Deployed 9bcff00c with MkDocs version: 1.0.4

index.html

@@ -298,5 +298,5 @@
 
 <!--
 MkDocs version : 1.0.4
-Build Date UTC : 2020-01-03 03:53:47
+Build Date UTC : 2020-01-03 04:09:49
 -->

model_zoo2/index.html

@@ -212,6 +212,7 @@
 <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>
@@ -219,55 +220,64 @@
 </thead>
 <tbody>
 <tr>
-<td align="center">MobileNetV1 FP32</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">MobileNetV1 quant_post</td>
+<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 quant_aware</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>
 <tr>
-<td align="center">MobileNetV2 FP32</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"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNetV2 quant_post</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 quant_aware</td>
+<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 FP32</td>
+<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 quant_post</td>
+<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 quant_aware</td>
+<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>
@@ -280,6 +290,7 @@
 <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>
@@ -290,7 +301,8 @@
 </thead>
 <tbody>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 FP32</td>
+<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>
@@ -299,7 +311,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 quant_post</td>
+<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>
@@ -308,7 +321,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 quant_aware</td>
+<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>
@@ -318,6 +332,7 @@
 </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">-</td>
@@ -326,7 +341,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3 quant_post</td>
+<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">-</td>
@@ -335,7 +351,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3 quant_aware</td>
+<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">-</td>
@@ -350,6 +367,7 @@
 <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>
@@ -359,7 +377,8 @@
 </thead>
 <tbody>
 <tr>
-<td align="center">BlazeFace FP32</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>
@@ -367,7 +386,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace quant_post</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>
@@ -375,7 +395,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace quant_aware</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>
@@ -383,7 +404,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-Lite FP32</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>
@@ -391,7 +413,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-Lite quant_post</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>
@@ -399,7 +422,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-Lite quant_aware</td>
+<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>
@@ -407,7 +431,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-NAS FP32</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>
@@ -415,7 +440,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-NAS quant_post</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>
@@ -423,7 +449,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">BlazeFace-NAS quant_aware</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>
@@ -432,13 +459,13 @@
 </tr>
 </tbody>
 </table>
-<h3 id="_1"><a class="headerlink" href="#_1" 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">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>
@@ -447,43 +474,48 @@
 <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 quant_post</td>
+<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 quant_aware</td>
+<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 quant_post</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">DeepLabv3+/MobileNetv2 quant_aware</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>
-<h3 id="_2"><a class="headerlink" href="#_2" 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>
@@ -491,6 +523,7 @@
 <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>
@@ -500,20 +533,23 @@
 <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 uniform -50%</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"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNetV1 sensitive -xx%</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>
@@ -521,20 +557,23 @@
 </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 uniform -50%</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"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNetV2 sensitive -xx%</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>
@@ -542,20 +581,23 @@
 </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 uniform -50%</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"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">ResNet34 auto -50%</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>
@@ -563,13 +605,13 @@
 </tr>
 </tbody>
 </table>
-<h3 id="_3"><a class="headerlink" href="#_3" 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>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>
@@ -583,6 +625,7 @@
 <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>
@@ -593,7 +636,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 prune xx%</td>
+<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>
@@ -605,6 +649,7 @@
 </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>
@@ -615,7 +660,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 prune xx%</td>
+<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>
@@ -627,6 +673,7 @@
 </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>
@@ -637,7 +684,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">R50-dcn-YOLOv3 prune xx%</td>
+<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>
@@ -655,6 +703,7 @@
 <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>
@@ -664,13 +713,15 @@
 <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 prune xx%</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>
@@ -685,6 +736,7 @@
 <thead>
 <tr>
 <th align="center">Model</th>
+<th align="center">蒸馏 teacher</th>
 <th align="center">baseline</th>
 <th align="center">下载</th>
 </tr>
@@ -692,31 +744,37 @@
 <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 distilled (teacher: ResNet50_vd<sup><a href="#trans1">1</a></sup>)</td>
+<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 distilled (teacher: ResNet50_vd)</td>
+<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 distilled (teacher: ResNet101<sup><a href="#trans2">2</a></sup>)</td>
+<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>
@@ -734,6 +792,7 @@
 <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>
@@ -745,6 +804,7 @@
 <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>
@@ -753,7 +813,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 distilled (teacher: ResNet34-YOLOv3-VOC<sup><a href="#trans3">3</a></sup>)</td>
+<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>
@@ -763,6 +824,7 @@
 </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>
@@ -771,7 +833,8 @@
 <td align="center"><a href="">下载链接</a></td>
 </tr>
 <tr>
-<td align="center">MobileNet-V1-YOLOv3 distilled (teacher: ResNet34-YOLOv3-COCO<sup><a href="#trans4">4</a></sup>)</td>
+<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>