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<!DOCTYPE html>
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Eureka Tesla
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<h1><a href="/">Eureka Tesla</a></h1>
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<article
id="[post]-你想“GAN”什么"
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<a class="article-title" href="/2021/07/28/%E4%BD%A0%E6%83%B3%E2%80%9CGAN%E2%80%9D%E4%BB%80%E4%B9%88/"
>你想“GAN”什么</a>
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<a href="/2021/07/28/%E4%BD%A0%E6%83%B3%E2%80%9CGAN%E2%80%9D%E4%BB%80%E4%B9%88/" class="article-date">
<time datetime="2021-07-28T10:23:19.000Z" itemprop="datePublished">2021-07-28</time>
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<a class="article-category-link" href="/categories/%E4%BD%A0%E6%83%B3%E2%80%9CGAN%E2%80%9D%E4%BB%80%E4%B9%88/">你想“GAN”什么</a>
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<p><strong>简单介绍了 GAN 的基本思路、基本理论和训练过程，更为深奥的数学部分可以看最后模块“从 GAN 到 WGAN”。虽然 GAN 在“生成”方面应用颇多，但 GAN 更多的是一种思路而不是一个具体应用，“对抗”的思想是其核心，在多个领域都有其影子。实际上“对抗”也不局限于原论文的两个网络之间的一种对抗，可能是两个网络之间的多种对抗，也可能是多个网络之间的一种或多种对抗。所以根据具体任务具体应用不同 GAN，那么你想“GAN”什么？</strong></p>
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<a class="article-title" href="/2021/05/22/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%85%A5%E9%97%A8-Part-3%EF%BC%9AR-CNN-%E7%B3%BB%E5%88%97/"
>目标检测入门 Part 3：R-CNN 系列</a>
</h2>
</header>
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<a href="/2021/05/22/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%85%A5%E9%97%A8-Part-3%EF%BC%9AR-CNN-%E7%B3%BB%E5%88%97/" class="article-date">
<time datetime="2021-05-22T12:14:19.000Z" itemprop="datePublished">2021-05-22</time>
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<a class="article-category-link" href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%85%A5%E9%97%A8/">目标检测入门</a>
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<p><strong>在“目标检测入门”系列中，我们在第1部分从图像处理的基本概念开始，如梯度向量和 HOG。然后，我们在第2部分介绍了用于分类的经典卷积神经网络结构设计和用于目标识别的先进模型 Overfeat 和 DPM。在本系列的第三篇文章中，我们将介绍 R-CNN（“基于区域的 CNN”）家族中的一系列模型。</strong></p>
<p><strong>在第3部分中，我们将研究四种目标检测模型：R-CNN、Fast R-CNN、Faster R-CNN 和 Mask R-CNN。这些模型是高度相关的并且新版本与旧版本相比，在速度上有巨大的改善</strong></p>
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id="[post]-STR概述"
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<a class="article-title" href="/2021/04/26/STR%E6%A6%82%E8%BF%B0/"
>STR 深度学习算法综述</a>
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<a href="/2021/04/26/STR%E6%A6%82%E8%BF%B0/" class="article-date">
<time datetime="2021-04-26T12:53:04.000Z" itemprop="datePublished">2021-04-26</time>
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<a class="article-category-link" href="/categories/OCR/">OCR</a>
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<p><strong>随着深度学习在计算机视觉上的巨大成功，越来越多的场景文字检测和识别引入深度学习。本文旨在总结和分析场景文字检测与识别在深度学习时代的主要变化和重大进展，还将分析现状和未来的研究趋势。</strong></p>
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<a class="article-title" href="/2021/04/02/Windows-10-%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%9F%BA%E6%9C%AC%E7%8E%AF%E5%A2%83/"
>Windows 10 配置深度学习基本环境</a>
</h2>
</header>
<div class="article-meta">
<a href="/2021/04/02/Windows-10-%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%9F%BA%E6%9C%AC%E7%8E%AF%E5%A2%83/" class="article-date">
<time datetime="2021-04-02T14:26:50.000Z" itemprop="datePublished">2021-04-02</time>
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<a class="article-category-link" href="/categories/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%8E%AF%E5%A2%83/">深度学习环境</a>
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<p><strong>包括在 Windows 10 安装Python，CUDA 和 cuDNN，opencv，TensorFlow，pytorch</strong></p>
<p><strong>注意 CUDA 和 cuDNN 的版本要和 TensorFlow 版本保持一致，下面会有介绍</strong></p>
<p><strong>本教程在 RTX20 系列显卡上进行，注意 RTX30 系列显卡可能需要较高版本的 CUDA，对应的 cuDNN，TensorFlow 版本也可能不同</strong></p>
<p>相对于在 CENTOS 7 环境下配置环境，Windows 10 环境要友好很多，内容也比较简单，按步骤来即可</p>
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<a class="article-title" href="/2020/11/12/Windows-10-%E5%92%8C-CentOS-7-%E5%8F%8C%E7%B3%BB%E7%BB%9F/"
>Windows 10 和 CentOS 7 双系统</a>
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<p><strong>因实验需要，在深度学习工作站上的 Windows 10 基础上安装 CentOS 7 系统。</strong></p>
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>CentOS 7 配置深度学习基本环境</a>
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<a href="/2020/10/11/CentOS-7-%E9%85%8D%E7%BD%AE%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%9F%BA%E6%9C%AC%E7%8E%AF%E5%A2%83/" class="article-date">
<time datetime="2020-10-11T15:16:40.000Z" itemprop="datePublished">2020-10-11</time>
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<p><strong>包括在 CentOS 7 安装 NVIDIA 显卡驱动，CUDA 和 cuDNN，TensorFlow</strong></p>
<p><strong>注意 CUDA 和 cuDNN 的版本要和 TensorFlow 版本保持一致，下面会有介绍</strong></p>
<p><strong>本教程在 RTX20 系列显卡上进行，注意 RTX30 系列显卡需要较高版本的 CUDA，对应的 TensorFlow 版本也不同</strong></p>
<p>内容比较繁琐，一步步慢慢来</p>
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>目标检测入门 Part 2：CNN，DPM 和 Overfeat</a>
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<time datetime="2020-08-12T14:13:06.000Z" itemprop="datePublished">2020-08-12</time>
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<a class="article-category-link" href="/categories/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%85%A5%E9%97%A8/">目标检测入门</a>
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<p><strong>在 "目标检测入门"系列第一部分介绍了：（1）图像梯度向量的概念以及 HOG 算法如何汇总一张图像中所有梯度向量的信息；（2）图像分割算法如何工作以检测可能包含对象的区域；（3）选择性搜索算法如何优化图像分割的结果，以获得更好的区域建议。</strong></p>
<p><strong>第2部分介绍了几种用于图像分类的经典卷积神经工作架构设计（AlexNet，VGG，ResNet），以及用于对象识别的 DPM（可变形零件模型）和 Overfeat 模型。</strong></p>
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<a class="article-title" href="/2020/07/10/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E5%85%A5%E9%97%A8-Part-1%EF%BC%9A%E6%A2%AF%E5%BA%A6%E5%90%91%E9%87%8F%EF%BC%8CHOG-%E5%92%8C-SS/"
>目标检测入门 Part 1：梯度向量，HOG 和 SS</a>
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<time datetime="2020-07-10T15:53:04.000Z" itemprop="datePublished">2020-07-10</time>
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<p><strong>在 "目标检测入门"系列文章中，将介绍一些用于图像处理和对象检测的基本概念，算法和流行的深度学习模型。第1部分介绍了梯度向量的概念，HOG（Histogram of Oriented Gradients）算法以及图像分割的选择性搜索。</strong></p>
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>建站过程简述</a>
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<p><strong>详细教程因需求不同和电脑系统不同而有所差异。这里仅列出自己思路和参考资料。</strong></p>
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$icon.removeClass('ri-file-copy-2-line');
$icon.addClass('ri-time-line');
let $span = $($btn.find('span'));
$span[0].innerText = 'COPY FAILED';
wait(function () { // 等待两秒钟后恢复
$icon.removeClass('ri-time-line');
$icon.addClass('ri-file-copy-2-line');
$span[0].innerText = 'COPY';
}, 2000);
});
}
initCopyCode();
}(window, document);
</script>
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</div><!-- hexo-inject:begin --><!-- Begin: Injected MathJax -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config("");
</script>
<script type="text/x-mathjax-config">
MathJax.Hub.Queue(function() {
var all = MathJax.Hub.getAllJax(), i;
for(i=0; i < all.length; i += 1) {
all[i].SourceElement().parentNode.className += ' has-jax';
}
});
</script>
<script type="text/javascript" src="">
</script>
<!-- End: Injected MathJax -->
<!-- hexo-inject:end -->
</body>
</html>