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0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Book-online</span></span></h3><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-621e7d5388f64a199634440eb8567eb7" href="https://github.com/ChristosChristofidis/awesome-deep-learning"><div><div class="notion-bookmark-title">GitHub - ChristosChristofidis/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities.</div><div class="notion-bookmark-description">You can also find the most cited deep learning papers from here ImageNet Classification with Deep Convolutional Neural Networks Using Very Deep Autoencoders for Content Based Image Retrieval Learning Deep Architectures for AI CMU&#x27;s list of papers Neural Networks for Named Entity Recognition zip Training tricks by YB Geoff Hinton&#x27;s</div><div class="notion-bookmark-link"><div 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Learning</div><div class="notion-bookmark-description">The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. 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AMiner</a></div><div class="notion-text notion-block-5714e190901848d78cebf6e032a1f1a0"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://www.aminer.cn/pub/53e9ba84b7602d97046a6f5b/affective-tutoring-systems-enhancing-e-learning-with-the-emotional-awareness-of-a">Affective Tutoring Systems: Enhancing e-Learning with the Emotional Awareness of a Human Tutor | AMiner - AMiner</a></div><h4 class="notion-h notion-h3 notion-h-indent-1 notion-block-b93597695be04f4c8a31b284c62ac34b" data-id="b93597695be04f4c8a31b284c62ac34b"><span><div id="b93597695be04f4c8a31b284c62ac34b" class="notion-header-anchor"></div><a class="notion-hash-link" href="#b93597695be04f4c8a31b284c62ac34b" title="Tensorflow"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Tensorflow</span></span></h4><div class="notion-text notion-block-939d74e3e671474abdf14a0a4a639cb5"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/machinelearningmindset/TensorFlow-Course#why-use-tensorflow">machinelearningmindset/TensorFlow-Course: Simple and ready-to-use tutorials for TensorFlow</a></div><div class="notion-text notion-block-eff87f2f2e6e4679a740198155d6eb19"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="http://www.tensorfly.cn/tfdoc/tutorials/overview.html">TensorFlow 官方文档中文版</a></div><div class="notion-text notion-block-966466b54e734f0681f115b807e3b4ce"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://tensorflow.google.cn/js/demos">TensorFlow.js demos  |  TensorFlow</a></div><div class="notion-text notion-block-4e175718751f45a79b6632807a895610"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://tensorflow.google.cn/beta/guide/effective_tf2">Effective TensorFlow 2.0  |  TensorFlow Core</a></div><div class="notion-text notion-block-5a3fff1fce874ed6b7eb263d3fb65e29"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://tensorflow.google.cn/beta/guide/upgrade">Upgrade code to TensorFlow 2.0  |  TensorFlow Core</a></div><div class="notion-text notion-block-e4f13455dc5a4ecd916aed00aa0b8522"><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/aymericdamien/TensorFlow-Examples">GitHub - 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            <title><![CDATA[Tools]]></title>
            <link>https://ai-charlie.vercel.app/article/Tools</link>
            <guid>https://ai-charlie.vercel.app/article/Tools</guid>
            <pubDate>Thu, 13 Jul 2023 00:00:00 GMT</pubDate>
            <description><![CDATA[开发笔记文档]]></description>
            <content:encoded><![CDATA[<div id="container" class="mx-auto undefined"><main class="notion light-mode notion-page notion-block-30c7b63e92f6446bada6d0a800173097"><div class="notion-viewport"></div><div class="notion-collection-page-properties"></div><div class="notion-table-of-contents notion-gray notion-block-7c1526c9d8c54d17b9a29aa894133383"></div><div class="notion-row notion-block-99048025eba94813ba6c5c1ad9eef9d7"><div class="notion-column notion-block-7034934b097c4728a5951b745ea865fb" style="width:calc((100% - (1 * min(32px, 4vw))) * 0.5)"><h3 class="notion-h notion-h2 notion-block-088fff8de7fc432fa1bcf9d86bbcd882" data-id="088fff8de7fc432fa1bcf9d86bbcd882"><span><div id="088fff8de7fc432fa1bcf9d86bbcd882" class="notion-header-anchor"></div><a class="notion-hash-link" href="#088fff8de7fc432fa1bcf9d86bbcd882" title="Usual"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Usual</span></span></h3><a class="notion-page-link notion-block-085dec16e5054371b03469e03d526236" href="/085dec16e5054371b03469e03d526236"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🌩️">🌩️</span></div><span class="notion-page-title-text">mongodb</span></span></a><a class="notion-page-link notion-block-e3b023cbaa7f4ea59e07d839db719094" href="/e3b023cbaa7f4ea59e07d839db719094"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🌝">🌝</span></div><span class="notion-page-title-text">OH-MY-POSH</span></span></a><a class="notion-page-link notion-block-53e132b932a645559a39656dedb7704d" href="/53e132b932a645559a39656dedb7704d"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🎍">🎍</span></div><span class="notion-page-title-text">Endnote GB/T文献格式下载</span></span></a><a class="notion-page-link notion-block-1e97d25449f24f5e9a0017914a1ae1c2" href="/1e97d25449f24f5e9a0017914a1ae1c2"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🚗">🚗</span></div><span class="notion-page-title-text">csdn下载卡密</span></span></a><a class="notion-page-link notion-block-693b73db80f14a79b95171b8fa2a71be" href="/693b73db80f14a79b95171b8fa2a71be"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🔥">🔥</span></div><span class="notion-page-title-text">火狐浏览器标签页高度调整</span></span></a><h3 class="notion-h notion-h2 notion-block-b3281100e81a47249f9affe523703996" data-id="b3281100e81a47249f9affe523703996"><span><div id="b3281100e81a47249f9affe523703996" class="notion-header-anchor"></div><a class="notion-hash-link" href="#b3281100e81a47249f9affe523703996" title="Docker"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Docker</span></span></h3><a class="notion-page-link notion-block-0733bf0c8a2247bbabd997c5a56def5b" href="/0733bf0c8a2247bbabd997c5a56def5b"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🧸">🧸</span></div><span class="notion-page-title-text">docker</span></span></a><a class="notion-page-link notion-block-69a64f26b0484ff09e27a1414de05b76" href="/69a64f26b0484ff09e27a1414de05b76"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="⛪">⛪</span></div><span class="notion-page-title-text">Docker Desktop</span></span></a></div><div class="notion-spacer"></div><div class="notion-column notion-block-b9e93830ba954d28a4b7d89de6633af8" style="width:calc((100% - (1 * min(32px, 4vw))) * 0.5)"><h3 class="notion-h notion-h2 notion-block-29b60d54af9744d28484c3a78a29fb79" data-id="29b60d54af9744d28484c3a78a29fb79"><span><div id="29b60d54af9744d28484c3a78a29fb79" class="notion-header-anchor"></div><a class="notion-hash-link" href="#29b60d54af9744d28484c3a78a29fb79" title="python"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">python</span></span></h3><a class="notion-page-link notion-block-4085adb1f6f5440a8b047289831e7195" href="/4085adb1f6f5440a8b047289831e7195"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🖥️">🖥️</span></div><span class="notion-page-title-text"><b>Anaconda </b></span></span></a><a class="notion-page-link notion-block-f08862b4052a4bf9a48e74f51d05bc50" href="/f08862b4052a4bf9a48e74f51d05bc50"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🗓️">🗓️</span></div><span class="notion-page-title-text">设置当前项目pypi镜像地址</span></span></a><h3 class="notion-h notion-h2 notion-block-f47e0fab326247dcb5afdad9d0b3bd4e" data-id="f47e0fab326247dcb5afdad9d0b3bd4e"><span><div id="f47e0fab326247dcb5afdad9d0b3bd4e" class="notion-header-anchor"></div><a class="notion-hash-link" href="#f47e0fab326247dcb5afdad9d0b3bd4e" title="web develop"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">web develop</span></span></h3><a class="notion-page-link notion-block-287fba5aa7034977a3638f21564bbf24" href="/287fba5aa7034977a3638f21564bbf24"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-image"><img class="notion-page-title-icon notion-page-icon" src="https://png.pngtree.com/svg/20170710/12175a889e.svg" alt="Frontend" loading="lazy" decoding="async"/></div><span class="notion-page-title-text">Frontend</span></span></a><a class="notion-page-link notion-block-7432a6520404488bbaeca17118754dd8" href="/7432a6520404488bbaeca17118754dd8"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🥇">🥇</span></div><span class="notion-page-title-text">钉钉提供的内网穿透工具</span></span></a><a class="notion-page-link notion-block-2180fa1472f6482bb83cc5db0d88c85a" href="/2180fa1472f6482bb83cc5db0d88c85a"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🌀">🌀</span></div><span class="notion-page-title-text">好用的docker镜像网站</span></span></a><div class="notion-blank notion-block-613b4efd3bc64bb2ad9d500084f37c45"> </div></div><div class="notion-spacer"></div></div><a class="notion-page-link notion-block-c49a08fb39404876b7d22076533d5392" href="/c49a08fb39404876b7d22076533d5392"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🕖">🕖</span></div><span class="notion-page-title-text">Generate requirements.txt file</span></span></a><a class="notion-page-link notion-block-0e804056c22346f3a588f5a1d2e7f0df" href="/0e804056c22346f3a588f5a1d2e7f0df"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-image"><svg class="notion-page-title-icon notion-page-icon" alt="好用的设计工具" viewBox="0 0 30 30" width="16"><path d="M16,1H4v28h22V11L16,1z M16,3.828L23.172,11H16V3.828z M24,27H6V3h8v10h10V27z M8,17h14v-2H8V17z M8,21h14v-2H8V21z M8,25h14v-2H8V25z"></path></svg></div><span class="notion-page-title-text">好用的设计工具</span></span></a><a class="notion-page-link notion-block-0f9a5855603040fe9635804acec3e2af" href="/0f9a5855603040fe9635804acec3e2af"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="💽">💽</span></div><span class="notion-page-title-text">阅读密码</span></span></a></main></div>]]></content:encoded>
        </item>
        <item>
            <title><![CDATA[TensorRT]]></title>
            <link>https://ai-charlie.vercel.app/article/tensorrt</link>
            <guid>https://ai-charlie.vercel.app/article/tensorrt</guid>
            <pubDate>Thu, 13 Jul 2023 00:00:00 GMT</pubDate>
            <description><![CDATA[tensorrt汇总]]></description>
            <content:encoded><![CDATA[<div id="container" class="mx-auto undefined"><main class="notion light-mode notion-page notion-block-05f7cab57cf84b489ed279fe7a335852"><div class="notion-viewport"></div><div class="notion-collection-page-properties"></div><div class="notion-text notion-block-2a8665a03e0541989c1057c82d1aaebf">TensorRT是可以在NVIDIA各种GPU硬件平台下运行的一个C++推理框架。我们利用Pytorch、TF或者其他框架训练好的模型，可以转化为TensorRT的格式，然后利用TensorRT推理引擎去运行我们这个模型，从而提升这个模型在英伟达GPU上运行的速度。</div><div class="notion-text notion-block-03e82df929254dfeab8400984101fa26">在GPU服务器上部署的话，TensorRT是首选！</div><div class="notion-blank notion-block-bd3ba2f39aea4f938a86e52adb95d467"> </div><div class="notion-text notion-block-33883f33d2264a7b9515c8bcbac78a4d"><b><b>TensorRT是硬件相关的，</b></b>不同的硬件不同的优化。TensorRT是<b>半开源</b>的，除<b>核心部分外</b>基本都开源了。</div><h3 class="notion-h notion-h2 notion-h-indent-0 notion-block-a0dc7a652abb4c148233e6a9ad469a27" data-id="a0dc7a652abb4c148233e6a9ad469a27"><span><div id="a0dc7a652abb4c148233e6a9ad469a27" class="notion-header-anchor"></div><a class="notion-hash-link" href="#a0dc7a652abb4c148233e6a9ad469a27" title="TensorRT模型转换"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">TensorRT模型转换</span></span></h3><ol start="1" class="notion-list notion-list-numbered notion-block-e1ef6c3917df40ea9c0e4e4825b8471a"><li><code class="notion-inline-code">TF-TRT</code>，TensorRT集成在TensorFlow中</li></ol><ol start="2" class="notion-list notion-list-numbered notion-block-4997b125c60c43f8a6025bad55a48d97"><li><code class="notion-inline-code">ONNX2TensorRT</code>，即ONNX转换trt的工具 </li><ol class="notion-list notion-list-numbered notion-block-4997b125c60c43f8a6025bad55a48d97"></ol></ol><ol start="3" class="notion-list notion-list-numbered notion-block-412a5aac31414c71a8fb0b9a3739b7a7"><li>手动构造模型结构，然后手动将权重信息挪过去，非常灵活但是时间成本略高，有大佬已经尝试过了： <!-- -->.</li></ol><h3 class="notion-h notion-h2 notion-h-indent-0 notion-block-61afe00a57964cfdb45d66de0dea245e" data-id="61afe00a57964cfdb45d66de0dea245e"><span><div id="61afe00a57964cfdb45d66de0dea245e" class="notion-header-anchor"></div><a class="notion-hash-link" href="#61afe00a57964cfdb45d66de0dea245e" title="Project "><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Project </span></span></h3><div class="notion-text notion-block-3b34563814bc4e51a5bb0369c4675a5f"><a target="_blank" rel="noopener noreferrer" href="https://github.com/wang-xinyu/pytorchx" class="notion-external notion-external-mention"><div class="notion-external-image"><svg viewBox="0 0 260 260"><g><path d="M128.00106,0 C57.3172926,0 0,57.3066942 0,128.00106 C0,184.555281 36.6761997,232.535542 87.534937,249.460899 C93.9320223,250.645779 96.280588,246.684165 96.280588,243.303333 C96.280588,240.251045 96.1618878,230.167899 96.106777,219.472176 C60.4967585,227.215235 52.9826207,204.369712 52.9826207,204.369712 C47.1599584,189.574598 38.770408,185.640538 38.770408,185.640538 C27.1568785,177.696113 39.6458206,177.859325 39.6458206,177.859325 C52.4993419,178.762293 59.267365,191.04987 59.267365,191.04987 C70.6837675,210.618423 89.2115753,204.961093 96.5158685,201.690482 C97.6647155,193.417512 100.981959,187.77078 104.642583,184.574357 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fill="#161614"></path></g></svg></div><div class="notion-external-description"><div class="notion-external-title">pytorchx</div><div class="notion-external-subtitle"><span>wang-xinyu</span><span> • </span><span>Updated <!-- -->Jul 7, 2023</span></div></div></a> Implement popular deep learning networks in pytorch, used by <!-- -->.</div><h3 class="notion-h notion-h2 notion-h-indent-0 notion-block-202ca4ba4f044aa2a44ca434ef37437a" data-id="202ca4ba4f044aa2a44ca434ef37437a"><span><div id="202ca4ba4f044aa2a44ca434ef37437a" class="notion-header-anchor"></div><a class="notion-hash-link" href="#202ca4ba4f044aa2a44ca434ef37437a" title="PyTorch与TensorRT"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">PyTorch与TensorRT</span></span></h3><div class="notion-text notion-block-f898d5654bff4c1c9c80018a02c894d3">模型转换方式</div><div class="notion-text notion-block-e54886cf1e32448b96acc0f6ca9539e4">推荐  <b>PyTorch → ONNX → TensorRT C++API</b></div><div class="notion-text notion-block-fec4bd93b10541c99f79f283e0a5d862">其他<div class="notion-text-children"><ul class="notion-list notion-list-disc notion-block-393dc05e89e74aa2b4c712430ec80880"><li>torch2trt  <a target="_blank" rel="noopener noreferrer" href="https://github.com/NVIDIA-AI-IOT/torch2trt" class="notion-external notion-external-mention"><div class="notion-external-image"><svg viewBox="0 0 260 260"><g><path d="M128.00106,0 C57.3172926,0 0,57.3066942 0,128.00106 C0,184.555281 36.6761997,232.535542 87.534937,249.460899 C93.9320223,250.645779 96.280588,246.684165 96.280588,243.303333 C96.280588,240.251045 96.1618878,230.167899 96.106777,219.472176 C60.4967585,227.215235 52.9826207,204.369712 52.9826207,204.369712 C47.1599584,189.574598 38.770408,185.640538 38.770408,185.640538 C27.1568785,177.696113 39.6458206,177.859325 39.6458206,177.859325 C52.4993419,178.762293 59.267365,191.04987 59.267365,191.04987 C70.6837675,210.618423 89.2115753,204.961093 96.5158685,201.690482 C97.6647155,193.417512 100.981959,187.77078 104.642583,184.574357 C76.211799,181.33766 46.324819,170.362144 46.324819,121.315702 C46.324819,107.340889 51.3250588,95.9223682 59.5132437,86.9583937 C58.1842268,83.7344152 53.8029229,70.715562 60.7532354,53.0843636 C60.7532354,53.0843636 71.5019501,49.6441813 95.9626412,66.2049595 C106.172967,63.368876 117.123047,61.9465949 128.00106,61.8978432 C138.879073,61.9465949 149.837632,63.368876 160.067033,66.2049595 C184.49805,49.6441813 195.231926,53.0843636 195.231926,53.0843636 C202.199197,70.715562 197.815773,83.7344152 196.486756,86.9583937 C204.694018,95.9223682 209.660343,107.340889 209.660343,121.315702 C209.660343,170.478725 179.716133,181.303747 151.213281,184.472614 C155.80443,188.444828 159.895342,196.234518 159.895342,208.176593 C159.895342,225.303317 159.746968,239.087361 159.746968,243.303333 C159.746968,246.709601 162.05102,250.70089 168.53925,249.443941 C219.370432,232.499507 256,184.536204 256,128.00106 C256,57.3066942 198.691187,0 128.00106,0 Z M47.9405593,182.340212 C47.6586465,182.976105 46.6581745,183.166873 45.7467277,182.730227 C44.8183235,182.312656 44.2968914,181.445722 44.5978808,180.80771 C44.8734344,180.152739 45.876026,179.97045 46.8023103,180.409216 C47.7328342,180.826786 48.2627451,181.702199 47.9405593,182.340212 Z M54.2367892,187.958254 C53.6263318,188.524199 52.4329723,188.261363 51.6232682,187.366874 C50.7860088,186.474504 50.6291553,185.281144 51.2480912,184.70672 C51.8776254,184.140775 53.0349512,184.405731 53.8743302,185.298101 C54.7115892,186.201069 54.8748019,187.38595 54.2367892,187.958254 Z M58.5562413,195.146347 C57.7719732,195.691096 56.4895886,195.180261 55.6968417,194.042013 C54.9125733,192.903764 54.9125733,191.538713 55.713799,190.991845 C56.5086651,190.444977 57.7719732,190.936735 58.5753181,192.066505 C59.3574669,193.22383 59.3574669,194.58888 58.5562413,195.146347 Z M65.8613592,203.471174 C65.1597571,204.244846 63.6654083,204.03712 62.5716717,202.981538 C61.4524999,201.94927 61.1409122,200.484596 61.8446341,199.710926 C62.5547146,198.935137 64.0575422,199.15346 65.1597571,200.200564 C66.2704506,201.230712 66.6095936,202.705984 65.8613592,203.471174 Z M75.3025151,206.281542 C74.9930474,207.284134 73.553809,207.739857 72.1039724,207.313809 C70.6562556,206.875043 69.7087748,205.700761 70.0012857,204.687571 C70.302275,203.678621 71.7478721,203.20382 73.2083069,203.659543 C74.6539041,204.09619 75.6035048,205.261994 75.3025151,206.281542 Z M86.046947,207.473627 C86.0829806,208.529209 84.8535871,209.404622 83.3316829,209.4237 C81.8013,209.457614 80.563428,208.603398 80.5464708,207.564772 C80.5464708,206.498591 81.7483088,205.631657 83.2786917,205.606221 C84.8005962,205.576546 86.046947,206.424403 86.046947,207.473627 Z M96.6021471,207.069023 C96.7844366,208.099171 95.7267341,209.156872 94.215428,209.438785 C92.7295577,209.710099 91.3539086,209.074206 91.1652603,208.052538 C90.9808515,206.996955 92.0576306,205.939253 93.5413813,205.66582 C95.054807,205.402984 96.4092596,206.021919 96.6021471,207.069023 Z" fill="#161614"></path></g></svg></div><div class="notion-external-description"><div class="notion-external-title">torch2trt</div><div class="notion-external-subtitle"><span>NVIDIA-AI-IOT</span><span> • </span><span>Updated <!-- -->Jul 14, 2023</span></div></div></a></li></ul><ul class="notion-list notion-list-disc notion-block-7d7bdad31d124345a7cb9a3d084840c9"><li>torch2trt_dynamic  <a target="_blank" rel="noopener noreferrer" href="https://github.com/grimoire/torch2trt_dynamic" class="notion-external notion-external-mention"><div class="notion-external-image"><svg viewBox="0 0 260 260"><g><path d="M128.00106,0 C57.3172926,0 0,57.3066942 0,128.00106 C0,184.555281 36.6761997,232.535542 87.534937,249.460899 C93.9320223,250.645779 96.280588,246.684165 96.280588,243.303333 C96.280588,240.251045 96.1618878,230.167899 96.106777,219.472176 C60.4967585,227.215235 52.9826207,204.369712 52.9826207,204.369712 C47.1599584,189.574598 38.770408,185.640538 38.770408,185.640538 C27.1568785,177.696113 39.6458206,177.859325 39.6458206,177.859325 C52.4993419,178.762293 59.267365,191.04987 59.267365,191.04987 C70.6837675,210.618423 89.2115753,204.961093 96.5158685,201.690482 C97.6647155,193.417512 100.981959,187.77078 104.642583,184.574357 C76.211799,181.33766 46.324819,170.362144 46.324819,121.315702 C46.324819,107.340889 51.3250588,95.9223682 59.5132437,86.9583937 C58.1842268,83.7344152 53.8029229,70.715562 60.7532354,53.0843636 C60.7532354,53.0843636 71.5019501,49.6441813 95.9626412,66.2049595 C106.172967,63.368876 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C53.6263318,188.524199 52.4329723,188.261363 51.6232682,187.366874 C50.7860088,186.474504 50.6291553,185.281144 51.2480912,184.70672 C51.8776254,184.140775 53.0349512,184.405731 53.8743302,185.298101 C54.7115892,186.201069 54.8748019,187.38595 54.2367892,187.958254 Z M58.5562413,195.146347 C57.7719732,195.691096 56.4895886,195.180261 55.6968417,194.042013 C54.9125733,192.903764 54.9125733,191.538713 55.713799,190.991845 C56.5086651,190.444977 57.7719732,190.936735 58.5753181,192.066505 C59.3574669,193.22383 59.3574669,194.58888 58.5562413,195.146347 Z M65.8613592,203.471174 C65.1597571,204.244846 63.6654083,204.03712 62.5716717,202.981538 C61.4524999,201.94927 61.1409122,200.484596 61.8446341,199.710926 C62.5547146,198.935137 64.0575422,199.15346 65.1597571,200.200564 C66.2704506,201.230712 66.6095936,202.705984 65.8613592,203.471174 Z M75.3025151,206.281542 C74.9930474,207.284134 73.553809,207.739857 72.1039724,207.313809 C70.6562556,206.875043 69.7087748,205.700761 70.0012857,204.687571 C70.302275,203.678621 71.7478721,203.20382 73.2083069,203.659543 C74.6539041,204.09619 75.6035048,205.261994 75.3025151,206.281542 Z M86.046947,207.473627 C86.0829806,208.529209 84.8535871,209.404622 83.3316829,209.4237 C81.8013,209.457614 80.563428,208.603398 80.5464708,207.564772 C80.5464708,206.498591 81.7483088,205.631657 83.2786917,205.606221 C84.8005962,205.576546 86.046947,206.424403 86.046947,207.473627 Z M96.6021471,207.069023 C96.7844366,208.099171 95.7267341,209.156872 94.215428,209.438785 C92.7295577,209.710099 91.3539086,209.074206 91.1652603,208.052538 C90.9808515,206.996955 92.0576306,205.939253 93.5413813,205.66582 C95.054807,205.402984 96.4092596,206.021919 96.6021471,207.069023 Z" fill="#161614"></path></g></svg></div><div class="notion-external-description"><div class="notion-external-title">torch2trt_dynamic</div><div class="notion-external-subtitle"><span>grimoire</span><span> • </span><span>Updated <!-- -->Jul 13, 2023</span></div></div></a></li></ul><ul class="notion-list notion-list-disc notion-block-b66569fd53ac46e6a62dd333b335c78c"><li>TRTorch  <a target="_blank" rel="noopener noreferrer" href="https://github.com/pytorch/TensorRT" class="notion-external notion-external-mention"><div class="notion-external-image"><svg viewBox="0 0 260 260"><g><path d="M128.00106,0 C57.3172926,0 0,57.3066942 0,128.00106 C0,184.555281 36.6761997,232.535542 87.534937,249.460899 C93.9320223,250.645779 96.280588,246.684165 96.280588,243.303333 C96.280588,240.251045 96.1618878,230.167899 96.106777,219.472176 C60.4967585,227.215235 52.9826207,204.369712 52.9826207,204.369712 C47.1599584,189.574598 38.770408,185.640538 38.770408,185.640538 C27.1568785,177.696113 39.6458206,177.859325 39.6458206,177.859325 C52.4993419,178.762293 59.267365,191.04987 59.267365,191.04987 C70.6837675,210.618423 89.2115753,204.961093 96.5158685,201.690482 C97.6647155,193.417512 100.981959,187.77078 104.642583,184.574357 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C62.5547146,198.935137 64.0575422,199.15346 65.1597571,200.200564 C66.2704506,201.230712 66.6095936,202.705984 65.8613592,203.471174 Z M75.3025151,206.281542 C74.9930474,207.284134 73.553809,207.739857 72.1039724,207.313809 C70.6562556,206.875043 69.7087748,205.700761 70.0012857,204.687571 C70.302275,203.678621 71.7478721,203.20382 73.2083069,203.659543 C74.6539041,204.09619 75.6035048,205.261994 75.3025151,206.281542 Z M86.046947,207.473627 C86.0829806,208.529209 84.8535871,209.404622 83.3316829,209.4237 C81.8013,209.457614 80.563428,208.603398 80.5464708,207.564772 C80.5464708,206.498591 81.7483088,205.631657 83.2786917,205.606221 C84.8005962,205.576546 86.046947,206.424403 86.046947,207.473627 Z M96.6021471,207.069023 C96.7844366,208.099171 95.7267341,209.156872 94.215428,209.438785 C92.7295577,209.710099 91.3539086,209.074206 91.1652603,208.052538 C90.9808515,206.996955 92.0576306,205.939253 93.5413813,205.66582 C95.054807,205.402984 96.4092596,206.021919 96.6021471,207.069023 Z" fill="#161614"></path></g></svg></div><div class="notion-external-description"><div class="notion-external-title">TensorRT</div><div class="notion-external-subtitle"><span>pytorch</span><span> • </span><span>Updated <!-- -->Jul 13, 2023</span></div></div></a>，pytorch官方文档</li></ul></div></div><h3 class="notion-h notion-h2 notion-h-indent-0 notion-block-747b96616a0d4e64a7be262b46e2554a" data-id="747b96616a0d4e64a7be262b46e2554a"><span><div id="747b96616a0d4e64a7be262b46e2554a" class="notion-header-anchor"></div><a class="notion-hash-link" href="#747b96616a0d4e64a7be262b46e2554a" title="理解TensorRT Runtimes"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">理解TensorRT Runtimes</span></span></h3><div class="notion-text notion-block-dc4978898072414f85823e70e2f9b001">在TensorRT Runtime环境中运行模型，就是直接使用TensorRT；</div><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-8728b96aa599471a812673492ae11ba3" href="https://github.com/NVIDIA/TensorRT/blob/main/quickstart/IntroNotebooks/5.%20Understanding%20TensorRT%20Runtimes.ipynb"><div><div class="notion-bookmark-title">TensorRT/5. Understanding TensorRT Runtimes.ipynb at main · NVIDIA/TensorRT</div><div class="notion-bookmark-description">TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators. - TensorRT/5. Understanding TensorRT Runtimes.ipynb at main · NVIDIA/TensorRT</div><div class="notion-bookmark-link"><div class="notion-bookmark-link-icon"><img src="https://github.com/favicon.ico" alt="TensorRT/5. Understanding TensorRT Runtimes.ipynb at main · NVIDIA/TensorRT" loading="lazy" decoding="async"/></div><div class="notion-bookmark-link-text">https://github.com/NVIDIA/TensorRT/blob/main/quickstart/IntroNotebooks/5.%20Understanding%20TensorRT%20Runtimes.ipynb</div></div></div><div class="notion-bookmark-image"><img style="object-fit:cover" src="https://opengraph.githubassets.com/a955849c29f6ea54ae4b48185c64cfd5af1f427217bcb67b5f97089efc95d784/NVIDIA/TensorRT" alt="TensorRT/5. Understanding TensorRT Runtimes.ipynb at main · NVIDIA/TensorRT" loading="lazy" decoding="async"/></div></a></div><div class="notion-text notion-block-ca75d1362e484e4e9369e47a84629b68"><b>Python API </b></div><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-9df71d96150e4d2da652c27901d14ad2" href="https://github.com/NVIDIA/TensorRT/blob/87f3394404ff9f9ec92c906cd4c39b5562aea42e/quickstart/IntroNotebooks/4.%20Using%20PyTorch%20through%20ONNX.ipynb"><div><div class="notion-bookmark-title">TensorRT/4. Using PyTorch through ONNX.ipynb at 87f3394404ff9f9ec92c906cd4c39b5562aea42e · NVIDIA/TensorRT</div><div class="notion-bookmark-description">TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators. - TensorRT/4. Using PyTorch through ONNX.ipynb at 87f3394404ff9f9ec92c906cd4c39b5562aea42e · ...</div><div class="notion-bookmark-link"><div class="notion-bookmark-link-icon"><img src="https://github.com/favicon.ico" alt="TensorRT/4. Using PyTorch through ONNX.ipynb at 87f3394404ff9f9ec92c906cd4c39b5562aea42e · NVIDIA/TensorRT" loading="lazy" decoding="async"/></div><div class="notion-bookmark-link-text">https://github.com/NVIDIA/TensorRT/blob/87f3394404ff9f9ec92c906cd4c39b5562aea42e/quickstart/IntroNotebooks/4.%20Using%20PyTorch%20through%20ONNX.ipynb</div></div></div><div class="notion-bookmark-image"><img style="object-fit:cover" src="https://opengraph.githubassets.com/a955849c29f6ea54ae4b48185c64cfd5af1f427217bcb67b5f97089efc95d784/NVIDIA/TensorRT" alt="TensorRT/4. Using PyTorch through ONNX.ipynb at 87f3394404ff9f9ec92c906cd4c39b5562aea42e · NVIDIA/TensorRT" loading="lazy" decoding="async"/></div></a></div><div class="notion-text notion-block-4d708474e2224b6aacf5286725060254"><b>C++ API </b><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://developer.nvidia.com/blog/speed-up-inference-tensorrt/">https://developer.nvidia.com/blog/speed-up-inference-tensorrt/</a></div><div class="notion-text notion-block-f81ddc36db73498f94126a4a32d293fb"><b>Tensorflow/TF-TRT Runtime: (Tensorflow Only) 操作简单，加速效果不好</b></div><div class="notion-text notion-block-f13bd74a3eff42e7b7b13ba084740bcc"><b>TRITON Inference Server </b>完美支持TensorRT，用在生产环境<a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/NVIDIA/TensorRT/tree/main/quickstart/deploy_to_triton">https://github.com/NVIDIA/TensorRT/tree/main/quickstart/deploy_to_triton</a></div><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-1ba49378e9a94b19b73fc0a1b913f79b" href="https://developer.nvidia.com/nvidia-triton-inference-server"><div><div class="notion-bookmark-title">Triton Inference Server</div><div class="notion-bookmark-description">An open-source inference serving software that helps standardize model deployment and delivers fast and scalable AI in production.</div><div class="notion-bookmark-link"><div class="notion-bookmark-link-icon"><img src="https://developer.nvidia.com/sites/all/themes/devzone_new/favicon.ico" alt="Triton Inference Server" loading="lazy" decoding="async"/></div><div class="notion-bookmark-link-text">https://developer.nvidia.com/nvidia-triton-inference-server</div></div></div><div class="notion-bookmark-image"><img style="object-fit:cover" src="https://developer.nvidia.com/sites/default/files/akamai/triton/og-gtc-22-triton-web-100.jpg" alt="Triton Inference Server" loading="lazy" decoding="async"/></div></a></div><h4 class="notion-h notion-h3 notion-h-indent-1 notion-block-06b227d5c85f4d81bd103adcf005b13f" data-id="06b227d5c85f4d81bd103adcf005b13f"><span><div id="06b227d5c85f4d81bd103adcf005b13f" class="notion-header-anchor"></div><a class="notion-hash-link" href="#06b227d5c85f4d81bd103adcf005b13f" title="Nvidia 官方镜像"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Nvidia 官方镜像</span></span></h4><div class="notion-text notion-block-06f9e3d76f674ed480018f06c6333b4d">nvidia官方提供了tensorrt以及包含tensorrt的流行深度学习环境的镜像，例如tensorflow、paddlepaddle、torch</div><div class="notion-text notion-block-d31aa94117824dfeadb2fcd6dcd0af29">推荐pytorch镜像</div><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-aa02d56c80d54ac5a5ec4d0c29847451" href="https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel_22-06.html#rel_22-06"><div><div class="notion-bookmark-title">PyTorch Release Notes :: NVIDIA Deep Learning Frameworks Documentation</div><div class="notion-bookmark-description">These release notes describe the key features, software enhancements and improvements, known issues, and how to run this container. The PyTorch framework enables you to develop deep learning models with flexibility, use Python packages such as SciPy, NumPy, and so on.</div><div class="notion-bookmark-link"><div class="notion-bookmark-link-text">https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel_22-06.html#rel_22-06</div></div></div></a></div><div class="notion-text notion-block-81706e806a09451b9cc82321d7afc296">容器运行命令</div><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-d2411698b45a475bb666590f0770c252" href="https://docs.nvidia.com/deeplearning/tensorrt/index.html"><div><div class="notion-bookmark-title">NVIDIA TensorRT</div><div class="notion-bookmark-description">NVIDIA Deep Learning TensorRT Documentation - Last updated July 28, 2022 - Send Feedback - Quick Start Guide This NVIDIA TensorRT 8.4.2 Quick Start Guide is a starting point for developers who want to try out TensorRT SDK; specifically, this document demonstrates how to quickly construct an application to run inference on a TensorRT engine.</div><div class="notion-bookmark-link"><div class="notion-bookmark-link-text">https://docs.nvidia.com/deeplearning/tensorrt/index.html</div></div></div></a></div><div class="notion-row"><a target="_blank" rel="noopener noreferrer" class="notion-bookmark notion-block-6cfc2685eb8b42af91ee4f3f62b50dd9" href="https://github.githistory.xyz/NVIDIA/trt-samples-for-hackathon-cn/blob/master/cookbook/README.md"><div><div class="notion-bookmark-title">Git History</div><div class="notion-bookmark-description">Quickly browse the history of a file from GitHub, GitLab, Bitbucket or any git repository</div><div class="notion-bookmark-link"><div class="notion-bookmark-link-icon"><img src="https://github.githistory.xyz/favicon.ico" alt="Git History" loading="lazy" decoding="async"/></div><div class="notion-bookmark-link-text">https://github.githistory.xyz/NVIDIA/trt-samples-for-hackathon-cn/blob/master/cookbook/README.md</div></div></div></a></div><div class="notion-text notion-block-c2ce48decdd84246a9993151256779ab"> </div><div class="notion-callout notion-gray_background_co notion-block-85c3c353d30043bbb9a01c643b7cc25c"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-icon" role="img" aria-label="💡">💡</span></div><div class="notion-callout-text">Tensorrt 仅支持在GPU环境下使用</div></div><h2 class="notion-h notion-h1 notion-h-indent-0 notion-block-45d28ada09c14fab8b4463e478bdd9b2" data-id="45d28ada09c14fab8b4463e478bdd9b2"><span><div id="45d28ada09c14fab8b4463e478bdd9b2" class="notion-header-anchor"></div><a class="notion-hash-link" href="#45d28ada09c14fab8b4463e478bdd9b2" title="Tensorrt C++部署"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Tensorrt C++部署</span></span></h2><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-1197a2ea35db404a8876e7a804b29b23" data-id="1197a2ea35db404a8876e7a804b29b23"><span><div id="1197a2ea35db404a8876e7a804b29b23" class="notion-header-anchor"></div><a class="notion-hash-link" href="#1197a2ea35db404a8876e7a804b29b23" title="环境配置"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">环境配置</span></span></h3><h4 class="notion-h notion-h3 notion-h-indent-2 notion-block-1206f4611bae46b4981adec23e39d918" data-id="1206f4611bae46b4981adec23e39d918"><span><div id="1206f4611bae46b4981adec23e39d918" class="notion-header-anchor"></div><a class="notion-hash-link" href="#1206f4611bae46b4981adec23e39d918" title="Dockerfile"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Dockerfile</span></span></h4><div class="notion-text notion-block-4a6eaabc96094d509c5ec62f1cff95e3">由官方<!-- --> 提供的Ubuntu18.04.Dockerfile修改而来，参考 <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/NVIDIA/cuda-repo-management/issues/4">https://github.com/NVIDIA/cuda-repo-management/issues/4</a>解决GPG error，参考官方安装文档 <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-debian">https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-debian</a> 构建</div><details class="notion-toggle notion-block-038716bc35014563b575da73fe9db0d0"><summary>构建基础tensorrt版本</summary><div></div></details><details class="notion-toggle notion-block-db00249443cb43d2a564bd8fee2bfd8e"><summary>自动化构建脚本</summary><div><div class="notion-blank notion-block-0073554025d84d6b88bd7ccb094370d5"> </div></div></details><details class="notion-toggle notion-block-a10f45cb44cc40e7a6936a70d6221e83"><summary>二次构建包含torch和tf的环境</summary><div></div></details><details class="notion-toggle notion-block-17bfdeae6c9b4e04b8802f6c66b58169"><summary>自动化构建容器脚本</summary><div></div></details><details class="notion-toggle notion-block-47d7fece47d34fa5a979fdf87b83aed3"><summary>运行容器脚本</summary><div><div class="notion-text notion-block-65e7c7863c8a46a286815499ff0254bb">arg_jupyter=0 则不运行jupyter</div></div></details><div class="notion-blank notion-block-ae186d84dcbb47b6b2e43384692a70c7"> </div><h2 class="notion-h notion-h1 notion-h-indent-0 notion-block-b51f606f393b48eb9269cd293406bbce" data-id="b51f606f393b48eb9269cd293406bbce"><span><div id="b51f606f393b48eb9269cd293406bbce" class="notion-header-anchor"></div><a class="notion-hash-link" href="#b51f606f393b48eb9269cd293406bbce" title="tensorrt版本适配"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">tensorrt版本适配</span></span></h2><div class="notion-text notion-block-d7997787ff7a4f9180d621ba8d16eb9d">To build the TensorRT-OSS components, you will first need the following software packages.</div><div class="notion-text notion-block-1e16243ecbbc40b2804b8a37fecbebf1"><b>TensorRT GA build675</b></div><ul class="notion-list notion-list-disc notion-block-b6754adba2ce4b00aacdef842d53aa04"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://developer.nvidia.com/nvidia-tensorrt-download">TensorRT</a> v8.4.3.1</li></ul><div class="notion-text notion-block-03d6732026674f2d902389b3749b5a73"><b>System Packages</b></div><ul class="notion-list notion-list-disc notion-block-1d3defe26dbe47af92c97ca15175bd85"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://developer.nvidia.com/cuda-toolkit">CUDA</a></li><ul class="notion-list notion-list-disc notion-block-1d3defe26dbe47af92c97ca15175bd85"><li>Recommended versions:</li><li>cuda-11.6.x + cuDNN-8.4</li><li>cuda-10.2 + cuDNN-8.4</li></ul></ul><ul class="notion-list notion-list-disc notion-block-2f1403fc4b9f48dcb8405c810aa99486"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://ftp.gnu.org/gnu/make/">GNU make</a> &gt;= v4.1</li></ul><ul class="notion-list notion-list-disc notion-block-634994810fd34f8dbf5fe62377f9fce4"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/Kitware/CMake/releases">cmake</a> &gt;= v3.13</li></ul><ul class="notion-list notion-list-disc notion-block-0ecdafaea97e4d9aae74d44e66f31b5f"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://www.python.org/downloads/">python</a> &gt;= v3.6.9</li></ul><ul class="notion-list notion-list-disc notion-block-d3e70245d1d8492386a9c97371bb2170"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/pip/#history">pip</a> &gt;= v19.0</li></ul><ul class="notion-list notion-list-disc notion-block-5e586cad5968459a9e7f4dd9cd3270aa"><li>Essential utilities</li><ul class="notion-list notion-list-disc notion-block-5e586cad5968459a9e7f4dd9cd3270aa"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://git-scm.com/downloads">git</a>, <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://www.freedesktop.org/wiki/Software/pkg-config/">pkg-config</a>, <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://www.gnu.org/software/wget/faq.html#download">wget</a></li></ul></ul><div class="notion-text notion-block-c4bbe96eb35f48b2a0d225e9400762be"><b>Optional Packages</b></div><ul class="notion-list notion-list-disc notion-block-36e4b35185f243b48eca7102398d7307"><li>Containerized build</li><ul class="notion-list notion-list-disc notion-block-36e4b35185f243b48eca7102398d7307"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://docs.docker.com/install/">Docker</a> &gt;= 19.03</li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/NVIDIA/nvidia-docker">NVIDIA Container Toolkit</a></li></ul></ul><ul class="notion-list notion-list-disc notion-block-0d30058c162b4838afafbe8ca175be82"><li>Toolchains and SDKs</li><ul class="notion-list notion-list-disc notion-block-0d30058c162b4838afafbe8ca175be82"><li>(Cross compilation for Jetson platform) <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://developer.nvidia.com/embedded/jetpack">NVIDIA JetPack</a> &gt;= 5.0 (current support only for TensorRT 8.4.0)</li><li>(For Windows builds) <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://visualstudio.microsoft.com/vs/older-downloads/">Visual Studio</a> 2017 Community or Enterprise edition</li><li>(Cross compilation for QNX platform) <a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://blackberry.qnx.com/en">QNX Toolchain</a></li></ul></ul><ul class="notion-list notion-list-disc notion-block-4dbbe16f94f2469692cfa1fa48768c8d"><li>PyPI packages (for demo applications/tests)</li><ul class="notion-list notion-list-disc notion-block-4dbbe16f94f2469692cfa1fa48768c8d"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/onnx/">onnx</a> 1.9.0</li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/onnxruntime/">onnxruntime</a> 1.8.0</li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/tensorflow/">tensorflow-gpu</a> &gt;= 2.5.1</li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/Pillow/">Pillow</a> &gt;= 9.0.1</li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/pycuda/">pycuda</a> &lt; 2021.1</li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/numpy/">numpy</a></li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://pypi.org/project/pytest/">pytest</a></li></ul></ul><ul class="notion-list notion-list-disc notion-block-f43637e5fa18416aa96dc93c9d179754"><li>Code formatting tools (for contributors)</li><ul class="notion-list notion-list-disc notion-block-f43637e5fa18416aa96dc93c9d179754"><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://clang.llvm.org/docs/ClangFormat.html">Clang-format</a></li><li><a target="_blank" rel="noopener noreferrer" class="notion-link" href="https://github.com/llvm-mirror/clang/blob/master/tools/clang-format/git-clang-format">Git-clang-format</a></li><blockquote class="notion-quote notion-block-ed8b85e0ed5d4d8a9fd040d2583fdd9a"><div>NOTE: onnx-tensorrt, cub, and protobuf packages are downloaded along with TensorRT OSS, and not required to be installed.</div></blockquote><div class="notion-blank notion-block-67f10e2655be4fcb854f1cd77a62d643"> </div></ul></ul><div class="notion-blank notion-block-90c5bea4eb0c4be48029d99edcbc84f8"> </div><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-91128336399a4887a9cb43f490b31501" data-id="91128336399a4887a9cb43f490b31501"><span><div id="91128336399a4887a9cb43f490b31501" class="notion-header-anchor"></div><a class="notion-hash-link" href="#91128336399a4887a9cb43f490b31501" title="Torch转ONNX"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Torch转ONNX</span></span></h3><div class="notion-blank notion-block-f1fefa1b83554de09d177969a234fb9d"> </div><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-69a915d166d14ce5a45f0885129aec7f" data-id="69a915d166d14ce5a45f0885129aec7f"><span><div id="69a915d166d14ce5a45f0885129aec7f" class="notion-header-anchor"></div><a class="notion-hash-link" href="#69a915d166d14ce5a45f0885129aec7f" title="Onnxrunime 推理验证"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Onnxrunime 推理验证</span></span></h3><div class="notion-blank notion-block-f7a4f0902689483b845f5098f0895598"> </div><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-932bd4bfb3b54d26a7f50b871e90b51d" data-id="932bd4bfb3b54d26a7f50b871e90b51d"><span><div id="932bd4bfb3b54d26a7f50b871e90b51d" class="notion-header-anchor"></div><a class="notion-hash-link" href="#932bd4bfb3b54d26a7f50b871e90b51d" title="Trtexec 快速验证"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Trtexec 快速验证</span></span></h3><div class="notion-text notion-block-4521d00ec75348ecbdfbc1c586a99d2c">bug中</div><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-d270d9369fd04482a64ce7810b941c06" data-id="d270d9369fd04482a64ce7810b941c06"><span><div id="d270d9369fd04482a64ce7810b941c06" class="notion-header-anchor"></div><a class="notion-hash-link" href="#d270d9369fd04482a64ce7810b941c06" title="Tensort 推理"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Tensort 推理</span></span></h3><div class="notion-text notion-block-cd716cf6153741d0b6cda97c4cb74575">bug中</div><a class="notion-page-link notion-block-e0c7d04f3e1d41d2be67d69107a92f57" href="/e0c7d04f3e1d41d2be67d69107a92f57"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-image"><svg class="notion-page-title-icon notion-page-icon" alt="tensorrt step transform tutorials" viewBox="0 0 30 30" width="16"><path d="M16,1H4v28h22V11L16,1z M16,3.828L23.172,11H16V3.828z M24,27H6V3h8v10h10V27z M8,17h14v-2H8V17z M8,21h14v-2H8V21z M8,25h14v-2H8V25z"></path></svg></div><span class="notion-page-title-text">tensorrt step transform tutorials</span></span></a></main></div>]]></content:encoded>
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            <title><![CDATA[FaceRecgonize]]></title>
            <link>https://ai-charlie.vercel.app/article/facerec</link>
            <guid>https://ai-charlie.vercel.app/article/facerec</guid>
            <pubDate>Thu, 13 Jul 2023 00:00:00 GMT</pubDate>
            <description><![CDATA[人脸识别汇总]]></description>
            <content:encoded><![CDATA[<div id="container" class="mx-auto undefined"><main class="notion light-mode notion-page notion-block-9de8b6c0679e4690b3b03f40ce815a29"><div class="notion-viewport"></div><div class="notion-collection-page-properties"></div><h2 class="notion-h notion-h1 notion-h-indent-0 notion-block-eafcceb896bf47afad8c6165be130f5c" data-id="eafcceb896bf47afad8c6165be130f5c"><span><div id="eafcceb896bf47afad8c6165be130f5c" class="notion-header-anchor"></div><a class="notion-hash-link" href="#eafcceb896bf47afad8c6165be130f5c" title="1 人脸识别步骤"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">1 人脸识别步骤</span></span></h2><ol start="1" class="notion-list notion-list-numbered notion-block-6fa283c8483d481ea268cb6488e1706c"><li>人脸检测</li></ol><ol start="2" class="notion-list notion-list-numbered notion-block-b0faab0dcdd4455997b64e523fb6f708"><li>人脸预处理：人脸关键点检测+人脸矫正</li></ol><ol start="3" class="notion-list notion-list-numbered notion-block-074903dc37d84b178038dcb7de8da0b4"><li>人脸表示：提取具有较好表达能力的特征向量</li></ol><ol start="4" class="notion-list notion-list-numbered notion-block-abb415f91c0e4d9ca1a309790d2ffb7e"><li>特征匹配/比对</li></ol><h2 class="notion-h notion-h1 notion-h-indent-0 notion-block-dc59706f38e34a5a835514ac8ff30829" data-id="dc59706f38e34a5a835514ac8ff30829"><span><div id="dc59706f38e34a5a835514ac8ff30829" class="notion-header-anchor"></div><a class="notion-hash-link" href="#dc59706f38e34a5a835514ac8ff30829" title="2 数据集"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">2 数据集</span></span></h2><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-4f831ba97b944954b94f68838fb6293a" data-id="4f831ba97b944954b94f68838fb6293a"><span><div id="4f831ba97b944954b94f68838fb6293a" class="notion-header-anchor"></div><a class="notion-hash-link" href="#4f831ba97b944954b94f68838fb6293a" title="CH-FACE-Z 数据集"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">CH-FACE-Z 数据集</span></span></h3><div class="notion-text notion-block-2d9eae5c067f4804a6de27200e0c5251">numberi文件夹里代表每个子文件夹（人）包含i个图像。</div><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-b463c350458e440193435ae8795c0f34" data-id="b463c350458e440193435ae8795c0f34"><span><div id="b463c350458e440193435ae8795c0f34" class="notion-header-anchor"></div><a class="notion-hash-link" href="#b463c350458e440193435ae8795c0f34" title="LFW数据集"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">LFW数据集</span></span></h3><div class="notion-text notion-block-47cf72847032421f8a361787f77268f1">每个子文件夹（人）包含一到多个图像。</div><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-a5ff8eb35c79443ca5a432bd9fa66cd4" data-id="a5ff8eb35c79443ca5a432bd9fa66cd4"><span><div id="a5ff8eb35c79443ca5a432bd9fa66cd4" class="notion-header-anchor"></div><a class="notion-hash-link" href="#a5ff8eb35c79443ca5a432bd9fa66cd4" title="GASIA数据集"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">GASIA数据集</span></span></h3><div class="notion-blank notion-block-634a764feea848cdad7c8743ff7da928"> </div><h2 class="notion-h notion-h1 notion-h-indent-0 notion-block-cfd05bab2f424a33ac96e12a74a61b10" data-id="cfd05bab2f424a33ac96e12a74a61b10"><span><div id="cfd05bab2f424a33ac96e12a74a61b10" class="notion-header-anchor"></div><a class="notion-hash-link" href="#cfd05bab2f424a33ac96e12a74a61b10" title="3 论文"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">3 论文</span></span></h2><h3 class="notion-h notion-h2 notion-h-indent-1 notion-block-27e002ce3e4a4e86b4949396a193ac5f" data-id="27e002ce3e4a4e86b4949396a193ac5f"><span><div id="27e002ce3e4a4e86b4949396a193ac5f" class="notion-header-anchor"></div><a class="notion-hash-link" href="#27e002ce3e4a4e86b4949396a193ac5f" title="3.1 FaceNet"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">3.1 FaceNet</span></span></h3><div class="notion-text notion-block-73aeb176d73d47469513445195ccea52"><b>模型结构</b>：网络结构包含一个batch 输入层，一个深度卷积网络层，之后跟着一个L2正则化层，从而实现人脸embedding，然后使用Triplet Loss进行训练</div><div class="notion-text notion-block-24410157acec40e08c4bf52e6cc79ed1">facenet的主干网络起到提取特征的作用,以<b>Inception-ResNetV1</b>为主干特征提取网络。</div><div class="notion-text notion-block-d378065906ef4285b545fa49c1abfb45">facenet基于使用深度卷积网络学习每幅图像的欧几里德Embedding。</div><h4 class="notion-h notion-h3 notion-h-indent-2 notion-block-e3aaab7ba1b2462eb0dfc9ac5d2fc46b" data-id="e3aaab7ba1b2462eb0dfc9ac5d2fc46b"><span><div id="e3aaab7ba1b2462eb0dfc9ac5d2fc46b" class="notion-header-anchor"></div><a class="notion-hash-link" href="#e3aaab7ba1b2462eb0dfc9ac5d2fc46b" title="Triplet Loss三重损失（triplet loss）"><svg viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a><span class="notion-h-title">Triplet Loss三重损失（triplet loss）</span></span></h4><div class="notion-text notion-block-a60bda0de8b447b798d16ae1ad85b9ae">Triplet loss 的输入是一个三元组。</div><ol start="1" class="notion-list notion-list-numbered notion-block-52e1b01f89b345f3aa81761dcb16782d"><li>a：anchor，基准图片获得的128维人脸特征向量</li></ol><ol start="2" class="notion-list notion-list-numbered notion-block-39e17553007b45c29c0ca4f1a2bad8ae"><li>p：positive，与基准图片属于同一张人脸的图片获得的128维人脸特征向量</li></ol><ol start="3" class="notion-list notion-list-numbered notion-block-0dc74a8e130c4ffeb149021ee02fc4b1"><li>n：negative，与基准图片不属于同一张人脸的图片获得的128维人脸特征向量</li></ol><div class="notion-blank notion-block-ae6e167e590b4ec59f05e011deb77507"> </div></main></div>]]></content:encoded>
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            <title><![CDATA[Educations]]></title>
            <link>https://ai-charlie.vercel.app/article/edu</link>
            <guid>https://ai-charlie.vercel.app/article/edu</guid>
            <pubDate>Thu, 13 Jul 2023 00:00:00 GMT</pubDate>
            <description><![CDATA[ 教育技术汇总]]></description>
            <content:encoded><![CDATA[<div id="container" class="mx-auto undefined"><main class="notion light-mode notion-page notion-block-853e8ea0d5bb44e2b7c4f98ba62dca38"><div class="notion-viewport"></div><div class="notion-collection-page-properties"></div><a class="notion-page-link notion-block-e94f4ceab89148deb2824cd2dff3f64a" href="/e94f4ceab89148deb2824cd2dff3f64a"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🇱🇮">🇱🇮</span></div><span class="notion-page-title-text">教育政策</span></span></a><a class="notion-page-link notion-block-6e44ea929ae04a98a0b4540b00249544" href="/6e44ea929ae04a98a0b4540b00249544"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="📗">📗</span></div><span class="notion-page-title-text">信息技术课程</span></span></a><hr class="notion-hr notion-block-5cdc4ca6d25f40758a20c7038acca1a8"/><a class="notion-page-link notion-block-53d13c4e0db8430e8b922b28361b6158" href="/53d13c4e0db8430e8b922b28361b6158"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🌄">🌄</span></div><span class="notion-page-title-text">量化研究作业</span></span></a><a class="notion-page-link notion-block-18a0dc18fc8b427e9869335d730cbe88" href="/18a0dc18fc8b427e9869335d730cbe88"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🪒">🪒</span></div><span class="notion-page-title-text">教育科学研究方法</span></span></a><a class="notion-page-link notion-block-8cc4e00758de4cfba728675c96471f01" href="/8cc4e00758de4cfba728675c96471f01"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-span"><span class="notion-page-title-icon notion-page-icon" role="img" aria-label="🎿">🎿</span></div><span class="notion-page-title-text">课程考核提交材料（电子版）</span></span></a><a class="notion-page-link notion-block-9ab6730c902e4abe91a90006fbb3efa3" href="/9ab6730c902e4abe91a90006fbb3efa3"><span class="notion-page-title"><div class="notion-page-icon-inline notion-page-icon-image"><svg class="notion-page-title-icon notion-page-icon" alt="教招电子资料" viewBox="0 0 30 30" width="16"><path d="M16,1H4v28h22V11L16,1z M16,3.828L23.172,11H16V3.828z M24,27H6V3h8v10h10V27z M8,17h14v-2H8V17z M8,21h14v-2H8V21z M8,25h14v-2H8V25z"></path></svg></div><span class="notion-page-title-text">教招电子资料</span></span></a></main></div>]]></content:encoded>
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