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+++ b/index.html
@@ -293,5 +293,5 @@

 <!--
 MkDocs version : 1.0.4
-Build Date UTC : 2020-01-02 11:52:28
+Build Date UTC : 2020-01-02 12:07:49
 -->
--- a/model_zoo/index.html
+++ b/model_zoo/index.html
@@ -234,11 +234,11 @@
 </tr>
 </tbody>
 </table>
-<p>量化训练前后，模型大小的变化对比如下：</p>
+<p>量化前后，模型大小的变化对比如下：</p>
 <table>
 <thead>
 <tr>
-<th align="left">Model</th>
+<th align="center">Model</th>
 <th align="center">FP32</th>
 <th align="center">离线量化</th>
 <th align="center">量化训练</th>
@@ -246,19 +246,19 @@
 </thead>
 <tbody>
 <tr>
-<td align="left">MobileNetV1</td>
+<td align="center">MobileNetV1</td>
 <td align="center">17M</td>
-<td align="center"><a href="">xx%/xx%</a></td>
-<td align="center"><a href="">xx%/xx%</a></td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
 </tr>
 <tr>
-<td align="left">MobileNetV2</td>
+<td align="center">MobileNetV2</td>
 <td align="center">xxM</td>
 <td align="center"></td>
 <td align="center"></td>
 </tr>
 <tr>
-<td align="left">ResNet50</td>
+<td align="center">ResNet50</td>
 <td align="center">99M</td>
 <td align="center"></td>
 <td align="center"></td>
@@ -329,6 +329,32 @@
 </tr>
 </tbody>
 </table>
+<p>量化前后，模型大小的变化对比如下：</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">FP32</th>
+<th align="center">离线量化</th>
+<th align="center">量化训练</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">xxM</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<h3 id="_1"><a class="headerlink" href="#_1" title="Permanent link">#</a></h3>
 <p>数据集：WIDER-FACE</p>
 <p>评价指标：Easy/Medium/Hard mAP</p>
 <table>
@@ -369,6 +395,38 @@
 </tr>
 </tbody>
 </table>
+<p>量化前后，模型大小的变化对比如下：</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">FP32</th>
+<th align="center">离线量化</th>
+<th align="center">量化训练</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">BlazeFace</td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
+</tr>
+<tr>
+<td align="center">BlazeFace-Lite</td>
+<td align="center">xxM</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">BlazeFace-NAS</td>
+<td align="center">xxM</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<h3 id="_2"><a class="headerlink" href="#_2" title="Permanent link">#</a></h3>
 <h3 id="13">1.3 图像分割<a class="headerlink" href="#13" title="Permanent link">#</a></h3>
 <p>数据集：Cityscapes</p>
 <table>
@@ -395,6 +453,32 @@
 </tr>
 </tbody>
 </table>
+<p>量化前后，模型大小的变化对比如下：</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">FP32</th>
+<th align="center">离线量化</th>
+<th align="center">量化训练</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv1</td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
+<td align="center">xxM</td>
+</tr>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">xxM</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<h3 id="_3"><a class="headerlink" href="#_3" title="Permanent link">#</a></h3>
 <h2 id="2">2. 剪枝<a class="headerlink" href="#2" title="Permanent link">#</a></h2>
 <h3 id="21">2.1 图像分类<a class="headerlink" href="#21" title="Permanent link">#</a></h3>
 <p>数据集：ImageNet1000类</p>
@@ -444,6 +528,42 @@
 </tr>
 </tbody>
 </table>
+<p>剪枝前后，模型大小和计算量的变化对比如下：</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">baseline FLOPs</th>
+<th align="center">baseline size</th>
+<th align="center">剪枝后 FlOPs</th>
+<th align="center">剪枝后 size</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNetV1</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+</tr>
+<tr>
+<td align="center">MobileNetV2</td>
+<td align="center">xx</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">ResNet34</td>
+<td align="center">xx</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<h3 id="_4"><a class="headerlink" href="#_4" title="Permanent link">#</a></h3>
 <h3 id="22">2.2 目标检测<a class="headerlink" href="#22" title="Permanent link">#</a></h3>
 <p>数据集：Pasacl VOC &amp; COCO 2017</p>
 <table>
@@ -532,6 +652,41 @@
 </tr>
 </tbody>
 </table>
+<p>剪枝前后，模型大小和计算量的变化对比如下：</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">baseline FLOPs</th>
+<th align="center">baseline size</th>
+<th align="center">剪枝后 FlOPs</th>
+<th align="center">剪枝后 size</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3-VOC</td>
+<td align="center">xx</td>
+<td align="center">xxM</td>
+<td align="center">xx</td>
+<td align="center">xxM</td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3-COCO</td>
+<td align="center">xx</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3-COCO</td>
+<td align="center">xx</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
 <h3 id="23">2.3 图像分割<a class="headerlink" href="#23" title="Permanent link">#</a></h3>
 <p>数据集：Cityscapes</p>
 <table>
@@ -550,6 +705,27 @@
 </tr>
 </tbody>
 </table>
+<p>剪枝前后，模型大小和计算量的变化对比如下：</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">baseline FLOPs</th>
+<th align="center">baseline size</th>
+<th align="center">剪枝后 FlOPs</th>
+<th align="center">剪枝后 size</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">xx</td>
+<td align="center">xxM</td>
+<td align="center">xx</td>
+<td align="center">xxM</td>
+</tr>
+</tbody>
+</table>
 <h2 id="3">3. 蒸馏<a class="headerlink" href="#3" title="Permanent link">#</a></h2>
 <h3 id="31">3.1 图象分类<a class="headerlink" href="#31" title="Permanent link">#</a></h3>
 <p>数据集：ImageNet1000类</p>

--- a/search/search_index.json
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