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    <li class="toctree-l2"><a href="#1">1. 图象分类</a></li>
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            <li><a class="toctree-l3" href="#12">1.2 剪裁</a></li>
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    <span class="caption-text">教程</span>
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    <a class="" href="../tutorials/quant_aware_demo/">量化训练</a>
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    <a class="" href="../tutorials/quant_embedding_demo/">Embedding量化</a>
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    <a class="" href="../tutorials/nas_demo/">SA搜索</a>
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    <a class="" href="../tutorials/distillation_demo/">知识蒸馏</a>
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    <a class="" href="../api/quantization_api/">量化</a>
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    <a class="" href="../api/prune_api/">剪枝与敏感度</a>
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    <a class="" href="../api/analysis_api/">模型分析</a>
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        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/model_zoo.md"
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          class="icon icon-github"> Edit on GitHub</a>
      
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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>
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<th align="center">下载</th>
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<td align="center">MobileNetV1</td>
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<td align="center">-</td>
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<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">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>
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<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>
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<td align="center">MobileNetV2</td>
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<td align="center">-</td>
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<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">MobileNetV2</td>
<td align="center">quant_post</td>
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<td align="center">xx</td>
<td align="center"><a href="">下载链接</a></td>
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<td align="center">quant_aware</td>
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<td align="center"><a href="">下载链接</a></td>
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<td align="center">ResNet50</td>
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<td align="center">76.50%/93.00%</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>
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<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>
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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>
<td align="center"><a href="">下载链接</a></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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<table>
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<th align="center">模型</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">MobileNetV1</td>
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<td align="center">Baseline</td>
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<td align="center">70.99%/89.68%</td>
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<td align="center">17</td>
<td align="center">1.11</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">MobileNetV1</td>
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<td align="center">uniform -50%</td>
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<td align="center">69.4%/88.66% (-1.59%/-1.02%)</td>
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<td align="center">9</td>
<td align="center">0.56</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar">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>
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<td align="center">70.4%/89.3% (-0.59%/-0.38%)</td>
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<td align="center">12</td>
<td align="center">0.74</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar">下载链接</a></td>
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<td align="center">MobileNetV1</td>
<td align="center">sensitive -50%</td>
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<td align="center">69.8% / 88.9% (-1.19%/-0.78%)</td>
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<td align="center">9</td>
<td align="center">0.56</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar">下载链接</a></td>
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</tr>
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<td align="center">MobileNetV2</td>
B
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<td align="center">-</td>
B
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<td align="center">72.15%/90.65%</td>
<td align="center">15</td>
<td align="center">0.59</td>
B
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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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</tr>
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<td align="center">MobileNetV2</td>
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<td align="center">uniform -50%</td>
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<td align="center">65.79%/86.11% (-6.35%/-4.47%)</td>
B
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<td align="center">11</td>
<td align="center">0.296</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar">下载链接</a></td>
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</tr>
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<tr>
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<td align="center">ResNet34</td>
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<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="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar">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>
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<td align="center">70.99%/89.95% (-1.36%/-0.87%)</td>
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<td align="center">41</td>
<td align="center">3.67</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_uniform-50.tar">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>
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<td align="center">70.24%/89.63% (-2.04%/-1.06%)</td>
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<td align="center">33</td>
<td align="center">3.31</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar">下载链接</a></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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<table>
<thead>
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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>
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<th align="center">下载</th>
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</tr>
</thead>
<tbody>
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<td align="center">MobileNetV1</td>
B
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<td align="center">student</td>
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<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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</tr>
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<td align="center">ResNet50_vd</td>
B
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<td align="center">teacher</td>
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<td align="center">79.12%/94.44%</td>
<td align="center">99</td>
B
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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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</tr>
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<td align="center">MobileNetV1</td>
<td align="center">ResNet50_vd<sup><a href="#trans1">1</a></sup> distill</td>
B
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<td align="center">72.77%/90.68% (+1.78%/+1.00%)</td>
B
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<td align="center">17</td>
B
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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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<tr>
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<td align="center">MobileNetV2</td>
B
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<td align="center">student</td>
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<td align="center">72.15%/90.65%</td>
<td align="center">15</td>
B
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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>
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<td align="center">74.28%/91.53% (+2.13%/+0.88%)</td>
B
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<td align="center">15</td>
B
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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>
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<td align="center">student</td>
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<td align="center">76.50%/93.00%</td>
<td align="center">99</td>
B
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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>
B
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<td align="center">teacher</td>
B
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<td align="center">77.56%/93.64%</td>
<td align="center">173</td>
B
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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>
B
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<tr>
<td align="center">ResNet50</td>
<td align="center">ResNet101 distill</td>
B
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<td align="center">77.29%/93.65% (+0.79%/+0.65%)</td>
B
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<td align="center">99</td>
B
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436 
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_distilled.tar">下载链接</a></td>
B
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</tr>
</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>
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<th align="center">模型体积（MB）</th>
B
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<th align="center">下载</th>
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</tr>
</thead>
<tbody>
<tr>
B
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<td align="center">MobileNet-V1-YOLOv3</td>
B
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<td align="center">-</td>
B
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<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>
B
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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>
B
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</tr>
B
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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>
B
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</tr>
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<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
B
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<td align="center">-</td>
B
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<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>
B
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</tr>
<tr>
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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>
B
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527 528 529 
</tr>
</tbody>
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<p>数据集：WIDER-FACE</p>
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<table>
<thead>
<tr>
B
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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>
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<th align="center">模型体积（MB）</th>
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540 
<th align="center">下载</th>
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541 542 543 544 
</tr>
</thead>
<tbody>
<tr>
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<td align="center">BlazeFace</td>
B
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<td align="center">-</td>
B
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<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>
B
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<tr>
B
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554 555 556 557 558 559 560 
<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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</tr>
<tr>
B
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563 564 565 566 567 568 569 
<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>
B
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570 571 
</tr>
<tr>
B
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572 
<td align="center">BlazeFace-Lite</td>
B
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573 
<td align="center">-</td>
B
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574 575 576 577 578 
<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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579 580 
</tr>
<tr>
B
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581 582 583 584 585 586 587 
<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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588 
</tr>
B
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589 
<tr>
B
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590 591 592 593 594 595 596 
<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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597 598 
</tr>
<tr>
B
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599 
<td align="center">BlazeFace-NAS</td>
B
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600 
<td align="center">-</td>
B
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601 602 603 
<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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604 
<td align="center">xx</td>
B
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605 
<td align="center"><a href="">下载链接</a></td>
B
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606 607 
</tr>
<tr>
B
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608 609 610 611 612 
<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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613 
<td align="center">xx</td>
B
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614 
<td align="center"><a href="">下载链接</a></td>
B
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615 616 
</tr>
<tr>
B
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617 618 619 620 621 
<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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622 
<td align="center">xx</td>
B
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623 
<td align="center"><a href="">下载链接</a></td>
B
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624 625 626 
</tr>
</tbody>
</table>
B
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627 
<h3 id="22">2.2 剪裁<a class="headerlink" href="#22" title="Permanent link">#</a></h3>
B
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628 
<p>数据集：Pasacl VOC &amp; COCO 2017</p>
B
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629 630 631 
<table>
<thead>
<tr>
B
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<th align="center">模型</th>
<th align="center">压缩方法</th>
B
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<th align="center">数据集</th>
B
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635 
<th align="center">Image/GPU</th>
B
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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>
B
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<th align="center">模型体积(MB)</th>
B
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640 
<th align="center">GFLOPs (608*608)</th>
B
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641 
<th align="center">下载</th>
B
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642 643 644 645 646 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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647 
<td align="center">Baseline</td>
B
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648 
<td align="center">Pascal VOC</td>
B
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<td align="center">8</td>
B
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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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653 654 655 
<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>
B
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656 657 658 
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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659 660 
<td align="center">sensitive -52.88%</td>
<td align="center">Pascal VOC</td>
B
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661 
<td align="center">8</td>
B
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662 663 664 
<td align="center">77.6 (+1.4)</td>
<td align="center">77.7 (1.0)</td>
<td align="center">75.5 (+0.2)</td>
B
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665 666 667 
<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>
B
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668 669 670 
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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671 
<td align="center">-</td>
B
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672 673 674 675 
<td align="center">COCO</td>
<td align="center">8</td>
<td align="center">29.3</td>
<td align="center">29.3</td>
B
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676 677 678 679 
<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>
B
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680 681 682 
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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683 
<td align="center">sensitive -51.77%</td>
B
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684 
<td align="center">COCO</td>
B
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685 
<td align="center">8</td>
B
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686 687 688 
<td align="center">26.0 (-3.3)</td>
<td align="center">25.1 (-4.2)</td>
<td align="center">22.6 (-4.4)</td>
B
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689 690 691 
<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>
B
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692 693 
</tr>
<tr>
B
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694 
<td align="center">R50-dcn-YOLOv3</td>
B
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695 
<td align="center">-</td>
B
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696 
<td align="center">COCO</td>
B
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697 698 
<td align="center">8</td>
<td align="center">39.1</td>
B
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699 700 701 702 703 
<td align="center">-</td>
<td align="center">-</td>
<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>
B
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704 705 
</tr>
<tr>
B
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706 
<td align="center">R50-dcn-YOLOv3</td>
B
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707 
<td align="center">sensitive -9.37%</td>
B
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708 
<td align="center">COCO</td>
B
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709 
<td align="center">8</td>
B
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710 
<td align="center">39.3 (+0.2)</td>
B
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711 712 713 714 715 
<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_prune.tar">下载链接</a></td>
B
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716 717 718 
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
B
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719 
<td align="center">sensitive -24.68%</td>
B
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720 
<td align="center">COCO</td>
B
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721 
<td align="center">8</td>
B
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722 
<td align="center">37.3 (-1.8)</td>
B
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723 724 725 726 727 
<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_prune578.tar">下载链接</a></td>
B
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728 729 
</tr>
<tr>
B
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730 
<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
B
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731 
<td align="center">-</td>
B
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732 733 734 
<td align="center">COCO</td>
<td align="center">8</td>
<td align="center">41.4</td>
B
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735 736 737 738 739 
<td align="center">-</td>
<td align="center">-</td>
<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_obj365_pretrained_coco.tar">下载链接</a></td>
B
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740 741 742 
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
B
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743 
<td align="center">sensitive -9.37%</td>
B
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744 
<td align="center">COCO</td>
B
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745 
<td align="center">8</td>
B
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746 
<td align="center">40.5 (-0.9)</td>
B
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747 748 749 750 751 
<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>
B
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752 753 
</tr>
<tr>
B
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754 
<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
B
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755 
<td align="center">sensitive -24.68%</td>
B
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756 
<td align="center">COCO</td>
B
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757 
<td align="center">8</td>
B
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758 
<td align="center">37.8 (-3.3)</td>
B
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759 760 761 762 763 
<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>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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764 765 766 
</tr>
</tbody>
</table>
B
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767 768 
<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>
B
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769 770 771 
<table>
<thead>
<tr>
B
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772 773 774 775 776 777 778 
<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>
B
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779 
<th align="center">模型体积（MB）</th>
B
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780 
<th align="center">下载</th>
B
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781 782 783 784 
</tr>
</thead>
<tbody>
<tr>
B
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785 
<td align="center">MobileNet-V1-YOLOv3</td>
B
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786 
<td align="center">-</td>
B
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787 788 789 790 791 792 
<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>
B
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793 
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar">下载链接</a></td>
B
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794 795 
</tr>
<tr>
B
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796 
<td align="center">ResNet34-YOLOv3</td>
B
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797 
<td align="center">-</td>
B
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798 799 800 801 802 803 
<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>
B
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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>
B
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</tr>
<tr>
B
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807 808 809 810 
<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>
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<td align="center">79.0 (+2.8)</td>
<td align="center">78.2 (+1.5)</td>
<td align="center">75.5 (+0.2)</td>
B
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<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>
B
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<tr>
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<td align="center">MobileNet-V1-YOLOv3</td>
B
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<td align="center">-</td>
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<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>
B
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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>
B
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</tr>
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<td align="center">ResNet34-YOLOv3</td>
B
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<td align="center">-</td>
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<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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<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>
B
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<td align="center">31.4 (+2.1)</td>
<td align="center">30.0 (+0.7)</td>
<td align="center">27.1 (+0.1)</td>
B
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<td align="center">95</td>
B
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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>
B
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</tr>
</tbody>
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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">压缩方法</th>
<th align="center">mIoU</th>
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<th align="center">模型体积（MB）</th>
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<th align="center">下载</th>
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</tr>
</thead>
<tbody>
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B
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<td align="center">DeepLabv3+/MobileNetv1</td>
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<td align="center">-</td>
B
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<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>
B
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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>
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<td align="center">-</td>
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<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>
B
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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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<table>
<thead>
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<th align="center">模型</th>
<th align="center">压缩方法</th>
<th align="center">mIoU</th>
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<th align="center">GFLOPs</th>
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<th align="center">下载</th>
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</thead>
<tbody>
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<td align="center">fast-scnn</td>
<td align="center">baseline</td>
<td align="center">69.64</td>
<td align="center">11</td>
<td align="center">14.41</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape.tar">下载链接</a></td>
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B
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<td align="center">fast-scnn</td>
<td align="center">uniform  -17.07%</td>
<td align="center">69.58 (-0.06)</td>
<td align="center">8.5</td>
<td align="center">11.95</td>
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape_uniform-17.tar">下载链接</a></td>
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<td align="center">fast-scnn</td>
<td align="center">sensitive -47.60%</td>
<td align="center">66.68 (-2.96)</td>
<td align="center">5.7</td>
<td align="center">7.55</td>
B
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/PaddleSlim/fast_scnn_cityscape_sensitive-47.tar">下载链接</a></td>
B
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</tr>
B
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</tbody>
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