%92%8C%E7%BB%93%E6%9E%9C\"\u003e应用任务和结果\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e17.3.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-1-NLP%E9%A2%86%E5%9F%9F\"\u003eNLP领域\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e17.3.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-CV%E9%A2%86%E5%9F%9F\"\u003eCV领域\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e17.3.2.1 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-1-%E6%A3%80%E6%B5%8BDETR\"\u003e检测DETR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e17.3.2.2 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-2-%E5%88%86%E7%B1%BBViT\"\u003e分类ViT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e17.3.2.3 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-3-%E5%88%86%E5%89%B2SETR\"\u003e分割SETR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e17.3.2.4 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-4-Deformable-DETR\"\u003eDeformable-DETR\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e17.4 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E5%9B%9B-%E4%BC%98%E7%82%B9%E5%8F%8A%E5%88%86%E6%9E%90\"\u003e优点及分析\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e17.5 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%BA%94-%E7%BC%BA%E7%82%B9%E5%8F%8A%E5%88%86%E6%9E%90\"\u003e缺点及分析\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://blog.csdn.net/Charmve/article/details/115339803\" rel=\"nofollow\"\u003e实战项目 15 - 基于Transformer的视频实例分割网络VisTR (CVPR2021)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E5%85%AD-%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 18 章 \u003ca href=\"https://mp.weixin.qq.com/s/e3c_-rs2rncmWhbm-cU5rA\" rel=\"nofollow\"\u003e知识蒸馏\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e18.1 概要\u003c/li\u003e\n\u003cli\u003e18.2 KD主要方法\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e18.2.1 Logits(Response)-based Knowledge\u003c/li\u003e\n\u003cli\u003e18.2.2 Feature-based Knowledge\u003c/li\u003e\n\u003cli\u003e18.2.3 Relation-based Knowledge\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e18.3 知识蒸馏的应用 NLP-BERT\u003c/li\u003e\n\u003cli\u003e18.4 常见疑问解答\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://mp.weixin.qq.com/s/RTkBQJ7Uj86Wxt7HmwWKzA\" rel=\"nofollow\"\u003e实战项目 16 - 支付宝CVPR细粒度视觉分类挑战赛夺冠方案解读\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 19 章 \u003ca href=\"https://blog.csdn.net/Charmve/article/details/107650487\" rel=\"nofollow\"\u003eNormalization 模型\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e19.1 从Mini-Batch SGD说起\u003c/li\u003e\n\u003cli\u003e19.2 Normalization到底是在做什么\u003c/li\u003e\n\u003cli\u003e19.3 Batch Normalization如何做\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e19.3.1 前向神经网络中的BN\u003c/li\u003e\n\u003cli\u003e19.3.2 CNN网络中的BN\u003c/li\u003e\n\u003cli\u003e19.3.3 Batch Norm的四大罪状\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e19.4 Layer Normalization、Instance Normalization及Group Normalization\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e19.4.1 Layer Normalization\u003c/li\u003e\n\u003cli\u003e19.4.2 Instance Normalization\u003c/li\u003e\n\u003cli\u003e19.4.3 Group Normalization\u003c/li\u003e\n\u003cli\u003e19.4.4 用一个故事来总结\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e19.5 Normalization操作的Re-Scaling不变性\u003c/li\u003e\n\u003cli\u003e19.6 Batch Normalization为何有效\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e第 20 章 \u003ca href=\"https://mp.weixin.qq.com/s/e3c_-rs2rncmWhbm-cU5rA\" rel=\"nofollow\"\u003e模型压缩与裁剪\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e20.1 概述\u003c/li\u003e\n\u003cli\u003e20.2 模型压缩\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e20.2.1 线性或非线性量化（1990~2014 - 至今）\u003c/li\u003e\n\u003cli\u003e20.2.2 结构或非结构剪枝（1989~2014 - 至今\u003c/li\u003e\n\u003cli\u003e20.2.3 网络结构搜索（2016 - 至今）\u003c/li\u003e\n\u003cli\u003e20.2.4 权重矩阵的低秩分解（）\u003c/li\u003e\n\u003cli\u003e20.2.5 知识蒸馏（2014-至今）\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e20.3 模型优化加速\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e20.3.1 Op-Level 的快速算法\u003c/li\u003e\n\u003cli\u003e20.3.2 Layer0-level 的快速算法\u003c/li\u003e\n\u003cli\u003e20.3.3 硬件计算单元优化算法\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCPU、GPU和NPU\u003c/li\u003e\n\u003cli\u003eASIC 和 FPGA\u003c/li\u003e\n\u003cli\u003ePIM（NDP）\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e20.4 优化工具与库\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e20.4.1 TensorRT Nvidia)\u003c/li\u003e\n\u003cli\u003e20.4.2 TVM (Tensor Virtual Machine)\u003c/li\u003e\n\u003cli\u003e20.4.3 Tensor Comprehension (Facebook)\u003c/li\u003e\n\u003cli\u003e20.4.4 Distiller (Intel)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e小结\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#%E5%85%AD-%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE\"\u003e参考文献\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e附录\u003c/h4\u003e\u003ca id=\"user-content-附录\" class=\"anchor\" aria-label=\"Permalink: 附录\" href=\"#附录\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eA 矩阵\u003c/li\u003e\n\u003cli\u003eB \u003ca href=\"https://mp.weixin.qq.com/s?__biz=MzIxMjg1Njc3Mw==\u0026amp;mid=2247484495\u0026amp;idx=1\u0026amp;sn=0bbb2094d93169baf20eedb284bc668f\" rel=\"nofollow\"\u003e常用激活函数总结\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eC \u003ca href=\"https://blog.csdn.net/Charmve/article/details/106089198\" rel=\"nofollow\"\u003e梯度下降法\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eD \u003ca href=\"https://blog.csdn.net/Charmve/article/details/107650479\" rel=\"nofollow\"\u003e深度学习调参技巧总结\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/book_postscript.md\"\u003e后记\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-后记\" class=\"anchor\" aria-label=\"Permalink: 后记\" href=\"#后记\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\u003c/li\u003e\n\u003cli\u003e\u003cb\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ca href=\"#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE-1\"\u003e参考文献\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-参考文献\" class=\"anchor\" aria-label=\"Permalink: 参考文献\" href=\"#参考文献\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\u003c/b\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n- \u003cb\u003e更新中 ...\u003c/b\u003e\n\u003cp dir=\"auto\"\u003e\u003cbr\u003e\u003ca href=\"#-%E6%9C%AC%E4%B9%A6%E7%9B%AE%E5%BD%95\"\u003e收起\u003c/a\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\n\u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/details\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔍 文件浏览\u003c/h2\u003e\u003ca id=\"user-content--文件浏览\" class=\"anchor\" aria-label=\"Permalink: 🔍 文件浏览\" href=\"#-文件浏览\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e按书中内容先后顺序逐章阅读，或者选取特定章节祥读 📁 \u003ccode\u003edocs/\u003c/code\u003e \u003csup\u003e1\u003c/sup\u003e，动手实践章节代码，在代码文件 📁 \u003ccode\u003ecode/\u003c/code\u003e \u003csup\u003e2\u003c/sup\u003e 下找到对应代码，本地测试或者Colab 📁 \u003ccode\u003enotebooks/\u003c/code\u003e \u003csup\u003e3\u003c/sup\u003e 在线测试。\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e📁 \u003ccode\u003eL0CV/\u003c/code\u003e - 专为本项目建立的 \u003ccode\u003e💮 L0CV\u003c/code\u003e 包\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003ecode/\u003c/code\u003e - 书中完整代码\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003edatasets/\u003c/code\u003e - 本书所用数据集\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e📁 \u003ccode\u003eimages/\u003c/code\u003e - 经典图像处理图片\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003edocs/\u003c/code\u003e - 全书按照一下几个篇目进行编写\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e📁 \u003ccode\u003e0_绪论/\u003c/code\u003e - 全书绪论\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003e1_理论篇/\u003c/code\u003e - 基本理论和算法基础\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003e2_实战篇/\u003c/code\u003e - 项目实战教程\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003e3_进阶篇/\u003c/code\u003e - 最新CV模型和算法\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003e附件/\u003c/code\u003e - 所需数学和统计学知识\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003eimg/\u003c/code\u003e - 各章节所使用的插图\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003emodels/\u003c/code\u003e - 封装可用的预训练模型\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003enotebooks/\u003c/code\u003e - 🚩 全书 Colab notebook，可在线测试 \u003ca href=\"https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter01_neural-networks/\u003c/code\u003e - 第 1 章 - 神经网络 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter02_CNN/\u003c/code\u003e - 第 2 章 - 卷积神经网络 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter03_Image-Classification\u003c/code\u003e - 第 3 章 - 图像分类 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter04_recurrent-neural-networks/\u003c/code\u003e - 第 4 章 - 递归神经网络 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter05_graph-neural-network/\u003c/code\u003e - 第 5 章 - 图神经网络 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter07_optimization/\u003c/code\u003e - 第 6 章 - 模型拟合与优化算法 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter08_environment-setup-and-tool-use/\u003c/code\u003e - 第 7 章 - 软件环境搭建与工具使用 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter09_convolutional-neural-networks/\u003c/code\u003e - 第 8 章 - 经典神经网络架构：原理与PyTorch实现 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter12_practice-projects\u003c/code\u003e - 第 12 章 - 计算机视觉课题研究初探 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter13_Understanding-and-Visualizing/\u003c/code\u003e - 第 13 章 - 可视化与理解卷积神经网络 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter14_GAN/\u003c/code\u003e - 第 14 章 - 生成对抗模型 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter15_Transfer-Learning/\u003c/code\u003e - 第 15 章 - 迁移学习 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Charmve/computer-vision-in-action#-以用促学先会后懂-\"\u003e\u003cimg align=\"right\" alt=\"Go for it!\" src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/dd292873828228a753a9bd2de4576dbf8cc3902c/res/ui/footer-rocket.svg\" height=\"260\" title=\"Do what you like, and do it best!\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter16_Attention/\u003c/code\u003e - 第 16 章 - 注意力机制 Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003echapter17_Transformers/\u003c/code\u003e - 第 17 章 - Transformers Jupyter Notebook 实现\u003c/li\u003e\n\u003cli\u003e...\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003eimgs/\u003c/code\u003e - Jupyter Notebook 中用到的图片\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003edocker/\u003c/code\u003e - 为降低读者的学习成本，目前未进行搭建\u003c/li\u003e\n\u003cli\u003e📁 \u003ccode\u003eres/\u003c/code\u003e - ui 图片及全书思维导图PDF\u003c/li\u003e\n\u003cli\u003e📄 \u003ccode\u003eREADME.md\u003c/code\u003e - 全书介绍及目录\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eL0CV DemoDay\u003c/h2\u003e\u003ca id=\"user-content-l0cv-demoday\" class=\"anchor\" aria-label=\"Permalink: L0CV DemoDay\" href=\"#l0cv-demoday\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/Charmve/computer-vision-in-action/blob/main/res/ui/demoday.png\"\u003e\u003cimg src=\"/Charmve/computer-vision-in-action/raw/main/res/ui/demoday.png\" title=\"L0CV Demo Day\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/L0CV-Universe\"\u003eL0CV-Universe\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e如果你也是从这里出发，在开源的项目中应用进去，并在标题下给出引用 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/1e1e70b4dc201de93f26a93c1e892ba23c641a273e17fac7ed9b021737c9ac70/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2df09f92ae202532304c3043562d6c69676874677265656e2e737667\" alt=\"L0CV\" title=\"L0CV\" data-canonical-src=\"https://img.shields.io/badge/-💮 %20L0CV-lightgreen.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e，您的项目将会在这里展现！\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 align=\"center\" tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ci\u003e以用促学，先会后懂。理解深度学习的最佳方法是学以致用。\u003c/i\u003e\u003c/h5\u003e\u003ca id=\"user-content-以用促学先会后懂理解深度学习的最佳方法是学以致用\" class=\"anchor\" aria-label=\"Permalink: 以用促学，先会后懂。理解深度学习的最佳方法是学以致用。\" href=\"#以用促学先会后懂理解深度学习的最佳方法是学以致用\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter02_CNN/Pytorch_MNIST.ipynb\" rel=\"nofollow\"\u003e\u003cimg width=\"%40\" src=\"https://user-images.githubusercontent.com/29084184/128386334-e0273125-5d51-4e33-a6e7-f2e732fb0836.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/blob/main/docs/2__实战篇/chapter7_经典卷积神经网络架构-原理与PyTorch实现/7.12%20实战Kaggle比赛：图像分类（CIFAR-10）.md\"\u003e\u003cimg width=\"%40\" src=\"https://user-images.githubusercontent.com/29084184/128386363-dc0c987c-b374-4e43-9e56-65c30f7a1899.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/blob/main/docs/2_实战篇/chapter7_经典卷积神经网络架构-原理与PyTorch实现/7.13%20实战Kaggle比赛：狗的品种识别（ImageNet%20Dogs）.md\"\u003e\u003cimg width=\"%40\" src=\"https://user-images.githubusercontent.com/29084184/128386468-ca555572-a98d-44c5-bdef-442371322ee7.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Bringing-Old-Photo-Back-to-Life.ipynb\" rel=\"nofollow\"\u003e\u003cimg width=\"%40\" src=\"https://user-images.githubusercontent.com/29084184/128386504-25a9f798-be68-430d-b771-bd8db607c151.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://github.com/Charmve/Awesome-Lane-Detection/tree/main/lane-detector\"\u003e\u003cimg width=\"%40\" src=\"https://user-images.githubusercontent.com/29084184/128386530-d64210f0-d903-4004-9f6c-eb480d326241.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/blob/main/docs/3_进阶篇/chapter12-生成对抗模型/chapter12.3.3_neural-style.md\"\u003e\u003cimg width=\"%40\" src=\"https://user-images.githubusercontent.com/29084184/128386405-4223b171-a318-4f76-93b3-0fff016aa39f.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e*《计算机视觉实战演练：算法与应用》V1.2 \u003cem\u003e部分项目还在更新中\u003c/em\u003e\u003c/p\u003e\n\u003cbr\u003e\n\u003cp align=\"left\" dir=\"auto\"\u003e\n \u003ca href=\"https://github.com/Charmve/computer-vision-in-action\"\u003e\u003cimg src=\"/Charmve/computer-vision-in-action/raw/main/res/ui/workswith1.png\" title=\"Works with L0CV\" width=\"120\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e实战项目\u003c/th\u003e\n\u003cth align=\"left\"\u003e章节\u003c/th\u003e\n\u003cth align=\"center\"\u003eBinder\u003c/th\u003e\n\u003cth align=\"center\"\u003eGoogle Colab\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://blog.csdn.net/Charmve/article/details/108531735\" rel=\"nofollow\"\u003e实战项目 1 - 手写字分类\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 1 章 - 神经网络\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md\"\u003e实战项目 2 - 动手搭建一个卷积神经网络\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 2 章 - 卷积神经网络\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://blog.csdn.net/charmve/category_9754344.html\" rel=\"nofollow\"\u003e实战项目 3 - 基于卷积神经网络的人脸表情识别\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 3 章 - 图像分类\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"http://mp.weixin.qq.com/s?__biz=MzIxMjg1Njc3Mw%3D%3D\u0026amp;chksm=97bef597a0c97c813e185e1bbf987b93d496c6ead8371364fd175d9bac46e6dcf7059cf81cb2\u0026amp;idx=1\u0026amp;mid=2247487293\u0026amp;scene=21\u0026amp;sn=89684d1c107177983dc1b4dca8c20a5b#wechat_redirect\" rel=\"nofollow\"\u003e实战项目 4 - 使用卷积神经网络对CIFAR10图片进行分类\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 3 章 - 图像分类\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://blog.csdn.net/Charmve/article/details/107897468\" rel=\"nofollow\"\u003e实战项目 5 - 使用OpenCV进行图像全景拼接\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 6 章 - 软件环境搭建与工具使用\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/OpenCV-ImageStitching.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/OpenCV-ImageStitching.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.12%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%EF%BC%88CIFAR-10%EF%BC%89.md\"\u003e实战项目 6 - Kaggle比赛：图像分类（CIFAR-10）\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 8 章 - 著名数据集及基准\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_cifar10.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_cifar10.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.13%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E7%8B%97%E7%9A%84%E5%93%81%E7%A7%8D%E8%AF%86%E5%88%AB%EF%BC%88ImageNet%20Dogs%EF%BC%89.md\"\u003e实战项目 7 - Kaggle比赛：狗的品种识别（ImageNet Dogs）\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 8 章 - 著名数据集及基准\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_dog.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_dog.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e实战项目 8 - 基于PolarNet的点云端到端语义分割项目实战\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 9 章 - 检测与分割实战项目\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e实战项目 9 - 基于PyTorch的YOLO5目标检测项目实战\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 9 章 - 检测与分割实战项目\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md#932-%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE-8-%E5%AE%9E%E6%97%B6%E9%AB%98%E5%88%86%E8%BE%A8%E7%8E%87%E8%83%8C%E6%99%AF%E6%8A%A0%E5%9B%BE\"\u003e实战项目 10 - 实时高分辨率背景抠图\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 9 章 - 检测与分割实战项目\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://blog.csdn.net/Charmve/article/details/116678477\" rel=\"nofollow\"\u003e实战项目 11 - 车道线检测项目实战\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 10 章 - 计算机视觉课题研究初探\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md\"\u003e实战项目 12 - PyTorch 如何使用TensorBoard\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 13 章 - 可视化和理解\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"/Charmve/computer-vision-in-action/blob/main\"\u003e实战项目 13 - 图像样式迁移\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 14 章 生成对抗模型\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb#/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life\"\u003e实战项目 14 - 旧照片修复\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 14 章 - 生成对抗模型\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Bringing-Old-Photo-Back-to-Life.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life\"\u003e实战项目 15 - 动漫头像生成\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 14 章 - 生成对抗模型\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Anime-StyleGAN2.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3?id=_147-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3%E9%A1%B9%E7%9B%AE%E5%AE%9E%E6%88%98\" rel=\"nofollow\"\u003e项目实战 16 - 视频理解项目实战 SlowFast + Multi-Moments in Time\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 16 章 - 视频理解\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Bringing-Old-Photo-Back-to-Life.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md\"\u003e实战项目 17 - 蚂蚁和蜜蜂的分类问题\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 17 章 - 迁移学习\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter15_Transfer-Learning/TL-ants-bees-classification.ipynb#/\" rel=\"nofollow\"\u003e \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://blog.csdn.net/Charmve/article/details/115339803\" rel=\"nofollow\"\u003e实战项目 18 - 基于Transformer的视频实例分割网络VisTR (CVPR2021)\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 19 章 - 跨界模型 Transformer\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u003ca href=\"https://mp.weixin.qq.com/s/RTkBQJ7Uj86Wxt7HmwWKzA\" rel=\"nofollow\"\u003e实战项目 19 - 支付宝CVPR细粒度视觉分类挑战赛夺冠方案解读\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"left\"\u003e第 20 章 - 知识蒸馏\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e...\u003c/td\u003e\n\u003ctd align=\"left\"\u003e...\u003c/td\u003e\n\u003ctd align=\"center\"\u003e...\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔑 如何食用\u003c/h2\u003e\u003ca id=\"user-content--如何食用\" class=\"anchor\" aria-label=\"Permalink: 🔑 如何食用\" href=\"#-如何食用\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdetails\u003e\u003csummary\u003e🔎 详细攻略展开\u003c/summary\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e方式一 Jupyter Notebook (推荐方式 ✨)\u003c/h3\u003e\u003ca id=\"user-content-方式一-jupyter-notebook-推荐方式-\" class=\"anchor\" aria-label=\"Permalink: 方式一 Jupyter Notebook (推荐方式 ✨)\" href=\"#方式一-jupyter-notebook-推荐方式-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. 本地运行\u003c/h4\u003e\u003ca id=\"user-content-1-本地运行\" class=\"anchor\" aria-label=\"Permalink: 1. 本地运行\" href=\"#1-本地运行\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e依赖包安装\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip3 install -r requirements.txt\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip3 install -r requirements.txt\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e安装 Jupyter\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python3 -m pip install --upgrade pip\npython3 -m pip install jupyter\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython3 -m pip install --upgrade pip\npython3 -m pip install jupyter\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e查看并运行jupyter\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e请在终端（Mac / Linux）或命令提示符（Windows）上运行以下命令：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd notebooks\njupyter notesbook\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e notebooks\njupyter notesbook\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. 远程运行\u003c/h4\u003e\u003ca id=\"user-content-2-远程运行\" class=\"anchor\" aria-label=\"Permalink: 2. 远程运行\" href=\"#2-远程运行\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e打开每章节首页，点击 \u003ca href=\"https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e 可直接打开 Google Colab ，点击 \u003ccode\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png\"\u003e\u003cimg height=\"20\" src=\"https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png\" alt=\"Copy to Drive\" title=\"Copy to Drive\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/code\u003e [Copy to Drive] 即可在线运行测试。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e点击 \u003ca href=\"https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e 也可在 \u003ccode\u003emybinder\u003c/code\u003e 查看和在线运行。\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/29084184/126031057-1e6ca67f-4475-47c1-a6ff-66375cb86908.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/29084184/126031057-1e6ca67f-4475-47c1-a6ff-66375cb86908.png\" width=\"60%\" alt=\"Run on Colab\" title=\"Run on Colab\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\n 图2 例子：12.3.3 样式迁移\n\u003c/p\u003e \n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/29084184/126031137-14e349cd-1e89-4f98-9c56-0f1d3007ed89.png\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/29084184/126031137-14e349cd-1e89-4f98-9c56-0f1d3007ed89.png\" width=\"60%\" alt=\"点击 Copy to Drive\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e图3 例子：12.3.3 样式迁移 Colab 点击 \u003ccode\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png\"\u003e\u003cimg height=\"20\" src=\"https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png\" alt=\"Copy to Drive\" title=\"Copy to Drive\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/code\u003e [Copy to Drive]\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e方式二 使用 \u003ccode\u003e/code\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-方式二-使用-code\" class=\"anchor\" aria-label=\"Permalink: 方式二 使用 /code\" href=\"#方式二-使用-code\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. 运行环境 + L0CV 加载\u003c/h4\u003e\u003ca id=\"user-content-1-运行环境--l0cv-加载\" class=\"anchor\" aria-label=\"Permalink: 1. 运行环境 + L0CV 加载\" href=\"#1-运行环境--l0cv-加载\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e依赖包安装\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"sudo apt-get update\npip3 install -r requirements.txt\"\u003e\u003cpre\u003esudo apt-get update\npip3 install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e创建 L0CV\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python3 setup.py\"\u003e\u003cpre\u003epython3 setup.py\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e测试环境\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd code\npython3 L0CV_test.py\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e code\npython3 L0CV_test.py\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. 直接调用每个章节的代码测试\u003c/h4\u003e\u003ca id=\"user-content-2-直接调用每个章节的代码测试\" class=\"anchor\" aria-label=\"Permalink: 2. 直接调用每个章节的代码测试\" href=\"#2-直接调用每个章节的代码测试\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import L0CV\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eL0CV\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E5%A6%82%E4%BD%95%E9%A3%9F%E7%94%A8\"\u003e收起\u003c/a\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"1\" rowspan=\"1\"\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e每一小节都是可以运行的 \u003ca href=\"https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/\" rel=\"nofollow\"\u003eJupyter 记事本\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-每一小节都是可以运行的-jupyter-记事本\" class=\"anchor\" aria-label=\"Permalink: 每一小节都是可以运行的 Jupyter 记事本\" href=\"#每一小节都是可以运行的-jupyter-记事本\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n \u003cp dir=\"auto\"\u003e你可以自由修改代码和超参数来获取及时反馈，从而积累深度学习的实战经验。\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/README?id=🔎-如何食用\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/laptop_jupyter.png\" width=\"60\" style=\"max-width: 100%;\"\u003e\n \u003cp dir=\"auto\"\u003eRun\u003cbr\u003elocally\u003c/p\u003e\n \u003c/a\u003e\n \u003c/div\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca href=\"https://charmve.github.io/computer-vision-in-action/#/README?id=🔎-如何食用\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/colab.png\" width=\"60\" style=\"max-width: 100%;\"\u003e\n \u003cp dir=\"auto\"\u003eGoogle\u003cbr\u003eColab\u003c/p\u003e\n \u003c/a\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/notebook.gif\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/notebook.gif\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" rowspan=\"1\"\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e公式 + 图示 + 代码\u003c/h2\u003e\u003ca id=\"user-content-公式--图示--代码\" class=\"anchor\" aria-label=\"Permalink: 公式 + 图示 + 代码\" href=\"#公式--图示--代码\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n \u003cp dir=\"auto\"\u003e我们不仅结合文字、公式和图示来阐明深度学习里常用的模型和算法，还提供代码来演示如何从零开始实现它们，并使用真实数据来提供一个交互式的学习体验。\u003c/p\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/eq.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/eq.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/figure.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/figure.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cdiv dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/code.jpg\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/code.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e❓ 常见问题\u003c/h2\u003e\u003ca id=\"user-content--常见问题\" class=\"anchor\" aria-label=\"Permalink: ❓ 常见问题\" href=\"#-常见问题\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e在线教程页面无法打开\u003c/strong\u003e：\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e测试中存在部分人打不开在线教程的情况。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e部分小伙伴反馈尝试切换浏览器后可以正常打开了，如果仍然不行，最有效的解决办法是科学上网。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e无法加载图片的解决办法\u003c/strong\u003e：\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e根本解决办法还是科学上网，也可以尝试修改host文件看下是否能解决。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e解决方案: 修改host文件 \u003ccode\u003e\u003ca href=\"https://www.jianshu.com/p/25e5e07b2464\" rel=\"nofollow\"\u003e\u003cimg height=\"20\" src=\"https://user-images.githubusercontent.com/29084184/126457822-d431fb90-6b9e-4a4e-bedc-3c598e9e2ee2.png\" alt=\"Apple\" title=\"Apple\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/code\u003e Mac \u003ccode\u003e\u003ca href=\"https://blog.csdn.net/u011583927/article/details/104384169\" rel=\"nofollow\"\u003e\u003cimg height=\"20\" src=\"https://user-images.githubusercontent.com/29084184/126457902-0c1a71c2-f920-45a1-a143-ce8b5c435fe7.png\" alt=\"Win10\" title=\"Win10\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/code\u003e Windows\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e公式无法正常显示解决办法\u003c/strong\u003e：\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGitHub中的Markdown原生是不支持LATEX公式显示的，如果你喜欢在本项目中直接浏览教程，可以安装Chrome的\u003ccode\u003eMathJax Plugin for Github\u003c/code\u003e插件让大部分公式正常显示。而docs文件夹已经利用docsify被部署到了GitHub Pages上，包含公式的章节强力建议使用 \u003ca href=\"https://charmve.github.io/computer-vision-in-action\" rel=\"nofollow\"\u003e《计算机视觉实战演练：算法与应用》 在线阅读\u003c/a\u003e 进行学习。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e当然如果你还想跑一下运行相关代码的话还是得把本项目clone下来，然后运行code文件夹下相关代码。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eJupyter Notebook 无法在 GitHub 上呈现？\u003c/strong\u003e \u003ca href=\"https://leaherb.com/notebook_wont_render_use_nbviewer/\" rel=\"nofollow\"\u003e使用 nbviewer\u003c/a\u003e。 \u003ca href=\"https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e致谢\u003c/h2\u003e\u003ca id=\"user-content-致谢\" class=\"anchor\" aria-label=\"Permalink: 致谢\" href=\"#致谢\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://maiweiai.github.io/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/maiwei_ai.png\" height=\"36\" alt=\"迈微AI研习社\" title=\"迈微AI研习社\" style=\"max-width: 100%;\"\u003e \u003c/a\u003e \u003ca href=\"https://madewithml.com/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c194bbe5708823e7ff971fc7bd3097b2545189e23914d0a7f2b4469dfdea1ac9/68747470733a2f2f6d616465776974686d6c2e636f6d2f7374617469632f696d616765732f6c6f676f2e706e67\" height=\"30\" alt=\"Made With ML\" title=\"Made With ML\" data-canonical-src=\"https://madewithml.com/static/images/logo.png\" style=\"max-width: 100%;\"\u003e \u003c/a\u003e \u003ca href=\"https://www.epubit.com/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4fd02aada8101ea7a8fa321dfda56c3035521d6d578346519a27b7e6ba1a09b6/68747470733a2f2f63646e2e707470726573732e636e2f707562636c6f75642f332f6170702f30373138413642302f636f7665722f323031393132303442443534303039412e706e67\" height=\"30\" alt=\"异步社区\" title=\"异步社区\" data-canonical-src=\"https://cdn.ptpress.cn/pubcloud/3/app/0718A6B0/cover/20191204BD54009A.png\" style=\"max-width: 100%;\"\u003e \u003c/a\u003e \u003ca href=\"https://360.cn\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/464a0e094d37db54cf626e759b452e9b2177fbb4df39522af29652645059104c/68747470733a2f2f70332e73736c2e7168696d672e636f6d2f743031316539346630623965643865363662302e706e67\" height=\"36\" alt=\"奇虎360\" title=\"奇虎360\" data-canonical-src=\"https://p3.ssl.qhimg.com/t011e94f0b9ed8e66b0.png\" style=\"max-width: 100%;\"\u003e \u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e参考文献\u003c/h2\u003e\u003ca id=\"user-content-参考文献-1\" class=\"anchor\" aria-label=\"Permalink: 参考文献\" href=\"#参考文献-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e感谢前人的杰出工作，我才得以写出此书。感谢 \u003ca href=\"/Charmve/computer-vision-in-action/blob/main/docs/REFERENCE.md\"\u003e\u003cb\u003e参考文献\u003c/b\u003e\u003c/a\u003e 中列出及未列出的，所有对此开源工作有帮助的前辈！\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLICENSE\u003c/h2\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: LICENSE\" href=\"#license\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5db08927dc42829460b40746b7dda8c78a39aefab4bda202bd1deee744d9aed0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d7265643f6c6f676f3d617061636865\" alt=\"Code License\" data-canonical-src=\"https://img.shields.io/badge/license-Apache%202.0-red?logo=apache\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\" rel=\"nofollow\"\u003e\u003cimg alt=\"知识共享许可协议\" src=\"https://camo.githubusercontent.com/834a7753f25e27209ceb7b8677860c42d41cc282fd7df6a5b014b13e457b5be8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732532306c6963656e73652d434325323042592d2d4e432d2d5341253230342e302d677265656e3f6c6f676f3d6372656174697665636f6d6d6f6e73\" title=\"CC BY--NC--SA 4.0\" data-canonical-src=\"https://img.shields.io/badge/docs%20license-CC%20BY--NC--SA%204.0-green?logo=creativecommons\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eL0CV\u003c/code\u003e代码部分采用 \u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0\" rel=\"nofollow\"\u003eApache 2.0协议\u003c/a\u003e 进行许可，包括名为 \u003cb\u003e\u003cem\u003eL0CV\u003c/em\u003e\u003c/b\u003e 的原创第三方库、\u003ccode\u003e/code\u003c/code\u003e和\u003ccode\u003e/notebook\u003c/code\u003e下的源代码。遵循许可的前提下，你可以自由地对代码进行修改，再发布，可以将代码用作商业用途。但要求你：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e署名\u003c/strong\u003e：在原有代码和衍生代码中，保留原作者署名及代码来源信息。\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e保留许可证\u003c/strong\u003e：在原有代码和衍生代码中，保留\u003ccode\u003eApache 2.0\u003c/code\u003e协议文件。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eL0CV\u003c/code\u003e文档部分采用 \u003ca href=\"http://creativecommons.org/licenses/by/4.0/\" rel=\"nofollow\"\u003e知识共享署名 4.0 国际许可协议\u003c/a\u003e 进行许可。 遵循许可的前提下，你可以自由地共享，包括在任何媒介上以任何形式复制、发行本作品，亦可以自由地演绎、修改、转换或以本作品为基础进行二次创作。但要求你：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e署名\u003c/strong\u003e：应在使用本文档的全部或部分内容时候，注明原作者及来源信息。\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e非商业性使用\u003c/strong\u003e：不得用于商业出版或其他任何带有商业性质的行为。如需商业使用，请联系作者。\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e相同方式共享的条件\u003c/strong\u003e：在本文档基础上演绎、修改的作品，应当继续以知识共享署名 4.0国际许可协议进行许可。\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e👥 社区互助\u003c/h2\u003e\u003ca id=\"user-content--社区互助\" class=\"anchor\" aria-label=\"Permalink: 👥 社区互助\" href=\"#-社区互助\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e如果您在使用的过程中碰到问题，可以通过下面几个途径寻求帮助，同时我们也鼓励资深用户通过下面的途径给新人提供帮助。\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e通过 \u003ca href=\"https://github.com/Charmve/computer-vision-in-action/discussions\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bd30bd702c9ae10b961161870494791281e0aacb5c71942b25bce822df1c6cb5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4769744875622d44697363757373696f6e732d677265656e3f6c6f676f3d676974687562\" alt=\"GitHub Discuss\" data-canonical-src=\"https://img.shields.io/badge/GitHub-Discussions-green?logo=github\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e 提问时，建议使用 \u003ccode\u003eQ\u0026amp;A\u003c/code\u003e 标签。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e通过 \u003ca href=\"http://stackoverflow.com/questions/tagged/L0CV\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/acd86085b1c32dac2a3d87dd784e4e3727f295a276aa1e934d8cd16e708b0aa5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d537461636b2532304f766572666c6f772d677261793f6c6f676f3d737461636b6f766572666c6f77\" alt=\"Stack Overflow\" data-canonical-src=\"https://img.shields.io/badge/-Stack%20Overflow-gray?logo=stackoverflow\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e 或者 \u003ca href=\"https://segmentfault.com/t/L0CV\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/f928c398aed6888ce8d686d66dea0a0b9177204299bbd07b93619bb4e3cb7a3b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d5365676d656e742532304661756c742d677261793f6c6f676f3d6d6f6e676f6462\" alt=\"Segment Fault\" data-canonical-src=\"https://img.shields.io/badge/-Segment%20Fault-gray?logo=mongodb\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e 提问时，建议加上 \u003ccode\u003eL0CV\u003c/code\u003e 标签。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://segmentfault.com/t/L0CV\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5ac4815762fce97e10db0d521a995910fbb61a021a159c657906ca9a11bef0ba/68747470733a2f2f696d672e736869656c64732e696f2f62616467652fe5beaee4bfa12d4c3043562d677265656e3f6c6f676f3d776563686174\" alt=\"Segment Fault\" data-canonical-src=\"https://img.shields.io/badge/微信-L0CV-green?logo=wechat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e 微信、知乎、微博开话题可以生成tag，如微信聊天、朋友圈加 \u003ccode\u003e#L0CV\u003c/code\u003e 可话题交流。\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eL0CV 读者微信交流群 (加我微信Yida_Zhang2，备注：L0CV-高校/企业-称呼)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e💖 Support this project\u003c/h2\u003e\u003ca id=\"user-content--support-this-project\" class=\"anchor\" aria-label=\"Permalink: 💖 Support this project\" href=\"#-support-this-project\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDonating to help me continue working on this project. I'm appreciate all you in \u003ca href=\"https://charmve.github.io/sponsor.html\" rel=\"nofollow\"\u003ebacker list\u003c/a\u003e. \u003ca href=\"https://charmve.github.io/sponsor.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8e9605cadb9ef014e1f042da9c91069e3e91fc66913785445583e70bbdcd3934/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374796c652d2d3565626130302e7376673f6c6162656c3d436f66666565266c6f676f3d6b6f2d6669267374796c653d736f6369616c\" alt=\"BuymeaCoffee\" data-canonical-src=\"https://img.shields.io/badge/style--5eba00.svg?label=Coffee\u0026amp;logo=ko-fi\u0026amp;style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://charmve.github.io/sponsor.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8ca6cbc30815bc3ef458755d26604c424652dc18a51df3b3d708126667c31c91/68747470733a2f2f63646e2e6275796d6561636f666665652e636f6d2f627574746f6e732f76322f64656661756c742d7265642e706e67\" alt=\"Buy Me A Coffee\" width=\"180\" data-canonical-src=\"https://cdn.buymeacoffee.com/buttons/v2/default-red.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCONTRIBUTION\u003c/h2\u003e\u003ca id=\"user-content-contribution\" class=\"anchor\" aria-label=\"Permalink: CONTRIBUTION\" href=\"#contribution\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e作者会尽最大努力来确保书中内容的准确性，但难免会存在疏漏与不妥之处。欢迎您将发现的问题反馈给我们，帮助提升本开放资源的质量。\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\t\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\t\u003ctbody\u003e\u003ctr\u003e\n\t\u003ctd\u003e\n\t\t\u003cdiv dir=\"auto\"\u003e\n\t\t\t\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHelp us make these docs great!\u003c/h3\u003e\u003ca id=\"user-content-help-us-make-these-docs-great\" class=\"anchor\" aria-label=\"Permalink: Help us make these docs great!\" href=\"#help-us-make-these-docs-great\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\t\t\t\u003cp dir=\"auto\"\u003eAll \u003cb\u003e\u003ci\u003eVC-action\u003c/i\u003e\u003c/b\u003e docs are open source. 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support\n\t\t\t\u003c/a\u003e\n\t\t\u003c/div\u003e\n\t\u003c/td\u003e\n\t\u003c/tr\u003e\n\t\u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUse this bibtex to cite this repository:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{computer-vision-in-action,\n title={计算机视觉实战演练：算法与应用（Computer Vision in Action）},\n author={Charmve},\n year={2021.06},\n publisher={Github},\n journal={GitHub repository},\n howpublished={\\url{https://github.com/Charmve/computer-vision-in-action}},\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{computer-vision-in-action,\n title={计算机视觉实战演练：算法与应用（Computer Vision in Action）},\n author={Charmve},\n year={2021.06},\n publisher={Github},\n journal={GitHub repository},\n howpublished={\\url{https://github.com/Charmve/computer-vision-in-action}},\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-\"\u003eIndex\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStargazers Over Time\u003c/h2\u003e\u003ca id=\"user-content-stargazers-over-time\" class=\"anchor\" aria-label=\"Permalink: Stargazers Over Time\" href=\"#stargazers-over-time\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv dir=\"auto\"\u003e\n\t\u003cp align=\"center\" dir=\"auto\"\u003e\t\n\t\t\u003cb\u003eAwesome!\u003c/b\u003e\t\n\t\t\u003ca href=\"https://github.com/Charmve/computer-vision-in-action\"\u003e\u003cem\u003eCharmve/computer-vision-in-action\u003c/em\u003e\u003c/a\u003e\n\t\twas created 2 months ago\n\t\tand now has \u003cb\u003e1546\u003c/b\u003e stars.\n \u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://starchart.cc/Charmve/computer-vision-in-action\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59ba05f844823eecca0032894edec61f18b5716b8f3eb391d1eb990b14467607/68747470733a2f2f7374617263686172742e63632f436861726d76652f636f6d70757465722d766973696f6e2d696e2d616374696f6e2e737667\" alt=\"Stargazers over time\" data-canonical-src=\"https://starchart.cc/Charmve/computer-vision-in-action.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cbr\u003e\u003ca href=\"https://github.com/Charmve/computer-vision-in-action#-以用促学先会后懂-\"\u003e\u003cimg align=\"right\" alt=\"Go for it!\" src=\"https://raw.githubusercontent.com/Charmve/computer-vision-in-action/dd292873828228a753a9bd2de4576dbf8cc3902c/res/ui/footer-rocket.svg\" height=\"220\" title=\"Do what you like, and do it best!\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003eFeel free to ask any questions, open a PR if you feel something can be done differently!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 align=\"center\" tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🌟 Star this repository 🌟\u003c/h2\u003e\u003ca id=\"user-content--star-this-repository-\" class=\"anchor\" aria-label=\"Permalink: 🌟 Star this repository 🌟\" href=\"#-star-this-repository-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003eCreated by \u003ca href=\"https://github.com/Charmve\"\u003eCharmve\u003c/a\u003e \u0026amp; \u003ca href=\"https://github.com/MaiweiAI\"\u003emaiwei.ai\u003c/a\u003e Community | Deployed on \u003ca href=\"https://charmve.github.io/computer-vision-in-action/\" rel=\"nofollow\"\u003eGitHub Page\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"✨ 以用促学，先会后懂 ✨","anchor":"-以用促学先会后懂-","htmlText":"✨ 以用促学，先会后懂 ✨"},{"level":3,"text":"L0CV 一种结合了代码、图示和HTML的在线学习媒介","anchor":"l0cv---一种结合了代码图示和html的在线学习媒介","htmlText":"L0CV 一种结合了代码、图示和HTML的在线学习媒介"},{"level":1,"text":" 计算机视觉实战演练：算法与应用 Computer Vision in Action","anchor":"-计算机视觉实战演练算法与应用--computer-vision-in-action","htmlText":" 计算机视觉实战演练：算法与应用 Computer Vision in Action"},{"level":4,"text":"👉 在线阅读（优先更新实战篇和进阶篇）","anchor":"--在线阅读优先更新实战篇和进阶篇","htmlText":"👉 在线阅读（优先更新实战篇和进阶篇）"},{"level":4,"text":"最新版PDF下载","anchor":"最新版pdf下载","htmlText":"最新版PDF下载"},{"level":2,"text":"Index","anchor":"index","htmlText":"Index"},{"level":2,"text":"💠 全书组织","anchor":"-全书组织","htmlText":"💠 全书组织"},{"level":2,"text":"🌈 愿景","anchor":"-愿景","htmlText":"🌈 愿景"},{"level":2,"text":"📘 本书目录","anchor":"-本书目录","htmlText":"📘 本书目录"},{"level":4,"text":"👉 在线阅读（优先更新实战篇和进阶篇）","anchor":"--在线阅读优先更新实战篇和进阶篇-1","htmlText":"👉 在线阅读（优先更新实战篇和进阶篇）"},{"level":4,"text":"序言","anchor":"序言","htmlText":"序言"},{"level":4,"text":"主要符号表","anchor":"主要符号表","htmlText":"主要符号表"},{"level":4,"text":"绪论篇","anchor":"绪论篇","htmlText":"绪论篇"},{"level":4,"text":"理论篇","anchor":"理论篇","htmlText":"理论篇"},{"level":4,"text":"实战篇","anchor":"实战篇","htmlText":"实战篇"},{"level":4,"text":"进阶篇","anchor":"进阶篇","htmlText":"进阶篇"},{"level":4,"text":"附录","anchor":"附录","htmlText":"附录"},{"level":4,"text":"后记","anchor":"后记","htmlText":"后记"},{"level":4,"text":"参考文献","anchor":"参考文献","htmlText":"参考文献"},{"level":2,"text":"🔍 文件浏览","anchor":"-文件浏览","htmlText":"🔍 文件浏览"},{"level":2,"text":"L0CV DemoDay","anchor":"l0cv-demoday","htmlText":"L0CV DemoDay"},{"level":5,"text":"以用促学，先会后懂。理解深度学习的最佳方法是学以致用。","anchor":"以用促学先会后懂理解深度学习的最佳方法是学以致用","htmlText":"以用促学，先会后懂。理解深度学习的最佳方法是学以致用。"},{"level":2,"text":"🔑 如何食用","anchor":"-如何食用","htmlText":"🔑 如何食用"},{"level":3,"text":"方式一 Jupyter Notebook (推荐方式 ✨)","anchor":"方式一-jupyter-notebook-推荐方式-","htmlText":"方式一 Jupyter Notebook (推荐方式 ✨)"},{"level":4,"text":"1. 本地运行","anchor":"1-本地运行","htmlText":"1. 本地运行"},{"level":4,"text":"2. 远程运行","anchor":"2-远程运行","htmlText":"2. 远程运行"},{"level":3,"text":"方式二 使用 /code","anchor":"方式二-使用-code","htmlText":"方式二 使用 /code"},{"level":4,"text":"1. 运行环境 + L0CV 加载","anchor":"1-运行环境--l0cv-加载","htmlText":"1. 运行环境 + L0CV 加载"},{"level":4,"text":"2. 直接调用每个章节的代码测试","anchor":"2-直接调用每个章节的代码测试","htmlText":"2. 直接调用每个章节的代码测试"},{"level":2,"text":"每一小节都是可以运行的 Jupyter 记事本","anchor":"每一小节都是可以运行的-jupyter-记事本","htmlText":"每一小节都是可以运行的 Jupyter 记事本"},{"level":2,"text":"公式 + 图示 + 代码","anchor":"公式--图示--代码","htmlText":"公式 + 图示 + 代码"},{"level":2,"text":"❓ 常见问题","anchor":"-常见问题","htmlText":"❓ 常见问题"},{"level":2,"text":"致谢","anchor":"致谢","htmlText":"致谢"},{"level":2,"text":"参考文献","anchor":"参考文献-1","htmlText":"参考文献"},{"level":2,"text":"LICENSE","anchor":"license","htmlText":"LICENSE"},{"level":2,"text":"👥 社区互助","anchor":"-社区互助","htmlText":"👥 社区互助"},{"level":2,"text":"💖 Support this project","anchor":"-support-this-project","htmlText":"💖 Support this project"},{"level":2,"text":"CONTRIBUTION","anchor":"contribution","htmlText":"CONTRIBUTION"},{"level":3,"text":"Help us make these docs great!","anchor":"help-us-make-these-docs-great","htmlText":"Help us make these docs great!"},{"level":3,"text":"\n\t\t\t\tStill need help?\n\t\t\t","anchor":"still-need-help","htmlText":"\n\t\t\t\tStill need help?\n\t\t\t"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Stargazers Over Time","anchor":"stargazers-over-time","htmlText":"Stargazers Over Time"},{"level":2,"text":"🌟 Star this repository 🌟","anchor":"-star-this-repository-","htmlText":"🌟 Star this repository 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src="https://camo.githubusercontent.com/ecb1a7cccdfba231b654a8d1f956749524459fd9da5bc6493830a6b4f2d0a162/68747470733a2f2f6261646765732e737472726c2e6465762f7669736974732f436861726d76652f636f6d70757465722d766973696f6e2d696e2d616374696f6e3f636f6c6f723d626c61636b266c6f676f3d676974687562" data-canonical-src="https://badges.strrl.dev/visits/Charmve/computer-vision-in-action?color=black&logo=github" style="max-width: 100%;"> </a> </p> <blockquote> <p dir="auto">"如果你只是看了这个项目的在线文档，那么你并没有利用好这个项目。太可惜！"</p> </blockquote> <a href="https://charmve.github.io/computer-vision-in-action" rel="nofollow"> <img src="/Charmve/computer-vision-in-action/raw/main/res/ui/mutil-platform.png" width="=""" style="max-width: 100%;"> </a> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">✨ 以用促学，先会后懂 ✨</h2><a id="user-content--以用促学先会后懂-" class="anchor" aria-label="Permalink: ✨ 以用促学，先会后懂 ✨" href="#-以用促学先会后懂-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">L0CV <sup><a href="https://charmve.github.io/L0CV-web/" rel="nofollow"><code>🔊</code></a></sup> 一种结合了代码、图示和HTML的在线学习媒介</h3><a id="user-content-l0cv---一种结合了代码图示和html的在线学习媒介" class="anchor" aria-label="Permalink: L0CV 一种结合了代码、图示和HTML的在线学习媒介" href="#l0cv---一种结合了代码图示和html的在线学习媒介"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <br> <markdown-accessiblity-table><table align="center"> <tbody><tr> <td> <code>全面</code> <code>前沿</code> <code>免费</code> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"> 计算机视觉实战演练：算法与应用 <sup> 📌</sup> <br><em>Computer Vision in Action</em></h1><a id="user-content--计算机视觉实战演练算法与应用--computer-vision-in-action" class="anchor" aria-label="Permalink: 计算机视觉实战演练：算法与应用 Computer Vision in Action" href="#-计算机视觉实战演练算法与应用--computer-vision-in-action"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://github.com/Charmve">作者：张伟（Charmve）</a></p> <p align="center" dir="auto"> <a href="https://circleci.com/gh/Charmve/computer-vision-in-action" rel="nofollow"><img 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在线阅读（优先更新实战篇和进阶篇）</h4><a id="user-content---在线阅读优先更新实战篇和进阶篇" class="anchor" aria-label="Permalink: 👉 在线阅读（优先更新实战篇和进阶篇）" href="#--在线阅读优先更新实战篇和进阶篇"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> - 地址：<a href="https://charmve.github.io/computer-vision-in-action/" rel="nofollow">https://charmve.github.io/computer-vision-in-action/</a> </blockquote> <blockquote> <div 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3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/Charmve/computer-vision-in-action/blob/main/res/ui/L0CV.png"><img align="right" src="/Charmve/computer-vision-in-action/raw/main/res/ui/L0CV.png" width="63%" alt="L0CV architecture" title="L0CV architecture" style="max-width: 100%;"></a></p> <ul dir="auto"> <li><a href="#-%E5%85%A8%E4%B9%A6%E7%BB%84%E7%BB%87">💠 全书组织</a></li> <li><a href="#-%E6%84%BF%E6%99%AF">🌈 愿景</a></li> <li><a href="#-%E6%9C%AC%E4%B9%A6%E7%9B%AE%E5%BD%95">📘 本书目录</a></li> <li><a href="#-%E6%96%87%E4%BB%B6%E6%B5%8F%E8%A7%88">🔍 文件浏览</a></li> <li><a href="#l0cv-demoday">🌼 L0CV-DemoDay</a></li> <li><a href="#-%E5%A6%82%E4%BD%95%E9%A3%9F%E7%94%A8">🔑 如何食用</a></li> <li><a href="#-%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98">❓ 常见问题</a></li> <li><a href="#-%E7%A4%BE%E5%8C%BA%E4%BA%92%E5%8A%A9">👥 社区互助</a></li> <li><a href="#%E8%87%B4%E8%B0%A2">❤️ 致谢</a></li> <li><a href="#license">📄 LICENSE</a></li> <li><a href="#contribution">👐 参与项目</a></li> <li><a href="#%E5%85%B3%E6%B3%A8%E6%88%91%E4%BB%AC">🤝 关注我们</a></li> <li><a href="#citation">📎 Citation</a></li> </ul> <p align="right" dir="auto"> 🏷️ <code>sec_code</code> <b>图1 L0CV 结构图</b> </p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">💠 全书组织</h2><a id="user-content--全书组织" class="anchor" aria-label="Permalink: 💠 全书组织" href="#-全书组织"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">🏷️ <code>fig_book_org</code></p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/Charmve/computer-vision-in-action/blob/main/res/ui/book_org.png"><img src="/Charmve/computer-vision-in-action/raw/main/res/ui/book_org.png" alt="book_org.png" style="max-width: 100%;"></a> </p> <p align="right" dir="auto"> <a href="/Charmve/computer-vision-in-action/blob/main/res/计算机视觉实战演练：算法与应用_思维导图.pdf">全书详细思维导图</a> </p> <p dir="auto">本书详细介绍，请移步 <a href="/Charmve/computer-vision-in-action/blob/main/docs/book_preface.md"><b>序言</b></a>。</p> <ul dir="auto"> <li> <p dir="auto">第一部分包括基础知识和预备知识。提供深度学习的入门课程，然后在理论篇中，将快速向你介绍实践计算机视觉所需的前提条件，例如如何存储和处理数据，以及如何应用基于线性代数、微积分和概率基本概念的各种数值运算，涵盖了深度学习的最基本概念和技术，例如线性回归、多层感知机和正则化。</p> </li> <li> <p dir="auto">第二部分是本书涉及的计算机视觉基础理论，核心部分为神经网络模型，包括神经网络、卷积神经网络、循环神经网络理论讲解，以图像分类、模型拟合与优化作为其代码的实战项目。在模型拟合和优化章节中，着重分享梯度下降、随机梯度下降、动量法、AdaBoost等方法。</p> </li> <li> <p dir="auto">接下来的七章集中讨论现代计算机视觉技术实战，也是本书的核心部分。围绕这样的组织逻辑：什么是计算机视觉？计算机视觉解决什么问题，都是怎么解决的？传统方法——以卷积神经网络为中心的神经网络；现代方法——Transformer、强化学习、迁移学习、生成对抗等。各种方法是如何实现的，用到了什么框架？在第7章中，描述了计算机视觉的经典卷积神经网络PyTorch实现，并为我们随后实现更复杂的模型奠定了基础。在随后的几个章节中，我们主要解决图像分类、目标检测、语义分割、3D重建等实际问题，并给出实战项目。</p> </li> <li> <p dir="auto">该部分以项目为实战指导，给出详细的项目指导书和代码实现，更为特别的是，给出了<strong>notebook</strong>可以直接在线运行，跑通结果，免去了本地运行环境的搭建复杂性。于此同时，为了方便读者在本地调试，作者建立了一个名为 <code>L0CV</code> 的第三方包，可以直接在代码中 <code>import L0CV</code> 后使用。</p> </li> <li> <p dir="auto">第三部分讨论最近几年出现的<b>“网红”模型</b>，诸如：Transformer、Attention、知识蒸馏、迁移学习、生成对抗模型等。这部分也是此份资料的力挺之作。最后，在 <code>chap_optimization</code> 中，我们讨论了用于训练深度学习模型的几种常用优化算法，如：模型压缩、模型剪枝、微调、蒸馏等。</p> </li> </ul> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🌈 愿景</h2><a id="user-content--愿景" class="anchor" aria-label="Permalink: 🌈 愿景" href="#-愿景"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">本开源项目代表了我们的一种尝试：我们将教给读者概念、背景知识和代码；我们将在同一个地方阐述剖析问题所需的批判性思维、解决问题所需的数学知识，以及实现解决方案所需的工程技能。</p> <p dir="auto">我们的目标是创建一个为实现以下目标的统一资源：</p> <ol dir="auto"> <li>所有人均可在网上免费获取；</li> <li>提供足够的技术深度，从而帮助读者实际成为计算机视觉应用科学家：既理解数学原理，又能够实现并不断改进方法；</li> <li>包含可运行的代码，为读者展示如何在实际中解决问题。这样不仅直接将数学公式对应成实际代码，而且可以修改代码、观察结果并及时获取经验；</li> <li>允许我们和整个<a href="https://github.com/Charmve/computer-vision-in-action/discussions">社区</a>不断快速迭代内容，从而紧跟仍在高速发展的计算机视觉领域；</li> <li>由包含有关技术细节问答的论坛作为补充，使大家可以相互答疑并交换经验。</li> </ol> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <br> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📘 本书目录</h2><a id="user-content--本书目录" class="anchor" aria-label="Permalink: 📘 本书目录" href="#-本书目录"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <blockquote> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">👉 在线阅读（优先更新实战篇和进阶篇）</h4><a id="user-content---在线阅读优先更新实战篇和进阶篇-1" class="anchor" aria-label="Permalink: 👉 在线阅读（优先更新实战篇和进阶篇）" href="#--在线阅读优先更新实战篇和进阶篇-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> - 地址：<a href="https://charmve.github.io/computer-vision-in-action/" rel="nofollow">https://charmve.github.io/computer-vision-in-action/</a> </blockquote> <details><summary>📘 详细目录(点击展开)</summary> <markdown-accessiblity-table><table align="center"> <tbody><tr> <td> <ul dir="auto"> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><a href="/Charmve/computer-vision-in-action/blob/main/docs/book_preface.md">序言</a></h4><a id="user-content-序言" class="anchor" aria-label="Permalink: 序言" href="#序言"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b></li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">主要符号表</h4><a id="user-content-主要符号表" class="anchor" aria-label="Permalink: 主要符号表" href="#主要符号表"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b></li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">绪论篇</h4><a id="user-content-绪论篇" class="anchor" aria-label="Permalink: 绪论篇" href="#绪论篇"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b> <ul dir="auto"> <li>第 0 章 <a href="https://charmve.github.io/computer-vision-in-action/#/chapter0/chapter0" rel="nofollow">计算机视觉概述</a> <ul dir="auto"> <li>0.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.1_%E6%A6%82%E8%BF%B0.md">概述</a> <ul dir="auto"> <li>0.1.1 什么是计算机视觉</li> <li>0.1.2 计算机视觉解决什么问题</li> <li>0.1.3 行业应用</li> </ul> </li> <li>0.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.2_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5.md">计算机视觉基本概念</a></li> <li>0.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.3_%E5%8F%91%E5%B1%95%E5%8E%86%E5%8F%B2%E5%9B%9E%E9%A1%BE.md">发展历史回顾</a></li> <li>0.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.4_%E5%85%B8%E5%9E%8B%E7%9A%84%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E4%BB%BB%E5%8A%A1.md">典型的计算机视觉任务</a> <ul dir="auto"> <li>图像分类</li> <li>目标识别与目标检测</li> <li>实例分割与语义分割</li> <li>3D 建模</li> </ul> </li> <li>0.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/0_%E7%BB%AA%E8%AE%BA/chapter0.5_%E5%9B%BD%E5%86%85%E5%A4%96%E4%BC%98%E7%A7%80%E7%9A%84%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E5%9B%A2%E9%98%9F%E6%B1%87%E6%80%BB.md">国内外优秀的计算机视觉团队汇总</a></li> <li>小练习</li> <li>小结</li> <li>参考文献</li> </ul> </li> </ul> </li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">理论篇</h4><a id="user-content-理论篇" class="anchor" aria-label="Permalink: 理论篇" href="#理论篇"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b> <ul dir="auto"> <li>第 1 章 <a href="https://charmve.github.io/computer-vision-in-action/#/chapter2/chapter2" rel="nofollow">神经网络</a> <ul dir="auto"> <li>1.1 <a href="/Charmve/computer-vision-in-action/blob/main/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.1_line-regression.md">线性回归</a> <ul dir="auto"> <li>1.1.1 基本原理</li> <li>1.1.2 从零实现线性回归</li> <li>1.1.3 线性回归的简洁实现</li> </ul> </li> <li>1.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter1.2_Softmax%E5%9B%9E%E5%BD%92.md">Softmax 回归</a> <ul dir="auto"> <li>1.2.1 softmax回归模型</li> <li>1.2.2 从零开始实现softmax回归</li> <li>1.2.3 softmax回归的简洁实现</li> </ul> </li> <li>1.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.3_%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E5%99%A8MLP.md">多层感知器</a> <ul dir="auto"> <li>1.3.1 基本原理</li> <li>1.3.2 从零开始实现多层感知器</li> <li>1.3.3 多层感知器的简洁实现</li> </ul> </li> <li>1.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.4_Back-Propagation.md">反向传播算法</a></li> <li>1.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.5_neural-networks.md">神经网络</a> <ul dir="auto"> <li>1.5.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter1_Neural-Networks/chapter1.5.1_%E7%A5%9E%E7%BB%8F%E5%AD%A6%E8%A7%82%E7%82%B9.md">神经学观点</a></li> <li>1.5.2 <a href="https://cs231n.github.io/neural-networks-1/" rel="nofollow">神经网络1-建立神经网络架构</a></li> <li>1.5.3 <a href="https://cs231n.github.io/neural-networks-2/" rel="nofollow">神经网络2-设置数据和损失</a></li> <li>1.5.4 <a href="https://cs231n.github.io/neural-networks-3/" rel="nofollow">神经网络3-学习和评估</a></li> <li>1.5.5 <a href="https://cs231n.github.io/neural-networks-case-study/" rel="nofollow">案例分析-最小神经网络案例研究</a></li> </ul> </li> <li>1.6 <a href="https://blog.csdn.net/Charmve/article/details/108531735" rel="nofollow">实战项目 1 - 手写字分类</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 2 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md">卷积神经网络</a> <ul dir="auto"> <li>2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#21-%E4%BB%8E%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%88%B0%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">从神经网络到卷积神经网络</a> <ul dir="auto"> <li>2.1.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#211-%E5%AE%9A%E4%B9%89">定义</a></li> <li>2.1.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#212-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E6%9E%B6%E6%9E%84">卷积神经网络的架构</a></li> </ul> </li> <li>2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#22-%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C%E7%9A%84%E5%B1%82%E7%BA%A7%E7%BB%93%E6%9E%84">卷积网络的层级结构</a> <ul dir="auto"> <li>2.2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#221-%E6%95%B0%E6%8D%AE%E8%BE%93%E5%85%A5%E5%B1%82">数据输入层</a></li> <li>2.2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#222-%E5%8D%B7%E7%A7%AF%E8%AE%A1%E7%AE%97%E5%B1%82">卷积计算层</a></li> <li>2.2.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#223-%E9%9D%9E%E7%BA%BF%E6%80%A7%E5%B1%82%E6%88%96%E6%BF%80%E6%B4%BB%E5%B1%82">非线性层（或激活层）</a></li> <li>2.2.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#224-%E6%B1%A0%E5%8C%96%E5%B1%82">池化层</a></li> <li>2.2.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#225-%E5%85%A8%E8%BF%9E%E6%8E%A5%E5%B1%82">全连接层</a></li> </ul> </li> <li>2.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#23-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%87%A0%E7%82%B9%E8%AF%B4%E6%98%8E">卷积神经网络的几点说明</a></li> <li>2.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md">实战项目 2 - 动手搭建一个卷积神经网络</a> <ul dir="auto"> <li>2.4.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#271-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD">卷积神经网络的前向传播</a></li> <li>2.4.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#272-%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD">卷积神经网络的反向传播</a></li> <li>2.4.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#273-%E6%89%8B%E5%86%99%E4%B8%80%E4%B8%AA%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">手写一个卷积神经网络</a> <ul dir="auto"> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#1-%E5%AE%9A%E4%B9%89%E4%B8%80%E4%B8%AA%E5%8D%B7%E7%A7%AF%E5%B1%82">1. 定义一个卷积层</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#2-%E6%9E%84%E9%80%A0%E4%B8%80%E4%B8%AA%E6%BF%80%E6%B4%BB%E5%87%BD%E6%95%B0">2. 构造一个激活函数</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#3-%E5%AE%9A%E4%B9%89%E4%B8%80%E4%B8%AA%E7%B1%BB%E4%BF%9D%E5%AD%98%E5%8D%B7%E7%A7%AF%E5%B1%82%E7%9A%84%E5%8F%82%E6%95%B0%E5%92%8C%E6%A2%AF%E5%BA%A6">3. 定义一个类，保存卷积层的参数和梯度</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#4-%E5%8D%B7%E7%A7%AF%E5%B1%82%E7%9A%84%E5%89%8D%E5%90%91%E4%BC%A0%E6%92%AD">4. 卷积层的前向传播</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#5-%E5%8D%B7%E7%A7%AF%E5%B1%82%E7%9A%84%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD">5. 卷积层的反向传播</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#6-MaxPooling%E5%B1%82%E7%9A%84%E8%AE%AD%E7%BB%83">6. MaxPooling层的训练</a></li> </ul> </li> <li>2.4.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md#274-PaddlePaddle%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%BA%90%E7%A0%81%E8%A7%A3%E6%9E%90">PaddlePaddle卷积神经网络源码解析</a></li> </ul> </li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#%E5%B0%8F%E7%BB%93">小结</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 3 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification">图像分类</a> <ul dir="auto"> <li>3.1 <a href="https://cs231n.github.io/classification/" rel="nofollow">数据驱动方法</a> <ul dir="auto"> <li>3.1.1 语义上的差别</li> <li>3.1.2 图像分类任务面临着许多挑战</li> <li>3.1.3 数据驱动的方法</li> </ul> </li> <li>3.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter3.2_knn.md">k 最近邻算法</a> <ul dir="auto"> <li>3.2.1 k 近邻模型</li> <li>3.2.2 k 近邻模型三个基本要素</li> <li>3.2.3 KNN算法的决策过程</li> <li>3.2.4 k 近邻算法Python实现</li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>3.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter3.3_%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA.md">支持向量机</a> <ul dir="auto"> <li>3.3.1 概述</li> <li>3.3.2 线性支持向量机</li> <li>3.3.3 从零开始实现支持向量机</li> <li>3.3.4 支持向量机的简洁实现</li> </ul> </li> <li>3.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/chapter3.4_Logistic-Regression.md">逻辑回归 LR</a> <ul dir="auto"> <li>3.4.1 逻辑回归模型</li> <li>3.4.2 从零开始实现逻辑回归</li> <li>3.4.3 逻辑回归的简洁实现</li> </ul> </li> <li>3.5 <a href="https://blog.csdn.net/charmve/category_9754344.html" rel="nofollow">实战项目 3 - 表情识别</a></li> <li>3.6 <a href="http://mp.weixin.qq.com/s?__biz=MzIxMjg1Njc3Mw%3D%3D&chksm=97bef597a0c97c813e185e1bbf987b93d496c6ead8371364fd175d9bac46e6dcf7059cf81cb2&idx=1&mid=2247487293&scene=21&sn=89684d1c107177983dc1b4dca8c20a5b#wechat_redirect" rel="nofollow">实战项目 4 - 使用卷积神经网络对CIFAR10图片进行分类</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/README.md#%E5%B0%8F%E7%BB%93">小结</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter3_Image-Classification/README.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 4 章 循环神经网络 <ul dir="auto"> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.1_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md">4.1 循环神经网络 RNN</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.1_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md">4.2 循环神经网络的从零开始实现</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.1_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md">4.3 循环神经网络的简洁实现</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.4_%E9%95%BF%E7%9F%AD%E6%9C%9F%E8%AE%B0%E5%BF%86%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9CLSTM.md">4.4 长短期记忆人工神经网络 LSTM</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter4_%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter4.5_%E9%97%A8%E6%8E%A7%E5%BE%AA%E7%8E%AF%E5%8D%95%E5%85%83.md">4.5 门控循环单元（GRU）</a></li> <li>小结</li> <li>练习</li> <li>参考文献</li> </ul> </li> <li>第 5 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md">图神经网络</a> <ul dir="auto"> <li>5.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md#51-%E5%8E%86%E5%8F%B2%E8%84%89%E7%BB%9C">历史脉络</a></li> <li>5.2 <a href="https://www.cnblogs.com/SivilTaram/p/graph_neural_network_1.html" rel="nofollow">图神经网络(Graph Neural Network)</a> <ul dir="auto"> <li>5.2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/chapter5_%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C.md#51-%E5%B8%B8%E8%A7%81%E6%95%B0%E6%8D%AE%E9%9B%86">状态更新与输出</a></li> <li>5.2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#812-pytorch%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E8%AF%BB%E5%8F%96%E6%96%B9%E6%B3%95%E7%AE%80%E4%BB%8B">不动点理论</a></li> <li>5.2.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#813-%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA%E7%AE%80%E4%BB%8B">具体实现</a></li> <li>5.2.4 <a href="/Charmve/computer-vision-in-action/blob/main">模型学习</a></li> <li>5.2.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.2_%E5%9F%BA%E5%87%86BenchMark.md">GNN与RNN</a></li> <li>5.2.6 <a href="/Charmve/computer-vision-in-action/blob/main">GNN的局限</a></li> </ul> </li> <li>5.3 <a href="/Charmve/computer-vision-in-action/blob/main">门控图神经网络(Gated Graph Neural Network)</a> <ul dir="auto"> <li>5.3.1 状态更新</li> <li>5.3.2 实例1:到达判断</li> <li>5.3.3 实例2:语义解析</li> <li>5.3.4 GNN与GGNN</li> </ul> </li> <li>5.4 <a href="https://www.cnblogs.com/SivilTaram/p/graph_neural_network_2.html" rel="nofollow">图卷积神经网络(GCNN)</a> <ul dir="auto"> <li>5.4.1 图卷积缘起</li> <li>5.4.2 图卷积框架(Framework)</li> <li>5.4.3 再谈卷积</li> <li>5.4.4 空域卷积(Spatial Convolution)</li> <li>5.4.5 消息传递网络(Message Passing Neural Network)</li> <li>5.4.6 图采样与聚合(Graph Sample and Aggregate)</li> <li>5.4.7 图结构序列化(PATCHY-SAN)</li> <li>5.4.8 频域卷积(Spectral Convolution)</li> </ul> </li> <li>5.5 <a href="https://www.cnblogs.com/SivilTaram/p/graph_neural_network_3.html" rel="nofollow">生成图表示</a> <ul dir="auto"> <li>5.5.1 图读出操作(ReadOut)</li> <li>5.5.2 基于统计的方法(Statistics Category)</li> <li>5.5.3 基于学习的方法(Learning Category)</li> <li>5.5.4 其他方法</li> </ul> </li> <li>5.6 <a href="https://www.cnblogs.com/SivilTaram/p/graph_neural_network_3.html" rel="nofollow">图神经网络在计算机视觉上的应用</a> <ul dir="auto"> <li>5.6.1 <a href="https://arxiv.org/abs/1904.03751" rel="nofollow">点云分割</a></li> <li>5.6.2 <a href="https://openreview.net/forum?id=SJeXSo09FQ" rel="nofollow">点云生成</a></li> <li>5.6.3 <a href="https://www.cs.toronto.edu/~urtasun/publications/qi_etal_iccv17.pdf" rel="nofollow">RGBD图像分割</a></li> <li>5.6.4 <a href="https://visualqa.org/" rel="nofollow">视觉问答VQA</a></li> <li>5.6.5 <a href="https://arxiv.org/pdf/1803.08035.pdf" rel="nofollow">零次学习ZSL</a></li> </ul> </li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 5 章 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks">模型选择、欠拟合和过拟合</a> <ul dir="auto"> <li>5.1 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks">训练误差和泛化误差</a></li> <li>5.2 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks">模型选择</a></li> <li>5.3 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks">欠拟合和过拟合</a></li> <li>5.4 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks">多项式函数拟合实验</a></li> <li>5.5 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks">数值稳定性和模型初始化</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 6 章 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization">模型拟合与优化算法</a> <ul dir="auto"> <li>6.1 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.1_optimization-intro.ipynb">优化与深度学习</a></li> <li>6.2 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.2_gd-sgd.ipynb">梯度下降和随机梯度下降</a></li> <li>6.3 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.3_minibatch-sgd.ipynb">小批量随机梯度下降</a></li> <li>6.4 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.4_momentum.ipynb">动量法</a></li> <li>6.5 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.5_adagrad.ipynb">AdaGrad算法</a></li> <li>6.6 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.6_rmsprop.ipynb">RMSProp算法</a></li> <li>6.7 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.7_adadelta.ipynb">AdaDelta算法</a></li> <li>6.8 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter07_optimization/7.8_adam.ipynb">Adam算法</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> </ul> </li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">实战篇</h4><a id="user-content-实战篇" class="anchor" aria-label="Permalink: 实战篇" href="#实战篇"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b> <ul dir="auto"> <li>第 6 章 <a href="https://charmve.github.io/computer-vision-in-action/#/chapter6/chapter6" rel="nofollow">软件环境搭建与工具使用</a> <ul dir="auto"> <li>6.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.1_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA%E6%8C%87%E5%8D%97.md">深度学习环境搭建指南</a></li> <li>6.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md">Pytorch 基础使用介绍</a> <ul dir="auto"> <li>6.2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#621-tensors">Tensors</a></li> <li>6.2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#622-operations">Operations</a></li> <li>6.2.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#623-numpy%E6%A1%A5%E6%A2%81">Numpy桥梁</a></li> <li>6.2.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.2_Pytorch-%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%BB%8B%E7%BB%8D.md#624-cuda-tensors">CUDA Tensors</a></li> </ul> </li> <li>6.3 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/02_Python.ipynb">Python</a></li> <li>6.4 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/03_NumPy.ipynb">Numpy 基础使用</a></li> <li>6.5 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/04_Pandas.ipynb">Pandas 基础使用</a></li> <li>6.6 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/OpenCV-ImageStitching.ipynb">OpenCV 安装及基础使用</a></li> <li>6.7 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/01_Notebooks.ipynb">Jupyter Notebook 配置及基础使用</a></li> <li>6.8 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md">基本的图像操作和处理</a> <ul dir="auto"> <li>6.8.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#781-pil-python%E5%9B%BE%E5%83%8F%E5%A4%84%E7%90%86%E7%B1%BB%E5%BA%93">PIL：Python图像处理类库</a></li> <li>6.8.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#782-matplotlib">Matplotlib</a></li> <li>6.8.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#783-numpy">NumPy</a></li> <li>6.8.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#784-scipy">SciPy</a></li> <li>6.8.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter6_%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA/chapter6.8_%E5%9F%BA%E6%9C%AC%E7%9A%84%E5%9B%BE%E5%83%8F%E6%93%8D%E4%BD%9C%E5%92%8C%E5%A4%84%E7%90%86.md#785-%E9%AB%98%E7%BA%A7%E7%A4%BA%E4%BE%8B-%E5%9B%BE%E5%83%8F%E5%8E%BB%E5%99%AA">高级示例：图像去噪</a></li> </ul> </li> <li>6.9 <a href="https://blog.csdn.net/Charmve/article/details/107897468" rel="nofollow">实战项目 5 - 使用OpenCV进行图像全景拼接</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 7 章 <a href="https://github.com/Charmve/Semantic-Segmentation-PyTorch">经典卷积神经网络架构：原理与PyTorch实现</a> <ul dir="auto"> <li>7.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.1%20%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88LeNet%EF%BC%89.md">卷积神经网络（LeNet）</a></li> <li>7.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.2%20%E6%B7%B1%E5%BA%A6%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%EF%BC%88AlexNet%EF%BC%89.md">深度卷积神经网络（AlexNet）</a></li> <li>7.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.3%20%E4%BD%BF%E7%94%A8%E9%87%8D%E5%A4%8D%E5%85%83%E7%B4%A0%E7%9A%84%E7%BD%91%E7%BB%9C%EF%BC%88VGG%EF%BC%89.md">使用重复元素的网络（VGG）</a></li> <li>7.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.4%20%E5%90%AB%E5%B9%B6%E8%A1%8C%E8%BF%9E%E7%BB%93%E7%9A%84%E7%BD%91%E7%BB%9C%EF%BC%88GoogLeNet%EF%BC%89.md">含并行连结的网络（GoogLeNet）</a></li> <li>7.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.6_%E6%AE%8B%E5%B7%AE%E7%BD%91%E7%BB%9C-ResNet.md">残差网络（ResNet）</a></li> <li>7.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.7_%E4%BA%8C%E9%98%B6%E7%BD%91%E7%BB%9C%E7%BC%96%E7%A0%81%E8%A7%A3%E7%A0%81-UNet.md">二阶网络编码解码（U-Net）</a></li> <li>7.7 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.8_%E7%A8%A0%E5%AF%86%E8%BF%9E%E6%8E%A5%E7%BD%91%E7%BB%9C-DenseNet.md">稠密连接网络（DenseNet）</a></li> <li>7.8 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.9_%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E7%BD%91%E7%BB%9C-SegNet.md">语义分割网络（SegNet）</a></li> <li>7.9 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.9_%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2%E7%BD%91%E7%BB%9C-Mask-RCNN.md">实例分割网络（Mask-RCNN）</a></li> <li>7.10 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.10_%E5%8C%BA%E5%9F%9F%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C-RCNN.md">区域卷积神经网络（R-CNN）</a></li> <li>7.11 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/chapter7.11_%E5%85%A8%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C-FCN.md">全卷积网络（FCN）</a></li> <li>7.12 <a href="https://pjreddie.com/darknet/yolo/" rel="nofollow">YOLO: 实时目标检测</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 8 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86">著名数据集及基准</a> <ul dir="auto"> <li>8.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md">数据集</a> <ul dir="auto"> <li>8.1.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#811-%E5%B8%B8%E8%A7%81%E6%95%B0%E6%8D%AE%E9%9B%86">常见数据集</a> <ul dir="auto"> <li>8.1.1.1 <a href="https://image-net.org/" rel="nofollow">ImageNet</a></li> <li>8.1.1.2 <a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow">MNIST</a></li> <li>8.1.1.3 <a href="https://cocodataset.org/" rel="nofollow">COCO</a></li> <li>8.1.1.4 <a href="http://www.cs.toronto.edu/~kriz/cifar.html" rel="nofollow">CIFAR-10</a></li> </ul> </li> <li>8.1.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#812-pytorch%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E8%AF%BB%E5%8F%96%E6%96%B9%E6%B3%95%E7%AE%80%E4%BB%8B">Pytorch数据集及读取方法简介</a></li> <li>8.1.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#813-%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA%E7%AE%80%E4%BB%8B">数据增强简介</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.1_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86.md#%E6%80%BB%E7%BB%93">总结</a></li> </ul> </li> <li>8.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter8_%E8%91%97%E5%90%8D%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8F%8A%E5%9F%BA%E5%87%86/chapter8.2_%E5%9F%BA%E5%87%86BenchMark.md">基准测试</a></li> <li>8.3 <a href="/Charmve/computer-vision-in-action/blob/main/chapter8.3_%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87.md">评价指标</a></li> <li>8.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.12%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%EF%BC%88CIFAR-10%EF%BC%89.md">实战项目 6 - Kaggle比赛：图像分类（CIFAR-10）</a></li> <li>8.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.13%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E7%8B%97%E7%9A%84%E5%93%81%E7%A7%8D%E8%AF%86%E5%88%AB%EF%BC%88ImageNet%20Dogs%EF%BC%89.md">实战项目 7 - Kaggle比赛：狗的品种识别（ImageNet Dogs）</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 9 章 <a href="https://charmve.github.io/computer-vision-in-action/#/chapter9/chapter9" rel="nofollow">检测与分割实战项目</a> <ul dir="auto"> <li>9.1 语义分割 <ul dir="auto"> <li>9.1.1 <a href="https://github.com/Charmve/Semantic-Segmentation-PyTorch">语义分割 PyTorch 版</a></li> <li>9.1.2 <a href="/Charmve/computer-vision-in-action/blob/main">实战项目 8 - 基于PolarNet的点云端到端语义分割项目实战</a></li> </ul> </li> <li>9.2 目标检测 <ul dir="auto"> <li>9.2.1 常用网络</li> <li>9.2.2 <a href="/Charmve/computer-vision-in-action/blob/main">实战项目 9 - 基于PyTorch的YOLO5目标检测项目实战</a></li> </ul> </li> <li>9.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md">实例分割</a> <ul dir="auto"> <li>9.3.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md#931-%E5%B8%B8%E7%94%A8%E7%BD%91%E7%BB%9C">常用网络</a></li> <li>9.3.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md#932-%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE-8-%E5%AE%9E%E6%97%B6%E9%AB%98%E5%88%86%E8%BE%A8%E7%8E%87%E8%83%8C%E6%99%AF%E6%8A%A0%E5%9B%BE">实战项目 10 - 实时高分辨率背景抠图</a></li> <li>9.3.3 新方法：<a href="https://blog.csdn.net/Charmve/article/details/108915225" rel="nofollow">滑动窗口</a>, <a href="https://blog.csdn.net/Charmve/article/details/108892076" rel="nofollow">PointRend</a>, <a href="https://github.com/xieenze/PolarMask">PolarMask</a></li> </ul> </li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 10 章 <a href="https://charmve.github.io/computer-vision-in-action/#/chapter10/chapter10" rel="nofollow">计算机视觉课题研究初探</a> <ul dir="auto"> <li>10.1 <a href="https://blog.csdn.net/Charmve/article/details/108531735" rel="nofollow">手写字识别</a></li> <li>10.2 <a href="https://github.com/Charmve/Scene-Text-Detection">文本检测</a></li> <li>10.3 <a href="https://github.com/Charmve/Awesome-Lane-Detection">车道线检测</a> <ul dir="auto"> <li>10.3.1 <a href="https://github.com/Charmve/Awesome-Lane-Detection">常用网络</a></li> <li>10.3.2 <a href="https://blog.csdn.net/Charmve/article/details/116678477" rel="nofollow">实战项目 11 - 车道线检测项目实战</a></li> </ul> </li> <li>10.4 <a href="https://github.com/Charmve/Mirror-Glass-Detection">镜面检测</a></li> <li>10.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter10_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AF%BE%E9%A2%98%E7%A0%94%E7%A9%B6%E5%88%9D%E6%8E%A2/charpter10_5-%E5%9B%BE%E5%83%8F%E6%8A%A0%E5%9B%BE.md">图像抠图 Matting</a></li> <li>10.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter10_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AF%BE%E9%A2%98%E7%A0%94%E7%A9%B6%E5%88%9D%E6%8E%A2/charpter10_6-%E5%9B%BE%E5%83%8F%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87.md">图像超分辨率</a></li> <li>10.7 <a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter10_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AF%BE%E9%A2%98%E7%A0%94%E7%A9%B6%E5%88%9D%E6%8E%A2/charpter10_7-3D%E9%87%8D%E5%BB%BA.md">3D 重建</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> </ul> </li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">进阶篇</h4><a id="user-content-进阶篇" class="anchor" aria-label="Permalink: 进阶篇" href="#进阶篇"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b> <ul dir="auto"> <li>第 11 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3">可视化和理解卷积神经网络</a> <ul dir="auto"> <li>11.1 特征可视化</li> <li>11.2 倒置</li> <li>11.3 可视化数据梯度</li> <li>11.4 <a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter13_Understanding-and-Visualizing/Embeddings.ipynb" rel="nofollow">Embeddings</a></li> <li>11.5 对抗样本</li> <li>11.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3.3_neural-style.md">DeepDream 和风格迁移</a></li> <li>11.7 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md">实战项目 12: PyTorch 如何使用TensorBoard</a> <ul dir="auto"> <li>11.4.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1141-%E5%88%9B%E5%BB%BA-tensorboard">创建 TensorBoard</a></li> <li>11.4.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1142-%E5%86%99%E5%85%A5-tensorboard">写入 TensorBoard</a></li> <li>11.4.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1143-%E4%BD%BF%E7%94%A8-tensorboard-%E6%A3%80%E6%9F%A5%E6%A8%A1%E5%9E%8B">使用 TensorBoard 检查模型</a></li> <li>11.4.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1144-%E5%90%91-tensorboard-%E6%B7%BB%E5%8A%A0-projector">向 TensorBoard 添加 "Projector"</a></li> <li>11.4.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1145-%E4%BD%BF%E7%94%A8-tensorboard-%E8%B7%9F%E8%B8%AA%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83">使用 TensorBoard 跟踪模型训练</a></li> <li>11.4.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#1146-%E4%BD%BF%E7%94%A8-tensorboard-%E8%AF%84%E4%BC%B0%E8%AE%AD%E7%BB%83%E5%A5%BD%E7%9A%84%E6%A8%A1%E5%9E%8B">使用 TensorBoard 评估训练好的模型</a></li> <li>11.4.7 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md#%E5%B0%8F%E7%BB%93">案例总结</a></li> </ul> </li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 12 章 <a href="https://charmve.github.io/computer-vision-in-action/#/chapter6/chapter6" rel="nofollow">生成对抗模型</a> <ul dir="auto"> <li>12.1 Pixel RNN/CNN</li> <li>12.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_2-%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8Auto-encoder.md">自编码器 Auto-encoder</a></li> <li>12.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md">生成对抗网络 GAN</a> <ul dir="auto"> <li>12.3.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1231-%E6%A6%82%E8%BF%B0">概述</a></li> <li>12.3.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1232-gan%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%80%9D%E6%83%B3">GAN的基本思想</a></li> <li>12.3.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1233-gan%E6%B5%85%E6%9E%90">GAN浅析</a> <ul dir="auto"> <li>12.3.3.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#12331-gan%E7%9A%84%E5%9F%BA%E6%9C%AC%E7%BB%93%E6%9E%84">GAN的基本结构</a></li> <li>12.3.3.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#12332-gan%E7%9A%84%E8%AE%AD%E7%BB%83%E6%96%B9%E5%BC%8F">GAN的训练方式</a> <ul dir="auto"> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%85%B3%E4%BA%8E%E7%94%9F%E6%88%90%E5%99%A8">关于生成器</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%85%B3%E4%BA%8E%E5%88%A4%E5%88%AB%E5%99%A8">关于判别器</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%A6%82%E4%BD%95%E8%AE%AD%E7%BB%83">如何训练</a></li> </ul> </li> </ul> </li> <li>12.3.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#1234%E8%AE%AD%E7%BB%83%E7%9B%B8%E5%85%B3%E7%90%86%E8%AE%BA%E5%9F%BA%E7%A1%80">训练相关理论基础</a></li> <li>12.3.5 项目实战案例StyleGAN <ul dir="auto"> <li><a href="https://github.com/Charmve/VOGUE-Try-On">StyleGAN</a></li> <li><a href="https://blog.csdn.net/Charmve/article/details/115315353" rel="nofollow">StyleGAN 2.0</a></li> </ul> </li> <li>12.3.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3.3_neural-style.md">实战项目11 - 图像风格迁移</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%B0%8F%E7%BB%93">小结</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12.3_%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9CGAN.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>12.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md">变分自编码器 Variational Auto-encoder, VAE</a> <ul dir="auto"> <li>12.4.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1241-%E6%A6%82%E8%BF%B0">概述</a></li> <li>12.4.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1242-%E5%9F%BA%E6%9C%AC%E5%8E%9F%E7%90%86">基本原理</a> <ul dir="auto"> <li>12.4.2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1-%E5%AE%9A%E4%B9%89">定义</a></li> <li>12.4.2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#2-%E7%90%86%E8%AE%BA%E5%9F%BA%E7%A1%80%E4%B8%89%E8%A6%81%E7%B4%A0">理论基础：三要素</a></li> <li>12.4.2.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#3-%E6%8E%A8%E5%AF%BC%E8%BF%87%E7%A8%8B">推导过程</a></li> </ul> </li> <li>12.4.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1243-vae-vs-ae-%E5%8C%BA%E5%88%AB%E4%B8%8E%E8%81%94%E7%B3%BB">VAE v.s. AE 区别与联系</a></li> <li>12.4.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1244-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8%E7%9A%84%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0">变分自编码器的代码实现</a></li> <li>12.4.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#1245-%E5%8D%B7%E7%A7%AF%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8%E7%9A%84%E5%AE%9E%E7%8E%B0%E4%B8%8E%E7%AE%80%E5%8D%95%E5%BA%94%E7%94%A8">卷积变分自编码器的实现与简单应用</a></li> <li>12.4.6 <a href="https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life">实战项目 13 - 旧照片修复</a></li> <li>小结</li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter12-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E6%A8%A1%E5%9E%8B/chapter12_4-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8VAE.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>参考文献</li> </ul> </li> <li>第 13 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md">深度增强学习</a> <ul dir="auto"> <li>13.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#141-%E5%BC%95%E8%A8%80-%E5%A6%82%E4%BD%95%E8%A7%A3%E5%86%B3%E9%80%9A%E7%94%A8%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E7%9A%84%E9%9A%BE%E7%82%B9">引言-如何解决通用人工智能的难点</a></li> <li>13.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#142-%E4%BB%80%E4%B9%88%E6%98%AF%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0">什么是深度增强学习</a> <ul dir="auto"> <li>13.2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1421-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0">深度学习</a></li> <li>13.2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1422-%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0">增强学习</a></li> <li>13.2.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1423-%E4%BA%8C%E8%80%85%E7%9A%84%E8%9E%8D%E5%90%88">二者的融合</a></li> </ul> </li> <li>13.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#143-%E6%80%8E%E4%B9%88%E5%88%A9%E7%94%A8%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0%E8%A7%A3%E5%86%B3%E9%97%AE%E9%A2%98">怎么利用深度增强学习解决问题</a> <ul dir="auto"> <li>13.3.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1431-policy-based-drl">Policy-based DRL</a></li> <li>13.3.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1432-value-based-drl">Value-based DRL</a></li> <li>13.3.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#1433-model-based-drl">Model-based DRL</a></li> </ul> </li> <li>13.4 <a href="#144-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0%E5%9C%A8%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E4%B8%AD%E7%9A%84%E5%BA%94%E7%94%A8">深度增强学习在计算机视觉中的应用</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#%E5%B0%8F%E7%BB%93">小结</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0/chapter13-%E6%B7%B1%E5%BA%A6%E5%A2%9E%E5%BC%BA%E5%AD%A6%E4%B9%A0.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 14 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md">视频理解</a> <ul dir="auto"> <li>14.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#141-%E6%A6%82%E8%BF%B0">概述</a></li> <li>14.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#142-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3%E5%9C%BA%E6%99%AF%E4%B8%AD%E7%9A%84%E4%B8%BB%E8%A6%81%E9%97%AE%E9%A2%98">视频理解场景中的主要问题</a></li> <li>14.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#143-%E5%B8%B8%E7%94%A8%E6%95%B0%E6%8D%AE%E9%9B%86">常用数据集</a></li> <li>14.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#144-%E4%B8%BB%E6%B5%81%E6%96%B9%E6%B3%95%E4%B8%8E%E6%A8%A1%E5%9E%8B%E6%9E%B6%E6%9E%84">主流方法与模型架构</a></li> <li>14.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#145-%E6%8C%87%E6%A0%87-metrics">指标 METRICS</a></li> <li>14.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#146-%E5%8F%AF%E8%83%BD%E7%9A%84%E6%9C%AA%E6%9D%A5%E6%96%B9%E5%90%91">可能的未来方向</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#%E5%B0%8F%E7%BB%93">小结</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3.md#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 15 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0">迁移学习</a> <ul dir="auto"> <li>15.1 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#151-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0">概述</a> <ul dir="auto"> <li>15.1.1 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#1511-%E8%83%8C%E6%99%AF">背景</a></li> <li>15.1.2 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#1512-%E5%AE%9A%E4%B9%89%E5%8F%8A%E5%88%86%E7%B1%BB">定义及分类</a></li> <li>15.1.3 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#1513-%E5%85%B3%E9%94%AE%E7%82%B9">关键点</a></li> </ul> </li> <li>15.2 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#152-%E5%9F%BA%E4%BA%8E%E5%AE%9E%E4%BE%8B%E7%9A%84%E8%BF%81%E7%A7%BB">基于实例的迁移</a></li> <li>15.3 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#153-%E5%9F%BA%E4%BA%8E%E7%89%B9%E5%BE%81%E7%9A%84%E8%BF%81%E7%A7%BB">基于特征的迁移</a></li> <li>15.4 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#154-%E5%9F%BA%E4%BA%8E%E5%85%B1%E4%BA%AB%E5%8F%82%E6%95%B0%E7%9A%84%E8%BF%81%E7%A7%BB">基于共享参数的迁移</a></li> <li>15.5 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E6%A6%82%E8%BF%B0.md#155-%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%92%8C%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%BB%93%E5%90%88">深度学习和迁移学习结合</a></li> <li>15.7 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md">实战项目 14 - 蚂蚁和蜜蜂的分类问题</a> <ul dir="auto"> <li>15.7.1 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md#1571-%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E5%9C%A8%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E9%A2%86%E5%9F%9F%E7%9A%84%E5%BA%94%E7%94%A8">迁移学习在计算机视觉领域的应用</a></li> <li>15.7.2 <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md#1572-%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE-%E8%9A%82%E8%9A%81%E5%92%8C%E8%9C%9C%E8%9C%82%E7%9A%84%E5%88%86%E7%B1%BB%E9%97%AE%E9%A2%98">实战项目: 蚂蚁和蜜蜂的分类问题</a></li> </ul> </li> <li><a href="#%E5%B0%8F%E7%BB%93">小结</a></li> <li><a href="#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 16 章 <a href="/Charmve/computer-vision-in-action/blob/main/notebooks/chapter16_Attention/1_Attention.ipynb">计算机视觉中的注意力机制 Attention is All You Need</a> <ul dir="auto"> <li>16.1 概述</li> <li>16.2 Attention with RNNs</li> <li>16.3 <a href="https://mp.weixin.qq.com/s/nUd7YtCci1_AwQ4nOwK9bA" rel="nofollow">Self-attention 自注意力</a></li> <li>16.4 软注意力（soft-attention） <ul dir="auto"> <li>16.4.1 空间域注意力</li> <li>16.4.2 通道注意力</li> <li>16.4.3 Positional encoding</li> <li>16.4.4 混合域模型</li> <li>16.4.5 Masked attention</li> <li>16.4.6 Multi-head attention</li> </ul> </li> <li>16.5 强注意力（hard attention）</li> <li>16.6 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter16-%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6%20Attention%20is%20All%20You%20Need/chapter16_Attention-is-All-You-Need.md">Attention九层塔 - 注意力机制的九重理解</a></li> <li>小结</li> <li>参考文献</li> </ul> </li> <li>第 17 章 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md">跨界模型 Transformer</a> <ul dir="auto"> <li>17.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%B8%80%E6%80%9D%E6%83%B3%E5%92%8C%E6%A1%86%E5%9B%BE">思想和框图</a></li> <li>17.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%BA%8C%E5%AE%9E%E7%8E%B0%E7%BB%86%E8%8A%82">实现细节</a> <ul dir="auto"> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-1-Encoder">17.2.1 Encoder</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-2-Decoder">17.2.2 Decoder</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-3-Self-Attention">17.2.3 Self-Attention</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-4-Multi-Headed-Attention">17.2.4 Multi-Headed Attention</a></li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#2-5-Positional-Encoding">17.2.5 Positional Encoding</a></li> </ul> </li> <li>17.3 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%B8%89-%E5%BA%94%E7%94%A8%E4%BB%BB%E5%8A%A1%E5%92%8C%E7%BB%93%E6%9E%9C">应用任务和结果</a> <ul dir="auto"> <li>17.3.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-1-NLP%E9%A2%86%E5%9F%9F">NLP领域</a></li> <li>17.3.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-CV%E9%A2%86%E5%9F%9F">CV领域</a> <ul dir="auto"> <li>17.3.2.1 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-1-%E6%A3%80%E6%B5%8BDETR">检测DETR</a></li> <li>17.3.2.2 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-2-%E5%88%86%E7%B1%BBViT">分类ViT</a></li> <li>17.3.2.3 <a href="https://github.com/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-3-%E5%88%86%E5%89%B2SETR">分割SETR</a></li> <li>17.3.2.4 <a href="https://github.com/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#3-2-4-Deformable-DETR">Deformable-DETR</a></li> </ul> </li> </ul> </li> <li>17.4 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E5%9B%9B-%E4%BC%98%E7%82%B9%E5%8F%8A%E5%88%86%E6%9E%90">优点及分析</a></li> <li>17.5 <a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E4%BA%94-%E7%BC%BA%E7%82%B9%E5%8F%8A%E5%88%86%E6%9E%90">缺点及分析</a></li> <li><a href="https://blog.csdn.net/Charmve/article/details/115339803" rel="nofollow">实战项目 15 - 基于Transformer的视频实例分割网络VisTR (CVPR2021)</a></li> <li>小结</li> <li><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter17-%E8%B7%A8%E7%95%8C%E6%A8%A1%E5%9E%8B%20Transformer/chapter17_Transformer.md#%E5%85%AD-%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 18 章 <a href="https://mp.weixin.qq.com/s/e3c_-rs2rncmWhbm-cU5rA" rel="nofollow">知识蒸馏</a> <ul dir="auto"> <li>18.1 概要</li> <li>18.2 KD主要方法 <ul dir="auto"> <li>18.2.1 Logits(Response)-based Knowledge</li> <li>18.2.2 Feature-based Knowledge</li> <li>18.2.3 Relation-based Knowledge</li> </ul> </li> <li>18.3 知识蒸馏的应用 NLP-BERT</li> <li>18.4 常见疑问解答</li> <li><a href="https://mp.weixin.qq.com/s/RTkBQJ7Uj86Wxt7HmwWKzA" rel="nofollow">实战项目 16 - 支付宝CVPR细粒度视觉分类挑战赛夺冠方案解读</a></li> <li>小结</li> <li><a href="#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 19 章 <a href="https://blog.csdn.net/Charmve/article/details/107650487" rel="nofollow">Normalization 模型</a> <ul dir="auto"> <li>19.1 从Mini-Batch SGD说起</li> <li>19.2 Normalization到底是在做什么</li> <li>19.3 Batch Normalization如何做 <ul dir="auto"> <li>19.3.1 前向神经网络中的BN</li> <li>19.3.2 CNN网络中的BN</li> <li>19.3.3 Batch Norm的四大罪状</li> </ul> </li> <li>19.4 Layer Normalization、Instance Normalization及Group Normalization <ul dir="auto"> <li>19.4.1 Layer Normalization</li> <li>19.4.2 Instance Normalization</li> <li>19.4.3 Group Normalization</li> <li>19.4.4 用一个故事来总结</li> </ul> </li> <li>19.5 Normalization操作的Re-Scaling不变性</li> <li>19.6 Batch Normalization为何有效</li> <li>小结</li> <li><a href="#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li>第 20 章 <a href="https://mp.weixin.qq.com/s/e3c_-rs2rncmWhbm-cU5rA" rel="nofollow">模型压缩与裁剪</a> <ul dir="auto"> <li>20.1 概述</li> <li>20.2 模型压缩 <ul dir="auto"> <li>20.2.1 线性或非线性量化（1990~2014 - 至今）</li> <li>20.2.2 结构或非结构剪枝（1989~2014 - 至今</li> <li>20.2.3 网络结构搜索（2016 - 至今）</li> <li>20.2.4 权重矩阵的低秩分解（）</li> <li>20.2.5 知识蒸馏（2014-至今）</li> </ul> </li> <li>20.3 模型优化加速 <ul dir="auto"> <li>20.3.1 Op-Level 的快速算法</li> <li>20.3.2 Layer0-level 的快速算法</li> <li>20.3.3 硬件计算单元优化算法 <ul dir="auto"> <li>CPU、GPU和NPU</li> <li>ASIC 和 FPGA</li> <li>PIM（NDP）</li> </ul> </li> </ul> </li> <li>20.4 优化工具与库 <ul dir="auto"> <li>20.4.1 TensorRT Nvidia)</li> <li>20.4.2 TVM (Tensor Virtual Machine)</li> <li>20.4.3 Tensor Comprehension (Facebook)</li> <li>20.4.4 Distiller (Intel)</li> </ul> </li> <li>小结</li> <li><a href="#%E5%85%AD-%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE">参考文献</a></li> </ul> </li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">附录</h4><a id="user-content-附录" class="anchor" aria-label="Permalink: 附录" href="#附录"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b> <ul dir="auto"> <li>A 矩阵</li> <li>B <a href="https://mp.weixin.qq.com/s?__biz=MzIxMjg1Njc3Mw==&mid=2247484495&idx=1&sn=0bbb2094d93169baf20eedb284bc668f" rel="nofollow">常用激活函数总结</a></li> <li>C <a href="https://blog.csdn.net/Charmve/article/details/106089198" rel="nofollow">梯度下降法</a></li> <li>D <a href="https://blog.csdn.net/Charmve/article/details/107650479" rel="nofollow">深度学习调参技巧总结</a></li> </ul> </li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><a href="/Charmve/computer-vision-in-action/blob/main/docs/book_postscript.md">后记</a></h4><a id="user-content-后记" class="anchor" aria-label="Permalink: 后记" href="#后记"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b></li> <li><b><div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto"><a href="#%E5%8F%82%E8%80%83%E6%96%87%E7%8C%AE-1">参考文献</a></h4><a id="user-content-参考文献" class="anchor" aria-label="Permalink: 参考文献" href="#参考文献"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div></b></li> </ul> </li> </ul> <br> - <b>更新中 ...</b> <p dir="auto"><br><a href="#-%E6%9C%AC%E4%B9%A6%E7%9B%AE%E5%BD%95">收起</a></p> </td></tr> </tbody></table></markdown-accessiblity-table> </details> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🔍 文件浏览</h2><a id="user-content--文件浏览" class="anchor" aria-label="Permalink: 🔍 文件浏览" href="#-文件浏览"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">按书中内容先后顺序逐章阅读，或者选取特定章节祥读 📁 <code>docs/</code> <sup>1</sup>，动手实践章节代码，在代码文件 📁 <code>code/</code> <sup>2</sup> 下找到对应代码，本地测试或者Colab 📁 <code>notebooks/</code> <sup>3</sup> 在线测试。</p> <blockquote> <ul dir="auto"> <li>📁 <code>L0CV/</code> - 专为本项目建立的 <code>💮 L0CV</code> 包</li> <li>📁 <code>code/</code> - 书中完整代码</li> <li>📁 <code>datasets/</code> - 本书所用数据集 <ul dir="auto"> <li>📁 <code>images/</code> - 经典图像处理图片</li> </ul> </li> <li>📁 <code>docs/</code> - 全书按照一下几个篇目进行编写 <ul dir="auto"> <li>📁 <code>0_绪论/</code> - 全书绪论</li> <li>📁 <code>1_理论篇/</code> - 基本理论和算法基础</li> <li>📁 <code>2_实战篇/</code> - 项目实战教程</li> <li>📁 <code>3_进阶篇/</code> - 最新CV模型和算法</li> <li>📁 <code>附件/</code> - 所需数学和统计学知识</li> <li>📁 <code>img/</code> - 各章节所使用的插图</li> </ul> </li> <li>📁 <code>models/</code> - 封装可用的预训练模型</li> <li>📁 <code>notebooks/</code> - 🚩 全书 Colab notebook，可在线测试 <a href="https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a> <ul dir="auto"> <li>📁 <code>chapter01_neural-networks/</code> - 第 1 章 - 神经网络 Jupyter Notebook 实现</li> <li>📁 <code>chapter02_CNN/</code> - 第 2 章 - 卷积神经网络 Jupyter Notebook 实现</li> <li>📁 <code>chapter03_Image-Classification</code> - 第 3 章 - 图像分类 Jupyter Notebook 实现</li> <li>📁 <code>chapter04_recurrent-neural-networks/</code> - 第 4 章 - 递归神经网络 Jupyter Notebook 实现</li> <li>📁 <code>chapter05_graph-neural-network/</code> - 第 5 章 - 图神经网络 Jupyter Notebook 实现</li> <li>📁 <code>chapter07_optimization/</code> - 第 6 章 - 模型拟合与优化算法 Jupyter Notebook 实现</li> <li>📁 <code>chapter08_environment-setup-and-tool-use/</code> - 第 7 章 - 软件环境搭建与工具使用 Jupyter Notebook 实现</li> <li>📁 <code>chapter09_convolutional-neural-networks/</code> - 第 8 章 - 经典神经网络架构：原理与PyTorch实现 Jupyter Notebook 实现</li> <li>📁 <code>chapter12_practice-projects</code> - 第 12 章 - 计算机视觉课题研究初探 Jupyter Notebook 实现</li> <li>📁 <code>chapter13_Understanding-and-Visualizing/</code> - 第 13 章 - 可视化与理解卷积神经网络 Jupyter Notebook 实现</li> <li>📁 <code>chapter14_GAN/</code> - 第 14 章 - 生成对抗模型 Jupyter Notebook 实现</li> <li>📁 <code>chapter15_Transfer-Learning/</code> - 第 15 章 - 迁移学习 Jupyter Notebook 实现</li> <li><a href="https://github.com/Charmve/computer-vision-in-action#-以用促学先会后懂-"><img align="right" alt="Go for it!" src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/dd292873828228a753a9bd2de4576dbf8cc3902c/res/ui/footer-rocket.svg" height="260" title="Do what you like, and do it best!" style="max-width: 100%;"></a></li> <li>📁 <code>chapter16_Attention/</code> - 第 16 章 - 注意力机制 Jupyter Notebook 实现</li> <li>📁 <code>chapter17_Transformers/</code> - 第 17 章 - Transformers Jupyter Notebook 实现</li> <li>...</li> <li>📁 <code>imgs/</code> - Jupyter Notebook 中用到的图片</li> <li>📁 <code>docker/</code> - 为降低读者的学习成本，目前未进行搭建</li> <li>📁 <code>res/</code> - ui 图片及全书思维导图PDF</li> <li>📄 <code>README.md</code> - 全书介绍及目录</li> </ul> </li> </ul> </blockquote> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">L0CV DemoDay</h2><a id="user-content-l0cv-demoday" class="anchor" aria-label="Permalink: L0CV DemoDay" href="#l0cv-demoday"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/Charmve/computer-vision-in-action/blob/main/res/ui/demoday.png"><img src="/Charmve/computer-vision-in-action/raw/main/res/ui/demoday.png" title="L0CV Demo Day" style="max-width: 100%;"></a> <a href="https://github.com/Charmve/computer-vision-in-action/tree/main/L0CV-Universe">L0CV-Universe</a> </p> <p dir="auto">如果你也是从这里出发，在开源的项目中应用进去，并在标题下给出引用 <a href="https://github.com/Charmve/computer-vision-in-action/"><img src="https://camo.githubusercontent.com/1e1e70b4dc201de93f26a93c1e892ba23c641a273e17fac7ed9b021737c9ac70/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2df09f92ae202532304c3043562d6c69676874677265656e2e737667" alt="L0CV" title="L0CV" data-canonical-src="https://img.shields.io/badge/-💮 %20L0CV-lightgreen.svg" style="max-width: 100%;"></a>，您的项目将会在这里展现！</p> <div class="markdown-heading" dir="auto"><h5 align="center" tabindex="-1" class="heading-element" dir="auto"><i>以用促学，先会后懂。理解深度学习的最佳方法是学以致用。</i></h5><a id="user-content-以用促学先会后懂理解深度学习的最佳方法是学以致用" class="anchor" aria-label="Permalink: 以用促学，先会后懂。理解深度学习的最佳方法是学以致用。" href="#以用促学先会后懂理解深度学习的最佳方法是学以致用"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <tbody> <tr> <td> <div dir="auto"> <a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter02_CNN/Pytorch_MNIST.ipynb" rel="nofollow"><img width="%40" src="https://user-images.githubusercontent.com/29084184/128386334-e0273125-5d51-4e33-a6e7-f2e732fb0836.png" style="max-width: 100%;"></a> </div> </td> <td> <div dir="auto"> <a href="https://github.com/Charmve/computer-vision-in-action/blob/main/docs/2__实战篇/chapter7_经典卷积神经网络架构-原理与PyTorch实现/7.12%20实战Kaggle比赛：图像分类（CIFAR-10）.md"><img width="%40" src="https://user-images.githubusercontent.com/29084184/128386363-dc0c987c-b374-4e43-9e56-65c30f7a1899.png" style="max-width: 100%;"></a> </div> </td> <td> <div dir="auto"> <a href="https://github.com/Charmve/computer-vision-in-action/blob/main/docs/2_实战篇/chapter7_经典卷积神经网络架构-原理与PyTorch实现/7.13%20实战Kaggle比赛：狗的品种识别（ImageNet%20Dogs）.md"><img width="%40" src="https://user-images.githubusercontent.com/29084184/128386468-ca555572-a98d-44c5-bdef-442371322ee7.png" style="max-width: 100%;"></a> </div> </td> </tr> <tr> <td> <div dir="auto"> <a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Bringing-Old-Photo-Back-to-Life.ipynb" rel="nofollow"><img width="%40" src="https://user-images.githubusercontent.com/29084184/128386504-25a9f798-be68-430d-b771-bd8db607c151.png" style="max-width: 100%;"></a> </div> </td> <td> <div dir="auto"> <a href="https://github.com/Charmve/Awesome-Lane-Detection/tree/main/lane-detector"><img width="%40" src="https://user-images.githubusercontent.com/29084184/128386530-d64210f0-d903-4004-9f6c-eb480d326241.png" style="max-width: 100%;"></a> </div> </td> <td> <div dir="auto"> <a href="https://github.com/Charmve/computer-vision-in-action/blob/main/docs/3_进阶篇/chapter12-生成对抗模型/chapter12.3.3_neural-style.md"><img width="%40" src="https://user-images.githubusercontent.com/29084184/128386405-4223b171-a318-4f76-93b3-0fff016aa39f.png" style="max-width: 100%;"></a> </div> </td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">*《计算机视觉实战演练：算法与应用》V1.2 <em>部分项目还在更新中</em></p> <br> <p align="left" dir="auto"> <a href="https://github.com/Charmve/computer-vision-in-action"><img src="/Charmve/computer-vision-in-action/raw/main/res/ui/workswith1.png" title="Works with L0CV" width="120" style="max-width: 100%;"></a> </p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">实战项目</th> <th align="left">章节</th> <th align="center">Binder</th> <th align="center">Google Colab</th> </tr> </thead> <tbody> <tr> <td align="left"><a href="https://blog.csdn.net/Charmve/article/details/108531735" rel="nofollow">实战项目 1 - 手写字分类</a></td> <td align="left">第 1 章 - 神经网络</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main/docs/1_%E7%90%86%E8%AE%BA%E7%AF%87/chapter2_CNN/chapter2_CNN-in-Action.md">实战项目 2 - 动手搭建一个卷积神经网络</a></td> <td align="left">第 2 章 - 卷积神经网络</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="https://blog.csdn.net/charmve/category_9754344.html" rel="nofollow">实战项目 3 - 基于卷积神经网络的人脸表情识别</a></td> <td align="left">第 3 章 - 图像分类</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="http://mp.weixin.qq.com/s?__biz=MzIxMjg1Njc3Mw%3D%3D&chksm=97bef597a0c97c813e185e1bbf987b93d496c6ead8371364fd175d9bac46e6dcf7059cf81cb2&idx=1&mid=2247487293&scene=21&sn=89684d1c107177983dc1b4dca8c20a5b#wechat_redirect" rel="nofollow">实战项目 4 - 使用卷积神经网络对CIFAR10图片进行分类</a></td> <td align="left">第 3 章 - 图像分类</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="https://blog.csdn.net/Charmve/article/details/107897468" rel="nofollow">实战项目 5 - 使用OpenCV进行图像全景拼接</a></td> <td align="left">第 6 章 - 软件环境搭建与工具使用</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/OpenCV-ImageStitching.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter08_environment-setup-and-tool-use/OpenCV-ImageStitching.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.12%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB%EF%BC%88CIFAR-10%EF%BC%89.md">实战项目 6 - Kaggle比赛：图像分类（CIFAR-10）</a></td> <td align="left">第 8 章 - 著名数据集及基准</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_cifar10.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_cifar10.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter7_%E7%BB%8F%E5%85%B8%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E6%9E%B6%E6%9E%84-%E5%8E%9F%E7%90%86%E4%B8%8EPyTorch%E5%AE%9E%E7%8E%B0/7.13%20%E5%AE%9E%E6%88%98Kaggle%E6%AF%94%E8%B5%9B%EF%BC%9A%E7%8B%97%E7%9A%84%E5%93%81%E7%A7%8D%E8%AF%86%E5%88%AB%EF%BC%88ImageNet%20Dogs%EF%BC%89.md">实战项目 7 - Kaggle比赛：狗的品种识别（ImageNet Dogs）</a></td> <td align="left">第 8 章 - 著名数据集及基准</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_dog.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter10_dataset-and-benchmark/kaggle_dog.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main">实战项目 8 - 基于PolarNet的点云端到端语义分割项目实战</a></td> <td align="left">第 9 章 - 检测与分割实战项目</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main">实战项目 9 - 基于PyTorch的YOLO5目标检测项目实战</a></td> <td align="left">第 9 章 - 检测与分割实战项目</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main/docs/2_%E5%AE%9E%E6%88%98%E7%AF%87/chapter9_%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%88%86%E5%89%B2%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/9.3%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2.md#932-%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE-8-%E5%AE%9E%E6%97%B6%E9%AB%98%E5%88%86%E8%BE%A8%E7%8E%87%E8%83%8C%E6%99%AF%E6%8A%A0%E5%9B%BE">实战项目 10 - 实时高分辨率背景抠图</a></td> <td align="left">第 9 章 - 检测与分割实战项目</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="https://blog.csdn.net/Charmve/article/details/116678477" rel="nofollow">实战项目 11 - 车道线检测项目实战</a></td> <td align="left">第 10 章 - 计算机视觉课题研究初探</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3/chapter11-%E5%8F%AF%E8%A7%86%E5%8C%96%E5%92%8C%E7%90%86%E8%A7%A3.md">实战项目 12 - PyTorch 如何使用TensorBoard</a></td> <td align="left">第 13 章 - 可视化和理解</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="/Charmve/computer-vision-in-action/blob/main">实战项目 13 - 图像样式迁移</a></td> <td align="left">第 14 章 生成对抗模型</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb#/" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life">实战项目 14 - 旧照片修复</a></td> <td align="left">第 14 章 - 生成对抗模型</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Bringing-Old-Photo-Back-to-Life.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life">实战项目 15 - 动漫头像生成</a></td> <td align="left">第 14 章 - 生成对抗模型</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Anime-StyleGAN2.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="https://charmve.github.io/computer-vision-in-action/#/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3/chapter14-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3?id=_147-%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3%E9%A1%B9%E7%9B%AE%E5%AE%9E%E6%88%98" rel="nofollow">项目实战 16 - 视频理解项目实战 SlowFast + Multi-Moments in Time</a></td> <td align="left">第 16 章 - 视频理解</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter12_practice-projects/Bringing-Old-Photo-Back-to-Life.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="https://github.com/Charmve/computer-vision-in-action/tree/main/docs/3_%E8%BF%9B%E9%98%B6%E7%AF%87/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/chapter15_%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BA%94%E7%94%A8.md">实战项目 17 - 蚂蚁和蜜蜂的分类问题</a></td> <td align="left">第 17 章 - 迁移学习</td> <td align="center"><a href="https://nbviewer.jupyter.org/format/slides/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter15_Transfer-Learning/TL-ants-bees-classification.ipynb#/" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> </tr> <tr> <td align="left"><a href="https://blog.csdn.net/Charmve/article/details/115339803" rel="nofollow">实战项目 18 - 基于Transformer的视频实例分割网络VisTR (CVPR2021)</a></td> <td align="left">第 19 章 - 跨界模型 Transformer</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left"><a href="https://mp.weixin.qq.com/s/RTkBQJ7Uj86Wxt7HmwWKzA" rel="nofollow">实战项目 19 - 支付宝CVPR细粒度视觉分类挑战赛夺冠方案解读</a></td> <td align="left">第 20 章 - 知识蒸馏</td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="left">...</td> <td align="left">...</td> <td align="center">...</td> <td align="center"></td> </tr> </tbody> </table></markdown-accessiblity-table> <br> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🔑 如何食用</h2><a id="user-content--如何食用" class="anchor" aria-label="Permalink: 🔑 如何食用" href="#-如何食用"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <details><summary>🔎 详细攻略展开</summary> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">方式一 Jupyter Notebook (推荐方式 ✨)</h3><a id="user-content-方式一-jupyter-notebook-推荐方式-" class="anchor" aria-label="Permalink: 方式一 Jupyter Notebook (推荐方式 ✨)" href="#方式一-jupyter-notebook-推荐方式-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">1. 本地运行</h4><a id="user-content-1-本地运行" class="anchor" aria-label="Permalink: 1. 本地运行" href="#1-本地运行"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>依赖包安装</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip3 install -r requirements.txt"><pre class="notranslate"><code>pip3 install -r requirements.txt </code></pre></div> <ul dir="auto"> <li>安装 Jupyter</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python3 -m pip install --upgrade pip python3 -m pip install jupyter"><pre class="notranslate"><code>python3 -m pip install --upgrade pip python3 -m pip install jupyter </code></pre></div> <ul dir="auto"> <li>查看并运行jupyter</li> </ul> <p dir="auto">请在终端（Mac / Linux）或命令提示符（Windows）上运行以下命令：</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd notebooks jupyter notesbook"><pre><span class="pl-c1">cd</span> notebooks jupyter notesbook</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">2. 远程运行</h4><a id="user-content-2-远程运行" class="anchor" aria-label="Permalink: 2. 远程运行" href="#2-远程运行"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto">打开每章节首页，点击 <a href="https://colab.research.google.com/github/Charmve/computer-vision-in-action/blob/main/notebooks/chapter09_computer-vision/9.11_neural-style.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> 可直接打开 Google Colab ，点击 <code><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png"><img height="20" src="https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png" alt="Copy to Drive" title="Copy to Drive" style="max-width: 100%;"></a></code> [Copy to Drive] 即可在线运行测试。</p> </li> <li> <p dir="auto">点击 <a href="https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a> 也可在 <code>mybinder</code> 查看和在线运行。</p> </li> </ul> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/29084184/126031057-1e6ca67f-4475-47c1-a6ff-66375cb86908.png"><img src="https://user-images.githubusercontent.com/29084184/126031057-1e6ca67f-4475-47c1-a6ff-66375cb86908.png" width="60%" alt="Run on Colab" title="Run on Colab" style="max-width: 100%;"></a> <br> 图2 例子：12.3.3 样式迁移 </p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/29084184/126031137-14e349cd-1e89-4f98-9c56-0f1d3007ed89.png"><img src="https://user-images.githubusercontent.com/29084184/126031137-14e349cd-1e89-4f98-9c56-0f1d3007ed89.png" width="60%" alt="点击 Copy to Drive" style="max-width: 100%;"></a> <br>图3 例子：12.3.3 样式迁移 Colab 点击 <code><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png"><img height="20" src="https://user-images.githubusercontent.com/29084184/126463073-90077dff-fb7a-42d3-af6b-63c357d6db9f.png" alt="Copy to Drive" title="Copy to Drive" style="max-width: 100%;"></a></code> [Copy to Drive] </p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">方式二 使用 <code>/code</code></h3><a id="user-content-方式二-使用-code" class="anchor" aria-label="Permalink: 方式二 使用 /code" href="#方式二-使用-code"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">1. 运行环境 + L0CV 加载</h4><a id="user-content-1-运行环境--l0cv-加载" class="anchor" aria-label="Permalink: 1. 运行环境 + L0CV 加载" href="#1-运行环境--l0cv-加载"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>依赖包安装</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="sudo apt-get update pip3 install -r requirements.txt"><pre>sudo apt-get update pip3 install -r requirements.txt</pre></div> <ul dir="auto"> <li>创建 L0CV</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python3 setup.py"><pre>python3 setup.py</pre></div> <ul dir="auto"> <li>测试环境</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd code python3 L0CV_test.py"><pre><span class="pl-c1">cd</span> code python3 L0CV_test.py</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">2. 直接调用每个章节的代码测试</h4><a id="user-content-2-直接调用每个章节的代码测试" class="anchor" aria-label="Permalink: 2. 直接调用每个章节的代码测试" href="#2-直接调用每个章节的代码测试"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import L0CV"><pre><span class="pl-k">import</span> <span class="pl-v">L0CV</span></pre></div> <br> <p dir="auto"><a href="#-%E5%A6%82%E4%BD%95%E9%A3%9F%E7%94%A8">收起</a></p> </details> <markdown-accessiblity-table><table> <tbody> <tr> <td colspan="1" rowspan="1"> <div dir="auto"> <div dir="auto"> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">每一小节都是可以运行的 <a href="https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/" rel="nofollow">Jupyter 记事本</a></h2><a id="user-content-每一小节都是可以运行的-jupyter-记事本" class="anchor" aria-label="Permalink: 每一小节都是可以运行的 Jupyter 记事本" href="#每一小节都是可以运行的-jupyter-记事本"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">你可以自由修改代码和超参数来获取及时反馈，从而积累深度学习的实战经验。</p> </div> <div align="center" dir="auto"> <div dir="auto"> <a href="https://charmve.github.io/computer-vision-in-action/#/README?id=🔎-如何食用" rel="nofollow"> <img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/laptop_jupyter.png" width="60" style="max-width: 100%;"> <p dir="auto">Run<br>locally</p> </a> </div> <div dir="auto"> <a href="https://charmve.github.io/computer-vision-in-action/#/README?id=🔎-如何食用" rel="nofollow"> <img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/colab.png" width="60" style="max-width: 100%;"> <p dir="auto">Google<br>Colab</p> </a> </div> </div> </div> </td> <td> <div dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/notebook.gif"><img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/notebook.gif" data-animated-image="" style="max-width: 100%;"></a> </div> </td> </tr> </tbody> </table></markdown-accessiblity-table> <markdown-accessiblity-table><table> <tbody> <tr> <td colspan="3" rowspan="1"> <div dir="auto"> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">公式 + 图示 + 代码</h2><a id="user-content-公式--图示--代码" class="anchor" aria-label="Permalink: 公式 + 图示 + 代码" href="#公式--图示--代码"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">我们不仅结合文字、公式和图示来阐明深度学习里常用的模型和算法，还提供代码来演示如何从零开始实现它们，并使用真实数据来提供一个交互式的学习体验。</p> </div> </td> </tr> <tr> <td> <div dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/eq.jpg"><img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/eq.jpg" style="max-width: 100%;"></a> </div> </td> <td> <div dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/figure.jpg"><img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/figure.jpg" style="max-width: 100%;"></a> </div> </td> <td> <div dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/code.jpg"><img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/frontpage/code.jpg" style="max-width: 100%;"></a> </div> </td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">❓ 常见问题</h2><a id="user-content--常见问题" class="anchor" aria-label="Permalink: ❓ 常见问题" href="#-常见问题"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>在线教程页面无法打开</strong>：</p> <p dir="auto">测试中存在部分人打不开在线教程的情况。</p> <p dir="auto">部分小伙伴反馈尝试切换浏览器后可以正常打开了，如果仍然不行，最有效的解决办法是科学上网。</p> </li> <li> <p dir="auto"><strong>无法加载图片的解决办法</strong>：</p> <p dir="auto">根本解决办法还是科学上网，也可以尝试修改host文件看下是否能解决。</p> <p dir="auto">解决方案: 修改host文件 <code><a href="https://www.jianshu.com/p/25e5e07b2464" rel="nofollow"><img height="20" src="https://user-images.githubusercontent.com/29084184/126457822-d431fb90-6b9e-4a4e-bedc-3c598e9e2ee2.png" alt="Apple" title="Apple" style="max-width: 100%;"></a></code> Mac <code><a href="https://blog.csdn.net/u011583927/article/details/104384169" rel="nofollow"><img height="20" src="https://user-images.githubusercontent.com/29084184/126457902-0c1a71c2-f920-45a1-a143-ce8b5c435fe7.png" alt="Win10" title="Win10" style="max-width: 100%;"></a></code> Windows</p> </li> <li> <p dir="auto"><strong>公式无法正常显示解决办法</strong>：</p> <p dir="auto">GitHub中的Markdown原生是不支持LATEX公式显示的，如果你喜欢在本项目中直接浏览教程，可以安装Chrome的<code>MathJax Plugin for Github</code>插件让大部分公式正常显示。而docs文件夹已经利用docsify被部署到了GitHub Pages上，包含公式的章节强力建议使用 <a href="https://charmve.github.io/computer-vision-in-action" rel="nofollow">《计算机视觉实战演练：算法与应用》 在线阅读</a> 进行学习。</p> <p dir="auto">当然如果你还想跑一下运行相关代码的话还是得把本项目clone下来，然后运行code文件夹下相关代码。</p> </li> <li> <p dir="auto"><strong>Jupyter Notebook 无法在 GitHub 上呈现？</strong> <a href="https://leaherb.com/notebook_wont_render_use_nbviewer/" rel="nofollow">使用 nbviewer</a>。 <a href="https://mybinder.org/v2/gh/Charmve/computer-vision-in-action/main/notebooks/" rel="nofollow"><img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"></a></p> </li> </ul> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">致谢</h2><a id="user-content-致谢" class="anchor" aria-label="Permalink: 致谢" href="#致谢"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://maiweiai.github.io/" rel="nofollow"><img src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/main/res/ui/maiwei_ai.png" height="36" alt="迈微AI研习社" title="迈微AI研习社" style="max-width: 100%;"> </a> <a href="https://madewithml.com/" rel="nofollow"><img src="https://camo.githubusercontent.com/c194bbe5708823e7ff971fc7bd3097b2545189e23914d0a7f2b4469dfdea1ac9/68747470733a2f2f6d616465776974686d6c2e636f6d2f7374617469632f696d616765732f6c6f676f2e706e67" height="30" alt="Made With ML" title="Made With ML" data-canonical-src="https://madewithml.com/static/images/logo.png" style="max-width: 100%;"> </a> <a href="https://www.epubit.com/" rel="nofollow"><img src="https://camo.githubusercontent.com/4fd02aada8101ea7a8fa321dfda56c3035521d6d578346519a27b7e6ba1a09b6/68747470733a2f2f63646e2e707470726573732e636e2f707562636c6f75642f332f6170702f30373138413642302f636f7665722f323031393132303442443534303039412e706e67" height="30" alt="异步社区" title="异步社区" data-canonical-src="https://cdn.ptpress.cn/pubcloud/3/app/0718A6B0/cover/20191204BD54009A.png" style="max-width: 100%;"> </a> <a href="https://360.cn" rel="nofollow"><img src="https://camo.githubusercontent.com/464a0e094d37db54cf626e759b452e9b2177fbb4df39522af29652645059104c/68747470733a2f2f70332e73736c2e7168696d672e636f6d2f743031316539346630623965643865363662302e706e67" height="36" alt="奇虎360" title="奇虎360" data-canonical-src="https://p3.ssl.qhimg.com/t011e94f0b9ed8e66b0.png" style="max-width: 100%;"> </a></p> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">参考文献</h2><a id="user-content-参考文献-1" class="anchor" aria-label="Permalink: 参考文献" href="#参考文献-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">感谢前人的杰出工作，我才得以写出此书。感谢 <a href="/Charmve/computer-vision-in-action/blob/main/docs/REFERENCE.md"><b>参考文献</b></a> 中列出及未列出的，所有对此开源工作有帮助的前辈！</p> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">LICENSE</h2><a id="user-content-license" class="anchor" aria-label="Permalink: LICENSE" href="#license"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://www.apache.org/licenses/LICENSE-2.0" rel="nofollow"><img src="https://camo.githubusercontent.com/5db08927dc42829460b40746b7dda8c78a39aefab4bda202bd1deee744d9aed0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d7265643f6c6f676f3d617061636865" alt="Code License" data-canonical-src="https://img.shields.io/badge/license-Apache%202.0-red?logo=apache" style="max-width: 100%;"></a> <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/" rel="nofollow"><img alt="知识共享许可协议" src="https://camo.githubusercontent.com/834a7753f25e27209ceb7b8677860c42d41cc282fd7df6a5b014b13e457b5be8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732532306c6963656e73652d434325323042592d2d4e432d2d5341253230342e302d677265656e3f6c6f676f3d6372656174697665636f6d6d6f6e73" title="CC BY--NC--SA 4.0" data-canonical-src="https://img.shields.io/badge/docs%20license-CC%20BY--NC--SA%204.0-green?logo=creativecommons" style="max-width: 100%;"></a></p> <ul dir="auto"> <li> <p dir="auto"><code>L0CV</code>代码部分采用 <a href="https://www.apache.org/licenses/LICENSE-2.0" rel="nofollow">Apache 2.0协议</a> 进行许可，包括名为 <b><em>L0CV</em></b> 的原创第三方库、<code>/code</code>和<code>/notebook</code>下的源代码。遵循许可的前提下，你可以自由地对代码进行修改，再发布，可以将代码用作商业用途。但要求你：</p> <ul dir="auto"> <li><strong>署名</strong>：在原有代码和衍生代码中，保留原作者署名及代码来源信息。</li> <li><strong>保留许可证</strong>：在原有代码和衍生代码中，保留<code>Apache 2.0</code>协议文件。</li> </ul> </li> <li> <p dir="auto"><code>L0CV</code>文档部分采用 <a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">知识共享署名 4.0 国际许可协议</a> 进行许可。 遵循许可的前提下，你可以自由地共享，包括在任何媒介上以任何形式复制、发行本作品，亦可以自由地演绎、修改、转换或以本作品为基础进行二次创作。但要求你：</p> <ul dir="auto"> <li><strong>署名</strong>：应在使用本文档的全部或部分内容时候，注明原作者及来源信息。</li> <li><strong>非商业性使用</strong>：不得用于商业出版或其他任何带有商业性质的行为。如需商业使用，请联系作者。</li> <li><strong>相同方式共享的条件</strong>：在本文档基础上演绎、修改的作品，应当继续以知识共享署名 4.0国际许可协议进行许可。</li> </ul> </li> </ul> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">👥 社区互助</h2><a id="user-content--社区互助" class="anchor" aria-label="Permalink: 👥 社区互助" href="#-社区互助"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">如果您在使用的过程中碰到问题，可以通过下面几个途径寻求帮助，同时我们也鼓励资深用户通过下面的途径给新人提供帮助。</p> <ul dir="auto"> <li> <p dir="auto">通过 <a href="https://github.com/Charmve/computer-vision-in-action/discussions"><img src="https://camo.githubusercontent.com/bd30bd702c9ae10b961161870494791281e0aacb5c71942b25bce822df1c6cb5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4769744875622d44697363757373696f6e732d677265656e3f6c6f676f3d676974687562" alt="GitHub Discuss" data-canonical-src="https://img.shields.io/badge/GitHub-Discussions-green?logo=github" style="max-width: 100%;"></a> 提问时，建议使用 <code>Q&A</code> 标签。</p> </li> <li> <p dir="auto">通过 <a href="http://stackoverflow.com/questions/tagged/L0CV" rel="nofollow"><img src="https://camo.githubusercontent.com/acd86085b1c32dac2a3d87dd784e4e3727f295a276aa1e934d8cd16e708b0aa5/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d537461636b2532304f766572666c6f772d677261793f6c6f676f3d737461636b6f766572666c6f77" alt="Stack Overflow" data-canonical-src="https://img.shields.io/badge/-Stack%20Overflow-gray?logo=stackoverflow" style="max-width: 100%;"></a> 或者 <a href="https://segmentfault.com/t/L0CV" rel="nofollow"><img src="https://camo.githubusercontent.com/f928c398aed6888ce8d686d66dea0a0b9177204299bbd07b93619bb4e3cb7a3b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f2d5365676d656e742532304661756c742d677261793f6c6f676f3d6d6f6e676f6462" alt="Segment Fault" data-canonical-src="https://img.shields.io/badge/-Segment%20Fault-gray?logo=mongodb" style="max-width: 100%;"></a> 提问时，建议加上 <code>L0CV</code> 标签。</p> </li> <li> <p dir="auto"><a href="https://segmentfault.com/t/L0CV" rel="nofollow"><img src="https://camo.githubusercontent.com/5ac4815762fce97e10db0d521a995910fbb61a021a159c657906ca9a11bef0ba/68747470733a2f2f696d672e736869656c64732e696f2f62616467652fe5beaee4bfa12d4c3043562d677265656e3f6c6f676f3d776563686174" alt="Segment Fault" data-canonical-src="https://img.shields.io/badge/微信-L0CV-green?logo=wechat" style="max-width: 100%;"></a> 微信、知乎、微博开话题可以生成tag，如微信聊天、朋友圈加 <code>#L0CV</code> 可话题交流。</p> </li> <li> <p dir="auto">L0CV 读者微信交流群 (加我微信Yida_Zhang2，备注：L0CV-高校/企业-称呼)</p> </li> </ul> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">💖 Support this project</h2><a id="user-content--support-this-project" class="anchor" aria-label="Permalink: 💖 Support this project" href="#-support-this-project"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Donating to help me continue working on this project. I'm appreciate all you in <a href="https://charmve.github.io/sponsor.html" rel="nofollow">backer list</a>. <a href="https://charmve.github.io/sponsor.html" rel="nofollow"><img src="https://camo.githubusercontent.com/8e9605cadb9ef014e1f042da9c91069e3e91fc66913785445583e70bbdcd3934/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7374796c652d2d3565626130302e7376673f6c6162656c3d436f66666565266c6f676f3d6b6f2d6669267374796c653d736f6369616c" alt="BuymeaCoffee" data-canonical-src="https://img.shields.io/badge/style--5eba00.svg?label=Coffee&logo=ko-fi&style=social" style="max-width: 100%;"></a></p> <p dir="auto"><a href="https://charmve.github.io/sponsor.html" rel="nofollow"><img src="https://camo.githubusercontent.com/8ca6cbc30815bc3ef458755d26604c424652dc18a51df3b3d708126667c31c91/68747470733a2f2f63646e2e6275796d6561636f666665652e636f6d2f627574746f6e732f76322f64656661756c742d7265642e706e67" alt="Buy Me A Coffee" width="180" data-canonical-src="https://cdn.buymeacoffee.com/buttons/v2/default-red.png" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">CONTRIBUTION</h2><a id="user-content-contribution" class="anchor" aria-label="Permalink: CONTRIBUTION" href="#contribution"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">作者会尽最大努力来确保书中内容的准确性，但难免会存在疏漏与不妥之处。欢迎您将发现的问题反馈给我们，帮助提升本开放资源的质量。</p> <div align="center" dir="auto"> <markdown-accessiblity-table><table> <tbody><tr> <td> <div dir="auto"> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Help us make these docs great!</h3><a id="user-content-help-us-make-these-docs-great" class="anchor" aria-label="Permalink: Help us make these docs great!" href="#help-us-make-these-docs-great"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">All <b><i>VC-action</i></b> docs are open source. See something that's wrong or unclear? <a href="https://github.com/Charmve/computer-vision-in-action/pulls">Submit a pull request</a>.</p> <a href="https://github.com/Charmve/computer-vision-in-action/edit/main/README.md"> Make a contribution </a> <br><p dir="auto">Or, <a href="https://github.com/Charmve/computer-vision-in-action/blob/main/CONTRIBUTING.md">learn how to contribute.</a></p> </div> <div dir="auto"> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto"> Still need help? </h3><a id="user-content-still-need-help" class="anchor" aria-label="Permalink: Still need help? " href="#still-need-help"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <a id="user-content-ask-community" href="https://github.com/Charmve/computer-vision-in-action/discussions"> Ask our community </a> <a id="user-content-contact-us" href="https://github.com/Charmve"> Contact support </a> </div> </td> </tr> </tbody></table></markdown-accessiblity-table> </div> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Use this bibtex to cite this repository:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{computer-vision-in-action, title={计算机视觉实战演练：算法与应用（Computer Vision in Action）}, author={Charmve}, year={2021.06}, publisher={Github}, journal={GitHub repository}, howpublished={\url{https://github.com/Charmve/computer-vision-in-action}}, }"><pre class="notranslate"><code>@misc{computer-vision-in-action, title={计算机视觉实战演练：算法与应用（Computer Vision in Action）}, author={Charmve}, year={2021.06}, publisher={Github}, journal={GitHub repository}, howpublished={\url{https://github.com/Charmve/computer-vision-in-action}}, } </code></pre></div> <p dir="auto"><a href="#-%E4%BB%A5%E7%94%A8%E4%BF%83%E5%AD%A6%E5%85%88%E4%BC%9A%E5%90%8E%E6%87%82-">Index</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Stargazers Over Time</h2><a id="user-content-stargazers-over-time" class="anchor" aria-label="Permalink: Stargazers Over Time" href="#stargazers-over-time"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div dir="auto"> <p align="center" dir="auto"> <b>Awesome!</b> <a href="https://github.com/Charmve/computer-vision-in-action"><em>Charmve/computer-vision-in-action</em></a> was created 2 months ago and now has <b>1546</b> stars. </p> </div> <p dir="auto"><a href="https://starchart.cc/Charmve/computer-vision-in-action" rel="nofollow"><img src="https://camo.githubusercontent.com/59ba05f844823eecca0032894edec61f18b5716b8f3eb391d1eb990b14467607/68747470733a2f2f7374617263686172742e63632f436861726d76652f636f6d70757465722d766973696f6e2d696e2d616374696f6e2e737667" alt="Stargazers over time" data-canonical-src="https://starchart.cc/Charmve/computer-vision-in-action.svg" style="max-width: 100%;"></a></p> <p dir="auto"><br><a href="https://github.com/Charmve/computer-vision-in-action#-以用促学先会后懂-"><img align="right" alt="Go for it!" src="https://raw.githubusercontent.com/Charmve/computer-vision-in-action/dd292873828228a753a9bd2de4576dbf8cc3902c/res/ui/footer-rocket.svg" height="220" title="Do what you like, and do it best!" style="max-width: 100%;"></a> <br></p> <p align="center" dir="auto">Feel free to ask any questions, open a PR if you feel something can be done differently!</p> <div class="markdown-heading" dir="auto"><h2 align="center" tabindex="-1" class="heading-element" dir="auto">🌟 Star this repository 🌟</h2><a id="user-content--star-this-repository-" class="anchor" aria-label="Permalink: 🌟 Star this repository 🌟" href="#-star-this-repository-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p align="center" dir="auto">Created by <a href="https://github.com/Charmve">Charmve</a> & <a 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