首发于 CV学习笔记汇总
写文章
点击打开用户名的主页
计算机视觉基本任务综述

计算机视觉基本任务综述

AdamLau
​ 关注他
40 人 赞同了该文章

写在前面：由于本人在本科阶段没有接触深度学习，仅了解部分传统图像处理方法，刚开始学习计算机视觉相关知识，因此想写点东西对所学做一些总结，如有问题，也请各位多多指教。本文出于方便整理的缘故，部分参考copy网络相关博客，如有侵权，请联系我删除。

计算机视觉基本任务共四大类：分类、目标检测、语义分割、实例分割

图1

a）分类(Classification)

图像分类要求给定一个图片输出图片里含有哪些分类，例如在图1(a)中检测出图中有瓶子、杯子以及立方体。

即将要介绍到的分类网络（ILSVRC历年冠亚军）：LeNet、AlexNet(2012冠军)、VGG(2014亚军)、GoogLeNet(2014冠军)、ResNet(2015冠军)、DenseNet

1.LeNet-5：卷积神经网络的祖师爷LeCun在1998年提出，用于解决手写数字识别的视觉任务。自那时起，CNN的最基本的架构就定下来了：卷积层、池化层、全连接层。conv1 (6) -> pool1 -> conv2 (16) -> pool2 -> fc3 (120) -> fc4 (84) -> fc5 (10) -> softmax 网络名称中有5表示它有5层conv/fc层。

创新点：定义了CNN的基本组件，是CNN的鼻祖。

LeNet torch 实现，应用于cifar-10

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, kernel_size=5)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.fc1 = nn.Linear(16*5*5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">x</span></code></pre></div><p data-pid="LWXkiMVx">2.AlexNet：在ILSVRC 2012夺得冠军，并领先第二名10.9个百分点。网络结构：conv1 (96) -&gt; pool1 -&gt; conv2 (256) -&gt; pool2 -&gt; conv3 (384) -&gt; conv4 (384) -&gt; conv5 (256) -&gt; pool5 -&gt; fc6 (4096) -&gt; fc7 (4096) -&gt; fc8 (1000) -&gt; softmax</p><figure data-size="normal"><noscript><img src="https://pic1.zhimg.com/v2-9d6827da91e857836f6ffdef99cb9a54_b.jpg" data-caption="" data-size="normal" data-rawwidth="1074" data-rawheight="517" class="origin_image zh-lightbox-thumb" width="1074" data-original="https://pic1.zhimg.com/v2-9d6827da91e857836f6ffdef99cb9a54_r.jpg"/></noscript><img src="https://pic1.zhimg.com/80/v2-9d6827da91e857836f6ffdef99cb9a54_720w.jpg" data-caption="" data-size="normal" data-rawwidth="1074" data-rawheight="517" class="origin_image zh-lightbox-thumb lazy" width="1074" data-original="https://pic1.zhimg.com/v2-9d6827da91e857836f6ffdef99cb9a54_r.jpg" data-actualsrc="https://pic1.zhimg.com/v2-9d6827da91e857836f6ffdef99cb9a54_b.jpg" data-lazy-status="ok"></figure><p data-pid="ErXlk4Nw">创新点：(1)<b>更深</b>的网络。(2)使用了<b>ReLU</b>激活函数，使之有更好的梯度特性、训练更快。(3)大量使用<b>数据增广</b>技术，防止过拟合。(4)让人们意识到利用<b>GPU加速</b>训练。(5)使用了<b>随机失活(dropout)</b>，该方法通过让全连接层的神经元（该模型在前两个全连接层引入Dropout）以一定的概率失去活性（比如0.5）失活的神经元不再参与前向和反向传播，相当于约有一半的神经元不再起作用。在测试的时候，让所有神经元的输出乘0.5。Dropout的引用，有效缓解了模型的过拟合。</p><p data-pid="bTPegVpk">AlexNet torch 实现，应用于cifar-10</p><div class="highlight"><pre><code class="language-python"><span class="n">NUM_CLASSES</span> <span class="o">=</span> <span class="mi">10</span>

  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10


  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8