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                <h2 id="1">1. 量化<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
<h3 id="11">1.1 图象分类<a class="headerlink" href="#11" title="Permanent link">#</a></h3>
<p>数据：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">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">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="">70.24%/89.03%</a></td>
<td align="center"><a href="">70.70%/89.55%</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="">71.36%/90.17%</a></td>
<td align="center"><a href="">72.02%/90.23%</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="">76.26%/92.81%</a></td>
<td align="center"><a href="">76.59%/93.04%</a></td>
</tr>
</tbody>
</table>
<p>量化训练前后，模型大小的变化对比如下：</p>
<table>
<thead>
<tr>
<th align="left">Model</th>
<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">MobileNetV1</td>
<td align="center">17M</td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="left">MobileNetV2</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="left">ResNet50</td>
<td align="center">99M</td>
<td align="center"></td>
<td align="center"></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：COCO 2017 </p>
<p>评价指标：mAP</p>
<p>输入尺寸：608</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
<td align="center"><a href="">27.9</a></td>
<td align="center"><a href="">28.0</a></td>
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</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="">40.4</a></td>
<td align="center"><a href="">40.6</a></td>
</tr>
</tbody>
</table>
<p>数据：WIDER-FACE</p>
<p>评价指标：Easy/Medium/Hard mAP</p>
<p>输入尺寸：640</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">BlazeFace</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar">0.915/0.892/0.797</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">BlazeFace-Lite</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar">0.909/0.885/0.781</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">BlazeFace-NAS</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar">0.837/0.807/0.658</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
</tbody>
</table>
<h3 id="13">1.3 图像分割<a class="headerlink" href="#13" title="Permanent link">#</a></h3>
<p>数据：Cityscapes</p>
<p>评价指标：mIoU</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">DeepLabv3+/MobileNetv1</td>
<td align="center"><a href="">63.26</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">DeepLabv3+/MobileNetv2</td>
<td align="center"><a href="https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz">69.81</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
</tbody>
</table>
<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：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">均匀剪枝</th>
<th align="center">敏感度剪枝</th>
<th align="center">自动剪枝</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
<td align="center"><a href="">69.4%/88.66%</a></td>
<td align="center"><a href="">69.8%/88.9%</a></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>
<td align="center"><a href="">65.79%/86.11%</a></td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">ResNet34</td>
<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar">74.57%/92.14%</a></td>
<td align="center"><a href="">70.99%/89.95%</a></td>
<td align="center">-</td>
<td align="center"><a href="">70.24%/89.63%</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：Pasacl VOC &amp; COCO 2017</p>
<p>评价指标：mAP</p>
<p>输入尺寸：608</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">数据集</th>
<th align="center">baseline</th>
<th align="center">敏感度剪枝</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
<td align="center"><a href="">76.2</a></td>
<td align="center"><a href="">77.59</a></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</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></td>
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">COCO</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></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：Cityscapes</p>
<p>评价指标：mIoU</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">Baseline</th>
<th align="center">剪枝</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">DeepLabv3+/MobileNetv2</td>
<td align="center"><a href="https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz">69.81</a></td>
<td align="center"></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：ImageNet1000类</p>
<p>评价指标：Top-1/Top-5准确率</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">baseline</th>
<th align="center">蒸馏后</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
<td align="center"><a href="">72.79%/90.69%</a> (teacher: ResNet50_vd<sup><a href="#trans1">1</a></sup>)</td>
</tr>
<tr>
<td align="center">MobileNetV2</td>
<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
<td align="center"><a href="">74.30%/91.52%</a> (teacher: ResNet50_vd)</td>
</tr>
<tr>
<td align="center">ResNet50</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar">76.50%/93.00%</a></td>
<td align="center"><a href="">77.40%/93.48%</a> (teacher: ResNet101<sup><a href="#trans2">2</a></sup>)</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a name="trans1">[1]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar">ResNet50_vd</a>预训练模型Top-1/Top-5准确率分别为79.12%/94.44%</p>
<p>带_vd后缀代表是开启了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：Pasacl VOC &amp; COCO 2017</p>
<p>评价指标：mAP</p>
<p>输入尺寸：608</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">数据集</th>
<th align="center">baseline</th>
<th align="center">蒸馏后</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</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">COCO</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>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a name="trans1">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
<p><a name="trans1">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
</div>
              
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