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@@ -48,6 +48,11 @@
          
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--- a/algo/algo/index.html
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@@ -55,6 +55,11 @@
          
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@@ -491,8 +481,6 @@ e^{\frac{(r_k-r)}{T_k}} & r_k < r\\
  
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--- a/api/analysis_api/index.html
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@@ -55,6 +55,11 @@
          
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@@ -55,6 +55,11 @@
          
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--- a/api/nas_api/index.html
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@@ -55,6 +55,11 @@
          
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--- a/api/prune_api/index.html
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@@ -55,6 +55,11 @@
          
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--- a/api/quantization_api/index.html
+++ b/api/quantization_api/index.html
@@ -55,6 +55,11 @@
          
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--- a/api/search_space/index.html
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--- a/api/single_distiller_api/index.html
+++ b/api/single_distiller_api/index.html
@@ -55,6 +55,11 @@
          
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@@ -73,6 +73,11 @@
          
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--- a/model_zoo/index.html
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@@ -53,6 +53,51 @@
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+    <a class="current" href="./">模型库</a>
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+    <li class="toctree-l2"><a href="#1">1. 图象分类</a></li>
+    
+        <ul>
+        
+            <li><a class="toctree-l3" href="#11">1.1 量化</a></li>
+        
+            <li><a class="toctree-l3" href="#12">1.2 剪枝</a></li>
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+            <li><a class="toctree-l3" href="#13">1.3 蒸馏</a></li>
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+        
+            <li><a class="toctree-l3" href="#32">3.2 剪枝</a></li>
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@@ -210,637 +200,792 @@
          <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>
+                <h2 id="1">1. 图象分类<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
 <p>数据集：ImageNet1000类</p>
-<p>评价指标：Top-1/Top-5准确率</p>
+<h3 id="11">1.1 量化<a class="headerlink" href="#11" title="Permanent link">#</a></h3>
 <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>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">Top-1/Top-5 Acc</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">下载</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"><a href="">xx%/xx%</a></td>
-<td align="center"><a href="">xx%/xx%</a></td>
+<td align="center">MobileNetV1</td>
+<td align="center">-</td>
+<td align="center">70.99%/89.68%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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"></td>
-<td align="center"></td>
+<td align="center">MobileNetV1</td>
+<td align="center">quant_post</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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"></td>
-<td align="center"></td>
+<td align="center">MobileNetV1</td>
+<td align="center">quant_aware</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">MobileNetV2</td>
+<td align="center">-</td>
+<td align="center">72.15%/90.65%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
-</thead>
-<tbody>
 <tr>
-<td align="center">MobileNetV1</td>
-<td align="center">17M</td>
-<td align="center">xxM</td>
-<td align="center">xxM</td>
+<td align="center">MobileNetV2</td>
+<td align="center">quant_post</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
 <td align="center">MobileNetV2</td>
-<td align="center">xxM</td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">quant_aware</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">ResNet50</td>
+<td align="center">-</td>
+<td align="center">76.50%/93.00%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
 <td align="center">ResNet50</td>
-<td align="center">99M</td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">quant_post</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">ResNet50</td>
+<td align="center">quant_aware</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 </tbody>
 </table>
-<h3 id="12">1.2 目标检测<a class="headerlink" href="#12" title="Permanent link">#</a></h3>
-<p>数据集：COCO 2017 </p>
+<h3 id="12">1.2 剪枝<a class="headerlink" href="#12" title="Permanent link">#</a></h3>
 <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 BoxAP</th>
-<th align="center">离线量化 BoxAP</th>
-<th align="center">量化训练 BoxAP</th>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">Top-1/Top-5 Acc</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">FLOPs（M）</th>
+<th align="center">arm时延（ms）</th>
+<th align="center">P4时延（ms）</th>
+<th align="center">下载</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3</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>
-<td align="center"><a href="">xx</a></td>
+<td align="center">MobileNetV1</td>
+<td align="center">-</td>
+<td align="center">70.99%/89.68%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">416</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">MobileNetV1</td>
+<td align="center">uniform -xx%</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">320</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">MobileNetV1</td>
+<td align="center">sensitive -xx%</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3</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>
-<td align="center"></td>
+<td align="center">MobileNetV2</td>
+<td align="center">-</td>
+<td align="center">72.15%/90.65%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">416</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">MobileNetV2</td>
+<td align="center">uniform -xx%</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">320</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">MobileNetV2</td>
+<td align="center">sensitive -xx%</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">ResNet34</td>
+<td align="center">-</td>
+<td align="center">74.57%/92.14%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </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>
+<td align="center">ResNet34</td>
+<td align="center">uniform -xx%</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">xxM</td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">ResNet34</td>
+<td align="center">auto -xx%</td>
+<td align="center">xx%/xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<h3 id="13">1.3 蒸馏<a class="headerlink" href="#13" title="Permanent link">#</a></h3>
 <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>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">Top-1/Top-5 Acc</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">下载</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>
+<td align="center">MobileNetV1</td>
+<td align="center">-</td>
+<td align="center">70.99%/89.68%</td>
+<td align="center">17</td>
+<td align="center"><a href="">下载链接</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>
+<td align="center">ResNet50_vd</td>
+<td align="center">-</td>
+<td align="center">79.12%/94.44%</td>
+<td align="center">99</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">MobileNetV1</td>
+<td align="center">ResNet50_vd<sup><a href="#trans1">1</a></sup> distill</td>
+<td align="center">72.77%/90.68%</td>
+<td align="center">17</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">MobileNetV2</td>
+<td align="center">-</td>
+<td align="center">72.15%/90.65%</td>
+<td align="center">15</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
-</thead>
-<tbody>
 <tr>
-<td align="center">BlazeFace</td>
-<td align="center">xxM</td>
-<td align="center">xxM</td>
-<td align="center">xxM</td>
+<td align="center">MobileNetV2</td>
+<td align="center">ResNet50_vd distill</td>
+<td align="center">74.28%/91.53%</td>
+<td align="center">15</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">xxM</td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">ResNet50</td>
+<td align="center">-</td>
+<td align="center">76.50%/93.00%</td>
+<td align="center">99</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">xxM</td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">ResNet101</td>
+<td align="center">-</td>
+<td align="center">77.56%/93.64%</td>
+<td align="center">173</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">ResNet50</td>
+<td align="center">ResNet101 distill</td>
+<td align="center">77.29%/93.65%</td>
+<td align="center">99</td>
+<td align="center"><a href="">下载链接</a></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>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><a name="trans1">[1]</a>：带_vd后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考<a href="https://arxiv.org/abs/1710.09412">mixup: Beyond Empirical Risk Minimization</a></p>
+</div>
+<h2 id="2">2. 目标检测<a class="headerlink" href="#2" title="Permanent link">#</a></h2>
+<p>数据集：Pasacl VOC &amp; COCO 2017 </p>
+<h3 id="21">2.1 量化<a class="headerlink" href="#21" title="Permanent link">#</a></h3>
+<p>数据集： COCO 2017</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>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">数据集</th>
+<th align="center">Image/GPU</th>
+<th align="center">输入608 Box AP</th>
+<th align="center">输入416 Box AP</th>
+<th align="center">输入320 Box AP</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">下载</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>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">-</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">29.3</td>
+<td align="center">29.3</td>
+<td align="center">27.1</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</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>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">quant_post</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">quant_aware</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </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>
+<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
+<td align="center">-</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">41.4</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">xxM</td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
+<td align="center">quant_post</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
+<td align="center">quant_aware</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<p>数据集：WIDER-FACE</p>
 <table>
 <thead>
 <tr>
-<th align="center">Model</th>
-<th align="center">Top-1/Top-5</th>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">Image/GPU</th>
+<th align="center">输入尺寸</th>
+<th align="center">Easy/Medium/Hard</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">下载</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>
+<td align="center">BlazeFace</td>
+<td align="center">-</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">0.915/0.892/0.797</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNetV2 sensitive -xx%</td>
-<td align="center"></td>
+<td align="center">BlazeFace</td>
+<td align="center">quant_post</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">xx/xx/xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">BlazeFace</td>
+<td align="center">quant_aware</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">xx/xx/xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">ResNet34 uniform -50%</td>
-<td align="center"></td>
+<td align="center">BlazeFace-Lite</td>
+<td align="center">-</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">0.909/0.885/0.781</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">ResNet34 auto -50%</td>
-<td align="center"></td>
+<td align="center">BlazeFace-Lite</td>
+<td align="center">quant_post</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">xx/xx/xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</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>
+<td align="center">BlazeFace-Lite</td>
+<td align="center">quant_aware</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">xx/xx/xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </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">BlazeFace-NAS</td>
+<td align="center">-</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">0.837/0.807/0.658</td>
 <td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNetV2</td>
+<td align="center">BlazeFace-NAS</td>
+<td align="center">quant_post</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">xx/xx/xx</td>
 <td align="center">xx</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">ResNet34</td>
+<td align="center">BlazeFace-NAS</td>
+<td align="center">quant_aware</td>
+<td align="center">8</td>
+<td align="center">640</td>
+<td align="center">xx/xx/xx</td>
 <td align="center">xx</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center"><a href="">下载链接</a></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>
+<h3 id="22">2.2 剪枝<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">模型</th>
+<th align="center">压缩方法</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>
+<th align="center">输入608 Box AP</th>
+<th align="center">输入416 Box AP</th>
+<th align="center">输入320 Box AP</th>
+<th align="center">模型大小(MB)</th>
+<th align="center">FLOPs（M）</th>
+<th align="center">arm时延（ms）</th>
+<th align="center">P4时延（ms）</th>
+<th align="center">下载</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">-</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>
+<td align="center">76.2</td>
+<td align="center">76.7</td>
+<td align="center">75.3</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
 <td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">sensitive  -xx%</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>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</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>
+<td align="center">-</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">29.3</td>
+<td align="center">29.3</td>
+<td align="center">27.1</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
 <td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">sensitive -xx%</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>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">-</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>
+<td align="center">8</td>
+<td align="center">39.1</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">sensitive -xx%</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>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
 <td align="center">R50-dcn-YOLOv3</td>
+<td align="center">sensitive -xx%</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>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
+<td align="center">-</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">41.4</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
+<td align="center">sensitive -xx%</td>
 <td align="center">COCO</td>
-<td align="center">416</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3</td>
+<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
+<td align="center">sensitive -xx%</td>
 <td align="center">COCO</td>
-<td align="center">320</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
+<td align="center">8</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</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>
+<h3 id="23">2.3 蒸馏<a class="headerlink" href="#23" 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">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>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">数据集</th>
+<th align="center">Image/GPU</th>
+<th align="center">输入608 Box AP</th>
+<th align="center">输入416 Box AP</th>
+<th align="center">输入320 Box AP</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">下载</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>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">-</td>
+<td align="center">Pascal VOC</td>
+<td align="center">8</td>
+<td align="center">76.2</td>
+<td align="center">76.7</td>
+<td align="center">75.3</td>
+<td align="center">94</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">ResNet34-YOLOv3</td>
+<td align="center">-</td>
+<td align="center">Pascal VOC</td>
+<td align="center">8</td>
+<td align="center">82.6</td>
+<td align="center">81.9</td>
+<td align="center">80.1</td>
+<td align="center">162</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">ResNet34-YOLOv3 distill</td>
+<td align="center">Pascal VOC</td>
+<td align="center">8</td>
+<td align="center">79.0</td>
+<td align="center">78.2</td>
+<td align="center">75.5</td>
+<td align="center">94</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">-</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">29.3</td>
+<td align="center">29.3</td>
+<td align="center">27.0</td>
+<td align="center">95</td>
+<td align="center"><a href="">下载链接</a></td>
 </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>
+<td align="center">ResNet34-YOLOv3</td>
+<td align="center">-</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">36.2</td>
+<td align="center">34.3</td>
+<td align="center">31.4</td>
+<td align="center">163</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">MobileNet-V1-YOLOv3</td>
+<td align="center">ResNet34-YOLOv3 distill</td>
+<td align="center">COCO</td>
+<td align="center">8</td>
+<td align="center">31.4</td>
+<td align="center">30.0</td>
+<td align="center">27.1</td>
+<td align="center">95</td>
+<td align="center"><a href="">下载链接</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>
+<h2 id="3">3. 图像分割<a class="headerlink" href="#3" title="Permanent link">#</a></h2>
+<p>数据集：Cityscapes</p>
+<h3 id="31">3.1 量化<a class="headerlink" href="#31" title="Permanent link">#</a></h3>
 <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>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">mIoU</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">下载</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">DeepLabv3+/MobileNetv1</td>
+<td align="center">-</td>
+<td align="center">63.26</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
+</tr>
+<tr>
+<td align="center">DeepLabv3+/MobileNetv1</td>
+<td align="center">quant_post</td>
 <td align="center">xx</td>
-<td align="center">xxM</td>
 <td align="center">xx</td>
-<td align="center">xxM</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">DeepLabv3+/MobileNetv1</td>
+<td align="center">quant_aware</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></td>
 </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>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">-</td>
+<td align="center">69.81</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">quant_post</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">quant_aware</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
+<h3 id="32">3.2 剪枝<a class="headerlink" href="#32" title="Permanent link">#</a></h3>
 <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>
+<th align="center">模型</th>
+<th align="center">压缩方法</th>
+<th align="center">mIoU</th>
+<th align="center">模型大小（MB）</th>
+<th align="center">FLOPs（M）</th>
+<th align="center">arm时延（ms）</th>
+<th align="center">P4时延（ms）</th>
+<th align="center">下载</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>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">-</td>
+<td align="center">69.81</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</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"></td>
-<td align="center"></td>
+<td align="center">DeepLabv3+/MobileNetv2</td>
+<td align="center">prune -xx%</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center">xx</td>
+<td align="center"><a href="">下载链接</a></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>
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-<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>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">压缩方法</th>
-<th align="center">Top-1/Top-5</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">-</td>
-<td align="center">70.99%/89.68%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">quant_psot</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">quant_aware</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">-</td>
-<td align="center">72.15%/90.65%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">quant_post</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">quant_aware</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">-</td>
-<td align="center">76.50%/93.00%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">quant_post</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">quant_aware</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<h3 id="12">1.2 目标检测<a class="headerlink" href="#12" title="Permanent link">#</a></h3>
-<p>数据集：COCO 2017 </p>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">压缩方法</th>
-<th align="center">Image/GPU</th>
-<th align="center">输入608 Box AP</th>
-<th align="center">输入416 Box AP</th>
-<th align="center">输入320 Box AP</th>
-<th align="center">模型大小(MB)</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">29.3</td>
-<td align="center">29.3</td>
-<td align="center">27.1</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3 FP32</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">41.4</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<p>数据集：WIDER-FACE</p>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">压缩方法</th>
-<th align="center">Image/GPU</th>
-<th align="center">输入尺寸</th>
-<th align="center">Easy/Medium/Hard</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">BlazeFace</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">0.915/0.892/0.797</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">0.909/0.885/0.781</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">0.837/0.807/0.658</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<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">压缩方法</th>
-<th align="center">mIoU</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv1</td>
-<td align="center">-</td>
-<td align="center">63.26</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv1</td>
-<td align="center">quant_post</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv1</td>
-<td align="center">quant_aware</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">-</td>
-<td align="center">69.81</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">quant_post</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">quant_aware</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<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">压缩方法</th>
-<th align="center">Top-1/Top-5</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">FLOPs</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">-</td>
-<td align="center">70.99%/89.68%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">uniform -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">sensitive -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">-</td>
-<td align="center">72.15%/90.65%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">uniform -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">sensitive -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet34</td>
-<td align="center">-</td>
-<td align="center">74.57%/92.14%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet34</td>
-<td align="center">uniform -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet34</td>
-<td align="center">auto -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<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>压缩方法</th>
-<th align="center">数据集</th>
-<th align="center">Image/GPU</th>
-<th align="center">输入608 mAP</th>
-<th align="center">输入416 mAP</th>
-<th align="center">输入320  mAP</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">FLOPs</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td>-</td>
-<td align="center">Pasacl VOC</td>
-<td align="center">8</td>
-<td align="center">76.2</td>
-<td align="center">76.7</td>
-<td align="center">75.3</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td>uniform  -xx%</td>
-<td align="center">Pasacl VOC</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td>-</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">29.3</td>
-<td align="center">29.3</td>
-<td align="center">27.1</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td>uniform -xx%</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td>-</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">41.4</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td>uniform -xx%</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></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">压缩方法</th>
-<th align="center">mIoU</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">FLOPs</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">-</td>
-<td align="center">69.81</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">prune -xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></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>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">蒸馏 teacher</th>
-<th align="center">Top-1/Top-5</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">-</td>
-<td align="center">70.99%/89.68%</td>
-<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">ResNet50_vd<sup><a href="#trans1">1</a></sup></td>
-<td align="center">72.79%/90.69%</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">-</td>
-<td align="center">72.15%/90.65%</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">ResNet50_vd<sup><a href="#trans1">1</a></sup></td>
-<td align="center">74.30%/91.52%</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">-</td>
-<td align="center">76.50%/93.00%</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">ResNet101<sup><a href="#trans2">2</a></sup></td>
-<td align="center">77.40%/93.48%</td>
-<td align="center"><a href="">下载链接</a></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="trans2">[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">蒸馏 teacher</th>
-<th align="center">数据集</th>
-<th align="center">Image/GPU</th>
-<th align="center">输入640 mAP</th>
-<th align="center">输入416 mAP</th>
-<th align="center">输入320 mAP</th>
-<th align="center">下载链接</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">-</td>
-<td align="center">Pasacl VOC</td>
-<td align="center">16</td>
-<td align="center">76.2</td>
-<td align="center">76.7</td>
-<td align="center">75.3</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">ResNet34-YOLOv3-VOC<sup><a href="#trans3">3</a></sup></td>
-<td align="center">Pasacl VOC</td>
-<td align="center">16</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">-</td>
-<td align="center">COCO</td>
-<td align="center">16</td>
-<td align="center">29.3</td>
-<td align="center">29.3</td>
-<td align="center">27.1</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">ResNet34-YOLOv3-COCO<sup><a href="#trans4">4</a></sup></td>
-<td align="center">COCO</td>
-<td align="center">16</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p><a name="trans3">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
-<p><a name="trans4">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
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-                <h3 id="1">1. 图象分类<a class="headerlink" href="#1" title="Permanent link">#</a></h3>
-<p>数据集：ImageNet1000类</p>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">压缩方法</th>
-<th align="center">Top-1/Top-5</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">FLOPs</th>
-<th>下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">-</td>
-<td align="center">70.99%/89.68%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">quant_psot</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">quant_aware</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">uniform -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">sensitive -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV1</td>
-<td align="center">ResNet50_vd<sup><a href="#trans1">1</a></sup> distill</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">-</td>
-<td align="center">72.15%/90.65%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">quant_post</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">quant_aware</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">uniform -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">sensitive -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNetV2</td>
-<td align="center">ResNet50_vd<sup><a href="#trans1">1</a></sup> distill</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet34</td>
-<td align="center">-</td>
-<td align="center">74.57%/92.14%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet34</td>
-<td align="center">uniform -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet34</td>
-<td align="center">auto -xx%</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">-</td>
-<td align="center">76.50%/93.00%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">quant_post</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">quant_aware</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">ResNet50</td>
-<td align="center">ResNet101<sup><a href="#trans2">2</a></sup> distill</td>
-<td align="center">xx%/xx%</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<h3 id="2">2. 目标检测<a class="headerlink" href="#2" 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">压缩方法</th>
-<th align="center">数据集</th>
-<th align="center">Image/GPU</th>
-<th align="center">输入608 Box AP</th>
-<th align="center">输入416 Box AP</th>
-<th align="center">输入320 Box AP</th>
-<th align="center">模型大小(MB)</th>
-<th align="center">FLOPs</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">-</td>
-<td align="center">Pasacl VOC</td>
-<td align="center">8</td>
-<td align="center">76.2</td>
-<td align="center">76.7</td>
-<td align="center">75.3</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">uniform  -xx%</td>
-<td align="center">Pasacl VOC</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">ResNet34-YOLOv3<sup><a href="#trans3">3</a></sup> distill</td>
-<td align="center">Pasacl VOC</td>
-<td align="center">16</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">-</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">29.3</td>
-<td align="center">29.3</td>
-<td align="center">27.1</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">quant_post</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">quant_aware</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">uniform -xx%</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">MobileNet-V1-YOLOv3</td>
-<td align="center">ResNet34-YOLOv3<sup><a href="#trans4">4</a></sup> distill</td>
-<td align="center">COCO</td>
-<td align="center">16</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3 FP32</td>
-<td align="center">-</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">41.4</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">quant_post</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">quant_aware</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">R50-dcn-YOLOv3</td>
-<td align="center">uniform -xx%</td>
-<td align="center">COCO</td>
-<td align="center">8</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<p>数据集：WIDER-FACE</p>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">压缩方法</th>
-<th align="center">Image/GPU</th>
-<th align="center">输入尺寸</th>
-<th align="center">Easy/Medium/Hard</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">BlazeFace</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">0.915/0.892/0.797</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">0.909/0.885/0.781</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-Lite</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">-</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">0.837/0.807/0.658</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">quant_post</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">BlazeFace-NAS</td>
-<td align="center">quant_aware</td>
-<td align="center">8</td>
-<td align="center">640</td>
-<td align="center">xx/xx/xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-</tbody>
-</table>
-<h3 id="3">3. 图像分割<a class="headerlink" href="#3" title="Permanent link">#</a></h3>
-<p>数据集：Cityscapes</p>
-<table>
-<thead>
-<tr>
-<th align="center">Model</th>
-<th align="center">压缩方法</th>
-<th align="center">mIoU</th>
-<th align="center">模型大小（MB）</th>
-<th align="center">FLOPs</th>
-<th align="center">下载</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv1</td>
-<td align="center">-</td>
-<td align="center">63.26</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv1</td>
-<td align="center">quant_post</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv1</td>
-<td align="center">quant_aware</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">-</td>
-<td align="center">69.81</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">quant_post</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">quant_aware</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">-</td>
-<td align="center"><a href="">下载链接</a></td>
-</tr>
-<tr>
-<td align="center">DeepLabv3+/MobileNetv2</td>
-<td align="center">prune -xx%</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center">xx</td>
-<td align="center"><a href="">下载链接</a></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="trans2">[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>
-<p><a name="trans3">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
-<p><a name="trans4">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
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--- a/search/search_index.json
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-     <changefreq>daily</changefreq>
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@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../model_zoo/">模型库</a>
+	    </li>
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+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
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                <li class="">
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--- a/tutorials/demo_guide/index.html
+++ b/tutorials/demo_guide/index.html
@@ -55,6 +55,11 @@
          
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+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
    <ul class="subnav">
                <li class="">
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    <a class="" href="../../algo/algo/">算法原理</a>
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--- a/tutorials/distillation_demo/index.html
+++ b/tutorials/distillation_demo/index.html
@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
    <ul class="subnav">
                <li class="">
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    <a class="" href="../../algo/algo/">算法原理</a>
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--- a/tutorials/nas_demo/index.html
+++ b/tutorials/nas_demo/index.html
@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
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-          
        </ul>
      </div>
      &nbsp;

--- a/tutorials/pruning_demo/index.html
+++ b/tutorials/pruning_demo/index.html
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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+    <ul class="subnav">
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+    <li>卷积通道剪裁示例</li>
+    <li class="wy-breadcrumbs-aside">
+      
+        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/tutorials/pruning_demo.md"
+          class="icon icon-github"> Edit on GitHub</a>
+      
+    </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main">
+            <div class="section">
+              
+                <h1 id="_1">卷积通道剪裁示例<a class="headerlink" href="#_1" title="Permanent link">#</a></h1>
+<p>本示例将演示如何按指定的剪裁率对每个卷积层的通道数进行剪裁。该示例默认会自动下载并使用mnist数据。</p>
+<p>当前示例支持以下分类模型：</p>
+<ul>
+<li>MobileNetV1</li>
+<li>MobileNetV2</li>
+<li>ResNet50</li>
+<li>PVANet</li>
+</ul>
+<h2 id="_2">接口介绍<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
+<p>该示例使用了<code>paddleslim.Pruner</code>工具类，用户接口使用介绍请参考：<a href="https://paddlepaddle.github.io/PaddleSlim/api/prune_api/">API文档</a></p>
+<h2 id="_3">确定待裁参数<a class="headerlink" href="#_3" title="Permanent link">#</a></h2>
+<p>不同模型的参数命名不同，在剪裁前需要确定待裁卷积层的参数名称。可通过以下方法列出所有参数名：</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">for</span> <span class="nv">param</span> <span class="nv">in</span> <span class="nv">program</span>.<span class="nv">global_block</span><span class="ss">()</span>.<span class="nv">all_parameters</span><span class="ss">()</span>:
+    <span class="nv">print</span><span class="ss">(</span><span class="s2">&quot;</span><span class="s">param name: {}; shape: {}</span><span class="s2">&quot;</span>.<span class="nv">format</span><span class="ss">(</span><span class="nv">param</span>.<span class="nv">name</span>, <span class="nv">param</span>.<span class="nv">shape</span><span class="ss">))</span>
+</pre></div>
+</td></tr></table>
+
+<p>在<code>train.py</code>脚本中，提供了<code>get_pruned_params</code>方法，根据用户设置的选项<code>--model</code>确定要裁剪的参数。</p>
+<h2 id="_4">启动裁剪任务<a class="headerlink" href="#_4" title="Permanent link">#</a></h2>
+<p>通过以下命令启动裁剪任务：</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">export</span> <span class="n">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="mi">0</span>
+<span class="n">python</span> <span class="n">train</span><span class="p">.</span><span class="n">py</span>
+</pre></div>
+</td></tr></table>
+
+<p>执行<code>python train.py --help</code>查看更多选项。</p>
+<h2 id="_5">注意<a class="headerlink" href="#_5" title="Permanent link">#</a></h2>
+<ol>
+<li>在接口<code>paddle.Pruner.prune</code>的参数中，<code>params</code>和<code>ratios</code>的长度需要一样。</li>
+</ol>
+              
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--- a/tutorials/quant_aware_demo/index.html
+++ b/tutorials/quant_aware_demo/index.html
@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
    <ul class="subnav">
                <li class="">
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--- a/tutorials/quant_embedding_demo/index.html
+++ b/tutorials/quant_embedding_demo/index.html
@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
    <ul class="subnav">
                <li class="">
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    <a class="" href="../../algo/algo/">算法原理</a>
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+++ b/tutorials/quant_post_demo/index.html
@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
    <ul class="subnav">
                <li class="">
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    <a class="" href="../../algo/algo/">算法原理</a>
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+++ b/tutorials/sensitivity_demo/index.html
@@ -55,6 +55,11 @@
          
            <li class="toctree-l1">
 		
+    <a class="" href="../../model_zoo/">模型库</a>
+	    </li>
+          
+            <li class="toctree-l1">
+		
    <span class="caption-text">API</span>
    <ul class="subnav">
                <li class="">
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    <a class="" href="../../algo/algo/">算法原理</a>
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