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            <li><a class="toctree-l3" href="#12">1.2 剪裁</a></li>
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            <li><a class="toctree-l3" href="#22">2.2 剪裁</a></li>
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                <h2 id="1">1. 图象分类<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
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<p>数据集：ImageNet1000类</p>
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<h3 id="11">1.1 量化<a class="headerlink" href="#11" title="Permanent link">#</a></h3>
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<table>
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<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>
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<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>
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<td align="center">xx%/xx%</td>
<td align="center">xx</td>
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<td align="center">MobileNetV1</td>
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<td align="center">xx%/xx%</td>
<td align="center">xx</td>
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<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>
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<td align="center">xx</td>
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<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>
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<td align="center">ResNet50</td>
<td align="center">-</td>
<td align="center">76.50%/93.00%</td>
<td align="center">xx</td>
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<td align="center">xx%/xx%</td>
<td align="center">xx</td>
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<td align="center">ResNet50</td>
<td align="center">quant_aware</td>
<td align="center">xx%/xx%</td>
<td align="center">xx</td>
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<h3 id="12">1.2 剪裁<a class="headerlink" href="#12" title="Permanent link">#</a></h3>
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<th align="center">模型大小（MB）</th>
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<th align="center">GFLOPs</th>
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<th align="center">下载</th>
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<td align="center">17</td>
<td align="center">1.11</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">MobileNetV1</td>
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<td align="center">uniform -50%</td>
<td align="center">69.4%/88.66%</td>
<td align="center">9</td>
<td align="center">0.56</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">MobileNetV1</td>
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<td align="center">sensitive -30%</td>
<td align="center">70.4%/89.3%</td>
<td align="center">12</td>
<td align="center">0.74</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">MobileNetV1</td>
<td align="center">sensitive -50%</td>
<td align="center">69.8% / 88.9%</td>
<td align="center">9</td>
<td align="center">0.56</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">MobileNetV2</td>
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<td align="center">-</td>
<td align="center">72.15%/90.65%</td>
<td align="center">15</td>
<td align="center">0.59</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">MobileNetV2</td>
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<td align="center">uniform -50%</td>
<td align="center">65.79%/86.11%</td>
<td align="center">11</td>
<td align="center">0.296</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">ResNet34</td>
<td align="center">-</td>
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<td align="center">72.15%/90.65%</td>
<td align="center">84</td>
<td align="center">7.36</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">ResNet34</td>
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<td align="center">uniform -50%</td>
<td align="center">70.99%/89.95%</td>
<td align="center">41</td>
<td align="center">3.67</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">ResNet34</td>
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<td align="center">auto -55.05%</td>
<td align="center">70.24%/89.63%</td>
<td align="center">33</td>
<td align="center">3.31</td>
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<h3 id="13">1.3 蒸馏<a class="headerlink" href="#13" title="Permanent link">#</a></h3>
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<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>
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<td align="center">MobileNetV1</td>
<td align="center">-</td>
<td align="center">70.99%/89.68%</td>
<td align="center">17</td>
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<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">下载链接</a></td>
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<td align="center">ResNet50_vd</td>
<td align="center">-</td>
<td align="center">79.12%/94.44%</td>
<td align="center">99</td>
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<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_distilled.tar">下载链接</a></td>
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</tr>
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<td align="center">MobileNetV2</td>
<td align="center">-</td>
<td align="center">72.15%/90.65%</td>
<td align="center">15</td>
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<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_distilled.tar">下载链接</a></td>
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<td align="center">ResNet50</td>
<td align="center">-</td>
<td align="center">76.50%/93.00%</td>
<td align="center">99</td>
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<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar">下载链接</a></td>
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<td align="center">ResNet101</td>
<td align="center">-</td>
<td align="center">77.56%/93.64%</td>
<td align="center">173</td>
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<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar">下载链接</a></td>
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<td align="center">ResNet50</td>
<td align="center">ResNet101 distill</td>
<td align="center">77.29%/93.65%</td>
<td align="center">99</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_distilled.tar">下载链接</a></td>
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</tbody>
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<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>
<h3 id="21">2.1 量化<a class="headerlink" href="#21" title="Permanent link">#</a></h3>
<p>数据集： COCO 2017</p>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<p>数据集：WIDER-FACE</p>
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<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>
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</thead>
<tbody>
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<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>
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<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>
B
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<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>
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<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>
B
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<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>
B
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</tr>
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<tr>
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<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>
B
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<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>
B
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<td align="center">xx</td>
B
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<td align="center"><a href="">下载链接</a></td>
B
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<tr>
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<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>
B
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<td align="center">xx</td>
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<td align="center"><a href="">下载链接</a></td>
B
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</tr>
<tr>
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<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>
B
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<td align="center">xx</td>
B
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<td align="center"><a href="">下载链接</a></td>
B
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</tr>
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<h3 id="22">2.2 剪裁<a class="headerlink" href="#22" title="Permanent link">#</a></h3>
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<p>数据集：Pasacl VOC &amp; COCO 2017</p>
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<th align="center">模型</th>
<th align="center">压缩方法</th>
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<th align="center">数据集</th>
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<th align="center">Image/GPU</th>
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<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>
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<th align="center">GFLOPs (608*608)</th>
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<th align="center">下载</th>
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<td align="center">MobileNet-V1-YOLOv3</td>
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<td align="center">-</td>
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<td align="center">Pascal VOC</td>
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<td align="center">8</td>
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<td align="center">76.2</td>
<td align="center">76.7</td>
<td align="center">75.3</td>
B
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<td align="center">94</td>
<td align="center">40.49</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar">下载链接</a></td>
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<td align="center">MobileNet-V1-YOLOv3</td>
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<td align="center">sensitive -52.88%</td>
<td align="center">Pascal VOC</td>
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<td align="center">8</td>
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<td align="center">77.6</td>
<td align="center">77.7</td>
<td align="center">75.5</td>
<td align="center">31</td>
<td align="center">19.08</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_v1_voc_prune.tar">下载链接</a></td>
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<td align="center">MobileNet-V1-YOLOv3</td>
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<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>
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<td align="center">27.0</td>
<td align="center">95</td>
<td align="center">41.35</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">下载链接</a></td>
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<td align="center">MobileNet-V1-YOLOv3</td>
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<td align="center">sensitive -51.77%</td>
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<td align="center">26.0</td>
<td align="center">25.1</td>
<td align="center">22.6</td>
<td align="center">32</td>
<td align="center">19.94</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_v1_prune.tar">下载链接</a></td>
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<td align="center">-</td>
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<td align="center">8</td>
<td align="center">39.1</td>
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<td align="center">-</td>
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<td align="center">177</td>
<td align="center">89.60</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar">下载链接</a></td>
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<td align="center">39.3</td>
<td align="center">-</td>
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<td align="center">150</td>
<td align="center">81.20</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_prune.tar">下载链接</a></td>
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<td align="center">37.3</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">113</td>
<td align="center">67.48</td>
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<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>
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<td align="center">-</td>
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<td align="center">177</td>
<td align="center">89.60</td>
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<td align="center">40.5</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">150</td>
<td align="center">81.20</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_prune.tar">下载链接</a></td>
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<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
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<td align="center">8</td>
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<td align="center">37.8</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">113</td>
<td align="center">67.48</td>
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_prune578.tar">下载链接</a></td>
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<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>
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<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>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34_voc.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar">下载链接</a></td>
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<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>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar">下载链接</a></td>
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<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>
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<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>
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<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>
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<td align="center">DeepLabv3+/MobileNetv1</td>
<td align="center">quant_post</td>
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<td align="center">xx</td>
<td align="center">xx</td>
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<td align="center"><a href="">下载链接</a></td>
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<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>
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<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>
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<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>
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<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>
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<h3 id="32">3.2 剪裁<a class="headerlink" href="#32" title="Permanent link">#</a></h3>
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<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>
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<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>
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<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>
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