Deployed 9bcff00c with MkDocs version: 1.0.4

98cdcace · baiyfbupt · 624e277e · 98cdcace · 98cdcace · 98cdcace
24 changed file
--- a/404.html
+++ b/404.html
@@ -118,6 +118,11 @@
    <a class="" href="/model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="/model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/algo/algo/index.html
+++ b/algo/algo/index.html
@@ -174,6 +174,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/analysis_api/index.html
+++ b/api/analysis_api/index.html
@@ -137,6 +137,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/api_guide/index.html
+++ b/api/api_guide/index.html
@@ -125,6 +125,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/nas_api/index.html
+++ b/api/nas_api/index.html
@@ -139,6 +139,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/prune_api/index.html
+++ b/api/prune_api/index.html
@@ -143,6 +143,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/quantization_api/index.html
+++ b/api/quantization_api/index.html
@@ -143,6 +143,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/search_space/index.html
+++ b/api/search_space/index.html
@@ -142,6 +142,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/api/single_distiller_api/index.html
+++ b/api/single_distiller_api/index.html
@@ -143,6 +143,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/index.html
+++ b/index.html
@@ -143,6 +143,11 @@
    <a class="" href="model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;
@@ -293,5 +298,5 @@
 <!--
 MkDocs version : 1.0.4
-Build Date UTC : 2020-01-02 12:07:49
+Build Date UTC : 2020-01-03 02:52:52
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--- a/model_zoo/index.html
+++ b/model_zoo/index.html
@@ -165,6 +165,11 @@
    </ul>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../model_zoo2/">模型库2</a>
+	    </li>
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@@ -838,6 +843,8 @@
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+        <a href="../model_zoo2/" class="btn btn-neutral float-right" title="模型库2">Next <span class="icon icon-circle-arrow-right"></span></a>
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@@ -870,6 +877,8 @@
        <span><a href="../algo/algo/" style="color: #fcfcfc;">&laquo; Previous</a></span>
+        <span style="margin-left: 15px"><a href="../model_zoo2/" style="color: #fcfcfc">Next &raquo;</a></span>
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 </div>
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--- a/model_zoo2/index.html
+++ b/model_zoo2/index.html
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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+	    </li>
+            <li class="toctree-l1">
+    <span class="caption-text">API</span>
+    <ul class="subnav">
+                <li class="">
+    <a class="" href="../api/quantization_api/">量化</a>
+                </li>
+                <li class="">
+    <a class="" href="../api/prune_api/">剪枝与敏感度</a>
+                </li>
+                <li class="">
+    <a class="" href="../api/analysis_api/">模型分析</a>
+                </li>
+                <li class="">
+    <a class="" href="../api/single_distiller_api/">知识蒸馏</a>
+                </li>
+                <li class="">
+    <a class="" href="../api/nas_api/">SA搜索</a>
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+    <a class="" href="../api/search_space/">搜索空间</a>
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+                <li class="">
+    <a class="" href="../table_latency/">硬件延时评估表</a>
+                </li>
+    </ul>
+	    </li>
+            <li class="toctree-l1">
+    <span class="caption-text">教程</span>
+    <ul class="subnav">
+                <li class="">
+    <a class="" href="../tutorials/quant_post_demo/">离线量化</a>
+                </li>
+                <li class="">
+    <a class="" href="../tutorials/quant_aware_demo/">量化训练</a>
+                </li>
+                <li class="">
+    <a class="" href="../tutorials/quant_embedding_demo/">Embedding量化</a>
+                </li>
+                <li class="">
+    <a class="" href="../tutorials/nas_demo/">SA搜索</a>
+                </li>
+                <li class="">
+    <a class="" href="../tutorials/distillation_demo/">知识蒸馏</a>
+                </li>
+    </ul>
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+            <li class="toctree-l1">
+    <a class="" href="../algo/algo/">算法原理</a>
+	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../model_zoo/">模型库</a>
+	    </li>
+            <li class="toctree-l1 current">
+    <a class="current" href="./">模型库2</a>
+    <ul class="subnav">
+    <li class="toctree-l2"><a href="#1">1. 量化</a></li>
+        <ul>
+            <li><a class="toctree-l3" href="#11">1.1 图象分类</a></li>
+            <li><a class="toctree-l3" href="#121">1.2.1 目标检测</a></li>
+            <li><a class="toctree-l3" href="#122">1.2.2 目标检测</a></li>
+            <li><a class="toctree-l3" href="#13">1.3 图像分割</a></li>
+        </ul>
+    <li class="toctree-l2"><a href="#2">2. 剪枝</a></li>
+        <ul>
+            <li><a class="toctree-l3" href="#21">2.1 图像分类</a></li>
+            <li><a class="toctree-l3" href="#22">2.2 目标检测</a></li>
+            <li><a class="toctree-l3" href="#23">2.3 图像分割</a></li>
+        </ul>
+    <li class="toctree-l2"><a href="#3">3. 蒸馏</a></li>
+        <ul>
+            <li><a class="toctree-l3" href="#31">3.1 图象分类</a></li>
+            <li><a class="toctree-l3" href="#32">3.2 目标检测</a></li>
+        </ul>
+    </ul>
+	    </li>
+        </ul>
+      </div>
+      &nbsp;
+    </nav>
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+      <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
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+        <a href="..">PaddleSlim Docs</a>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="breadcrumbs navigation">
+  <ul class="wy-breadcrumbs">
+    <li><a href="..">Docs</a> &raquo;</li>
+    <li>模型库2</li>
+    <li class="wy-breadcrumbs-aside">
+        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/model_zoo2.md"
+          class="icon icon-github"> Edit on GitHub</a>
+    </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main">
+            <div class="section">
+                <h2 id="1">1. 量化<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
+<h3 id="11">1.1 图象分类<a class="headerlink" href="#11" title="Permanent link">#</a></h3>
+<p>数据集：ImageNet1000类</p>
+<p>评价指标：Top-1/Top-5准确率</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">Top-1/Top-5</th>
+<th align="center">模型大小（MB）</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNetV1 FP32</td>
+<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
+<td align="center">xx</td>
+</tr>
+<tr>
+<td align="center">MobileNetV1 quant_post</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNetV1 quant_aware</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNetV2 FP32</td>
+<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
+<td align="center">xx</td>
+</tr>
+<tr>
+<td align="center">MobileNetV2 quant_post</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNetV2 quant_aware</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">ResNet50 FP32</td>
+<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar">76.50%/93.00%</a></td>
+<td align="center">xx</td>
+</tr>
+<tr>
+<td align="center">ResNet50 quant_post</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">ResNet50 quant_aware</td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<h3 id="121">1.2.1 目标检测<a class="headerlink" href="#121" title="Permanent link">#</a></h3>
+<p>数据集：COCO 2017 </p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">输入尺寸</th>
+<th align="center">Image/GPU</th>
+<th align="center">Box AP</th>
+<th align="center">模型大小(MB)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 FP32</td>
+<td align="center">608</td>
+<td align="center">8</td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 FP32</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 FP32</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_post</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_post</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_post</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_aware</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_aware</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_aware</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 FP32</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar">41.4</a></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 FP32</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 FP32</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_post</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_post</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_post</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_aware</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_aware</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_aware</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<h3 id="122">1.2.2 目标检测<a class="headerlink" href="#122" title="Permanent link">#</a></h3>
+<p>数据集：COCO 2017 </p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">Image/GPU</th>
+<th align="center">输入608 Box AP</th>
+<th>输入416 Box AP</th>
+<th>输入320 Box AP</th>
+<th align="center">模型大小(MB)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 FP32</td>
+<td align="center">8</td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
+<td><a href="">29.3</a></td>
+<td><a href="">27.1</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_post</td>
+<td align="center"></td>
+<td align="center"></td>
+<td></td>
+<td></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3 quant_aware</td>
+<td align="center"></td>
+<td align="center"></td>
+<td></td>
+<td></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 FP32</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar">41.4</a></td>
+<td></td>
+<td></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_post</td>
+<td align="center"></td>
+<td align="center"></td>
+<td></td>
+<td></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 quant_aware</td>
+<td align="center"></td>
+<td align="center"></td>
+<td></td>
+<td></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>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">输入尺寸</th>
+<th align="center">Image/GPU</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">640</td>
+<td align="center">8</td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar">0.915/0.892/0.797</a></td>
+<td align="center"><a href="">xx/xx/xx</a></td>
+<td align="center"><a href="">xx/xx/xx</a></td>
+</tr>
+<tr>
+<td align="center">BlazeFace-Lite</td>
+<td align="center">640</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar">0.909/0.885/0.781</a></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">BlazeFace-NAS</td>
+<td align="center">640</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar">0.837/0.807/0.658</a></td>
+<td align="center"></td>
+<td align="center"></td>
+</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>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">FP32 mIoU</th>
+<th align="center">离线量化 mIoU</th>
+<th align="center">量化训练 mIoU</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv1</td>
+<td align="center"><a href="">63.26</a></td>
+<td align="center"><a href="">xx</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center"><a href="https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz">69.81</a></td>
+<td align="center"></td>
+<td align="center"></td>
+</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>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">Top-1/Top-5</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNetV1</td>
+<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
+</tr>
+<tr>
+<td align="center">MobileNetV1 uniform -50%</td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNetV1 sensitive -xx%</td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNetV2</td>
+<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
+</tr>
+<tr>
+<td align="center">MobileNetV2 uniform -50%</td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNetV2 sensitive -xx%</td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">ResNet34</td>
+<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar">74.57%/92.14%</a></td>
+</tr>
+<tr>
+<td align="center">ResNet34 uniform -50%</td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">ResNet34 auto -50%</td>
+<td align="center"></td>
+</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>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">数据集</th>
+<th align="center">输入尺寸</th>
+<th align="center">Image/GPU</th>
+<th align="center">baseline mAP</th>
+<th align="center">敏感度剪枝 mAP</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">Pasacl VOC</td>
+<td align="center">608</td>
+<td align="center">8</td>
+<td align="center"><a href="">76.2</a></td>
+<td align="center"><a href="">77.59</a> (-50%)</td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">Pasacl VOC</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"><a href="">76.7</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">Pasacl VOC</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"><a href="">75.2</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
+<td align="center"><a href="">29.56</a> (-20%)</td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"><a href="">29.3</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"><a href="">27.1</a></td>
+<td align="center"><a href="">xx</a></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">608</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar">41.4</a></td>
+<td align="center"><a href="">37.8</a> (-30%)</td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</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>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">Baseline mIoU</th>
+<th align="center">xx剪枝 mIoU</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center"><a href="https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz">69.81</a></td>
+<td align="center"><a href="">xx</a></td>
+</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>
+<p>评价指标：Top-1/Top-5准确率</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">baseline</th>
+<th align="center">蒸馏后</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNetV1</td>
+<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
+<td align="center"><a href="">72.79%/90.69%</a> (teacher: ResNet50_vd<sup><a href="#trans1">1</a></sup>)</td>
+</tr>
+<tr>
+<td align="center">MobileNetV2</td>
+<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
+<td align="center"><a href="">74.30%/91.52%</a> (teacher: ResNet50_vd)</td>
+</tr>
+<tr>
+<td align="center">ResNet50</td>
+<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar">76.50%/93.00%</a></td>
+<td align="center"><a href="">77.40%/93.48%</a> (teacher: ResNet101<sup><a href="#trans2">2</a></sup>)</td>
+</tr>
+</tbody>
+</table>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><a name="trans1">  [1]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar">ResNet50_vd</a>预训练模型Top-1/Top-5准确率分别为79.12%/94.44%</p>
+<p>带_vd后缀代表开启了Mixup训练，Mixup相关介绍参考<a href="https://arxiv.org/abs/1710.09412">mixup: Beyond Empirical Risk Minimization</a></p>
+<p><a name="trans1">[2]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar">ResNet101</a>预训练模型Top-1/Top-5准确率分别为77.56%/93.64%</p>
+</div>
+<h3 id="32">3.2 目标检测<a class="headerlink" href="#32" title="Permanent link">#</a></h3>
+<p>数据集：Pasacl VOC &amp; COCO 2017</p>
+<table>
+<thead>
+<tr>
+<th align="center">Model</th>
+<th align="center">数据集</th>
+<th align="center">输入尺寸</th>
+<th align="center">Image/GPU</th>
+<th align="center">baseline</th>
+<th align="center">蒸馏后 mAP</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">Pasacl VOC</td>
+<td align="center">640</td>
+<td align="center">16</td>
+<td align="center"><a href="">76.2</a></td>
+<td align="center"><a href="">79.0</a> (teacher: ResNet34-YOLOv3-VOC<sup><a href="#trans3">3</a></sup>)</td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">Pasacl VOC</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"><a href="">76.7</a></td>
+<td align="center"><a href="">78.2</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">Pasacl VOC</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"><a href="">75.2</a></td>
+<td align="center"><a href="">75.5</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">640</td>
+<td align="center"></td>
+<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
+<td align="center"><a href="">31.0</a> (teacher: ResNet34-YOLOv3-COCO<sup><a href="#trans4">4</a></sup>)</td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">416</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">COCO</td>
+<td align="center">320</td>
+<td align="center"></td>
+<td align="center"></td>
+<td align="center"></td>
+</tr>
+</tbody>
+</table>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><a name="trans1">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
+<p><a name="trans1">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
+</div>
+            </div>
+          </div>
+          <footer>
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+        <a href="../model_zoo/" class="btn btn-neutral" title="模型库"><span class="icon icon-circle-arrow-left"></span> Previous</a>
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+      <script src="../search/main.js" defer></script>
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--- a/search.html
+++ b/search.html
@@ -118,6 +118,11 @@
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+            <li class="toctree-l1">
+    <a class="" href="./model_zoo2/">模型库2</a>
+	    </li>
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--- a/search/search_index.json
+++ b/search/search_index.json
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    <url>
     <loc>None</loc>
-     <lastmod>2020-01-02</lastmod>
+     <lastmod>2020-01-03</lastmod>
     <changefreq>daily</changefreq>
    </url>
    <url>
     <loc>None</loc>
-     <lastmod>2020-01-02</lastmod>
+     <lastmod>2020-01-03</lastmod>
     <changefreq>daily</changefreq>
    </url>
    <url>
     <loc>None</loc>
-     <lastmod>2020-01-02</lastmod>
+     <lastmod>2020-01-03</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2020-01-03</lastmod>
     <changefreq>daily</changefreq>
    </url>
 </urlset>
\ No newline at end of file
--- a/sitemap.xml.gz
+++ b/sitemap.xml.gz
--- a/table_latency/index.html
+++ b/table_latency/index.html
@@ -143,6 +143,11 @@
    <a class="" href="../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/demo_guide/index.html
+++ b/tutorials/demo_guide/index.html
@@ -125,6 +125,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/distillation_demo/index.html
+++ b/tutorials/distillation_demo/index.html
@@ -148,6 +148,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/nas_demo/index.html
+++ b/tutorials/nas_demo/index.html
@@ -137,6 +137,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/quant_aware_demo/index.html
+++ b/tutorials/quant_aware_demo/index.html
@@ -139,6 +139,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/quant_embedding_demo/index.html
+++ b/tutorials/quant_embedding_demo/index.html
@@ -139,6 +139,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/quant_post_demo/index.html
+++ b/tutorials/quant_post_demo/index.html
@@ -139,6 +139,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;

--- a/tutorials/sensitivity_demo/index.html
+++ b/tutorials/sensitivity_demo/index.html
@@ -125,6 +125,11 @@
    <a class="" href="../../model_zoo/">模型库</a>
 	    </li>
+            <li class="toctree-l1">
+    <a class="" href="../../model_zoo2/">模型库2</a>
+	    </li>
        </ul>
      </div>
      &nbsp;