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2 changes: 1 addition & 1 deletion 2022/03/03/基于github-Hexo创建个人博客/index.html
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2 changes: 1 addition & 1 deletion 2022/03/28/pytorch相关/index.html
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<time title="修改时间：2022-05-19 17:30:05" itemprop="dateModified" datetime="2022-05-19T17:30:05+08:00">2022-05-19</time>
<time title="修改时间：2023-06-12 16:31:51" itemprop="dateModified" datetime="2023-06-12T16:31:51+08:00">2023-06-12</time>
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Expand Down Expand Up @@ -273,7 +273,7 @@ <h5 id="Fast-RCNN"><a href="#Fast-RCNN" class="headerlink" title="Fast-RCNN"></a
<p><img src="/2022/04/19/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E7%9B%B8%E5%85%B3/66a6d7b1dc3414493a3e7114c12d31bc483a7539.png" alt="img"></p>
<p>Faste R-CNN 的改进是：</p>
<ul>
<li>在拿到一张图片之后，首先使用 CNN 对图片进行特征提取（不是对图片中的锚框进行特征提取，而是对整张图片进行特征提取，仅在整张图像上执行卷积神经网络的前向传播），最终会得到一个 7 <em> 7 或者 14 </em> 14 的 feature map</li>
<li>在拿到一张图片之后，首先使用 CNN 对图片进行特征提取（不是对图片中的锚框进行特征提取，而是对整张图片进行特征提取，仅在整张图像上执行卷积神经网络的前向传播），最终会得到一个 $7 <em> 7$ 或者 $14 </em> 14$ 的 feature map</li>
<li>抽取完特征之后，再对图片进行锚框的选择（selective search），搜索到原始图片上的锚框之后将其（按照一定的比例）映射到 CNN 的输出上</li>
<li>映射完锚框之后，再使用 RoI pooling 对 CNN 输出的 feature map 上的锚框进行特征抽取，生成固定长度的特征（将 n * m 的矩阵拉伸成为 nm 维的向量），之后再通过一个全连接层（这样就不需要使用SVM一个一个的操作，而是一次性操作了）对每个锚框进行预测：物体的类别和真实的边缘框的偏移</li>
<li>Fast R-CNN 相对于 R-CNN 更快的原因是：Fast R-CNN 中的 CNN 不再对每个锚框抽取特征，而是对整个图片进行特征的提取（这样做的好处是：不同的锚框之间可能会有重叠的部分，如果对每个锚框都进行特征提取的话，可能会对重叠的区域进行多次重复的特征提取操作），然后再在整张图片的feature中找出原图中锚框对应的特征，最后一起做预测</li>
Expand Down Expand Up @@ -373,9 +373,8 @@ <h5 id="YOLO系列-YOLOv2"><a href="#YOLO系列-YOLOv2" class="headerlink" title
<p><strong>Batch Normalization</strong>：显著的提升了训练收敛的速度，减少了所需的正则化程度。通过在每个卷积层之后添加BN层，模型提高了2％的map，且可以丢弃dropout层。</p>
<p><strong>High Resolution Classifier</strong>：因为使用了pre train技术，在yolov1中我们使用的是Image Net的224x224图像进行训练，在预测的时候将输入的分辨率调为448x448。在yolov2中，使用448x448的Image Net的图片进行预训练，使map提升4％。</p>
<p><strong>Convolutional With Anchor Boxes</strong>：在yolov1中对边界框的预测是直接用全连接层来预测的，是不基于anchor的。而这样会导致边界框的定位误差较大。所以yolov2认为，预测基于anchor的偏移会使这个问题较为简化，并且让这个网络更加容易学习收敛。所以YOLOV2采用了Faster R-CNN的方法，引入了anchor，在网络结构方面将原来网络的全连接层和最后一个pooling层去掉，使得最后的卷积层能输出更高分辨率的特征图。我们还缩减了网络，用416x416分辨率大小作为输入，而不是448x448。这样做的原因是希望得到的特征图有着奇数的宽和高，这样特征图的中心就只有一个，因为大的目标一般会占据图像的中心，所以最好在中心有一个单一的位置来预测这些目标。网络会经过32倍下采样，最后输入13x13大小的特征图使用anchor，模型的mAP值从69.5降到了69.2，下降了一丢丢，而召回率却从81%提高到了88%，模型的提升空间有了较大的提升。</p>
<p><strong>Dimension Clusters</strong>使用了anchor的聚类。yolo网络是去学习bounding box的改变，而anchor的初始值的好坏会导致学习的速度以及学习的最终结果。如果最开始就选用了较好的anchor则会使网络效果更好。所以yolov2使用k-means聚类算法对训练集的boxes进行聚类，寻找合适的anchor。</p>
<p><strong>Direct location prediction</strong>：模型在基于anchor的训练的早期的大部分的不稳定性是来自于预测box的中心坐标，faster RCNN公式如下$x=(t_x*w_a)+x_a$ </p>
<p>$y=(t_y*h_a)+y_a$ 但这个公式没有对$t_x t_y$的值有所限制，会导致一次迭代更新之后的box中心值偏离网格，到其余网格去。我们更希望每个anchor仅能预测当前对应网格的物体，所以yolov2更新了迭代预测的公式，采用相对于网格的坐标位置进行预测，公式如下：</p>
<p><strong>Dimension Clusters:</strong>使用了anchor的聚类。yolo网络是去学习bounding box的改变，而anchor的初始值的好坏会导致学习的速度以及学习的最终结果。如果最开始就选用了较好的anchor则会使网络效果更好。所以yolov2使用k-means聚类算法对训练集的boxes进行聚类，寻找合适的anchor。</p>
<p><strong>Direct location prediction</strong>：模型在基于anchor的训练的早期的大部分的不稳定性是来自于预测box的中心坐标，faster RCNN公式如下$x=(t_x<em>w_a)+x_a$ $y=(t_y</em>h_a)+y_a$ 但这个公式没有对$t_x t_y$的值有所限制，会导致一次迭代更新之后的box中心值偏离网格，到其余网格去。我们更希望每个anchor仅能预测当前对应网格的物体，所以yolov2更新了迭代预测的公式，采用相对于网格的坐标位置进行预测，公式如下：</p>
<p><img src="/2022/04/19/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E7%9B%B8%E5%85%B3/image-20220413110720861.png" alt="image-20220413110720861"></p>
<p>其中参数定义如下图：</p>
<p><img src="/2022/04/19/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E7%9B%B8%E5%85%B3/anchor.png" alt="anchor"></p>
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