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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">模型体积（MB）</th>
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<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">MobileNetV1</td>
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<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>
<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">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>
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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>
<td align="center"><a href="">下载链接</a></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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<th align="center">模型体积（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">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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<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="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>
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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>
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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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<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="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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</tr>
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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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</tr>
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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>
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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>
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<td align="center">MobileNetV1</td>
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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>
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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>
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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>
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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>
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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>
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<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>
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<td align="center">74.28%/91.53%(+2.13%/+0.88%)</td>
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<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>
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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>
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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>
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<td align="center">teacher</td>
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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>
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<tr>
<td align="center">ResNet50</td>
<td align="center">ResNet101 distill</td>
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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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<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>
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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>
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<tbody>
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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>
B
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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>
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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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<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>
B
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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>
B
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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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<tr>
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580 581 582 583 584 585 586 
<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>
B
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<tr>
B
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589 590 591 592 593 594 595 
<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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<tr>
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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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603 
<td align="center">xx</td>
B
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604 
<td align="center"><a href="">下载链接</a></td>
B
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</tr>
<tr>
B
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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>
B
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613 
<td align="center"><a href="">下载链接</a></td>
B
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614 615 
</tr>
<tr>
B
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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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622 
<td align="center"><a href="">下载链接</a></td>
B
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</tr>
</tbody>
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B
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<h3 id="22">2.2 剪裁<a class="headerlink" href="#22" title="Permanent link">#</a></h3>
B
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627 
<p>数据集：Pasacl VOC &amp; COCO 2017</p>
B
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628 629 630 
<table>
<thead>
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B
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<th align="center">模型</th>
<th align="center">压缩方法</th>
B
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633 
<th align="center">数据集</th>
B
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<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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<th align="center">GFLOPs (608*608)</th>
B
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640 
<th align="center">下载</th>
B
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641 642 643 644 645 
</tr>
</thead>
<tbody>
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<td align="center">MobileNet-V1-YOLOv3</td>
B
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<td align="center">Baseline</td>
B
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<td align="center">Pascal VOC</td>
B
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648 
<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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<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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<td align="center">MobileNet-V1-YOLOv3</td>
B
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<td align="center">sensitive -52.88%</td>
<td align="center">Pascal VOC</td>
B
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660 
<td align="center">8</td>
B
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661 662 663 
<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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<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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667 668 669 
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<td align="center">MobileNet-V1-YOLOv3</td>
B
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670 671 672 673 674 
<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>
B
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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>
B
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<td align="center">MobileNet-V1-YOLOv3</td>
B
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682 
<td align="center">sensitive -51.77%</td>
B
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683 
<td align="center">COCO</td>
B
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<td align="center">8</td>
B
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685 686 687 
<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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<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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</tr>
<tr>
B
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<td align="center">R50-dcn-YOLOv3</td>
<td align="center">-</td>
B
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<td align="center">COCO</td>
B
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<td align="center">8</td>
<td align="center">39.1</td>
B
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698 699 700 701 702 
<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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</tr>
<tr>
B
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<td align="center">R50-dcn-YOLOv3</td>
B
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706 
<td align="center">sensitive -9.37%</td>
B
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707 
<td align="center">COCO</td>
B
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<td align="center">8</td>
B
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<td align="center">39.3 (+0.2)</td>
B
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710 711 712 713 714 
<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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<td align="center">R50-dcn-YOLOv3</td>
B
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718 
<td align="center">sensitive -24.68%</td>
B
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719 
<td align="center">COCO</td>
B
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720 
<td align="center">8</td>
B
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721 
<td align="center">37.3 (-1.8)</td>
B
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722 723 724 725 726 
<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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</tr>
<tr>
B
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729 730 731 732 733 
<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>
B
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734 735 736 737 738 
<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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739 740 741 
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<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
B
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742 
<td align="center">sensitive -9.37%</td>
B
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743 
<td align="center">COCO</td>
B
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744 
<td align="center">8</td>
B
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<td align="center">40.5 (-0.9)</td>
B
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746 747 748 749 750 
<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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751 752 
</tr>
<tr>
B
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<td align="center">R50-dcn-YOLOv3 obj365_pretrain</td>
B
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754 
<td align="center">sensitive -24.68%</td>
B
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755 
<td align="center">COCO</td>
B
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756 
<td align="center">8</td>
B
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757 
<td align="center">37.8 (-3.3)</td>
B
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758 759 760 761 762 
<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
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763 764 765 
</tr>
</tbody>
</table>
B
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766 767 
<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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768 769 770 
<table>
<thead>
<tr>
B
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771 772 773 774 775 776 777 
<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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778 
<th align="center">模型体积（MB）</th>
B
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779 
<th align="center">下载</th>
B
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780 781 782 783 
</tr>
</thead>
<tbody>
<tr>
B
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784 785 786 787 788 789 790 791 
<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>
B
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792 
<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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793 794 
</tr>
<tr>
B
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795 796 797 798 799 800 801 802 
<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>
B
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803 
<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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804 805 
</tr>
<tr>
B
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806 807 808 809 
<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>
B
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810 811 812 
<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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813 
<td align="center">94</td>
B
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814 
<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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815 
</tr>
B
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816 
<tr>
B
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817 818 819 820 821 822 823 824 
<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>
B
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825 
<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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826 827 
</tr>
<tr>
B
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828 829 830 831 832 833 834 835 
<td align="center">ResNet34-YOLOv3</td>
<td align="center">-</td>
<td align="center">COCO</td>
<td align="center">8</td>
<td align="center">36.2</td>
<td align="center">34.3</td>
<td align="center">31.4</td>
<td align="center">163</td>
B
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836 
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar">下载链接</a></td>
B
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837 838 839 840 841 842 
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">ResNet34-YOLOv3 distill</td>
<td align="center">COCO</td>
<td align="center">8</td>
B
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843 844 845 
<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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846 
<td align="center">95</td>
B
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847 
<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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848 849 850 
</tr>
</tbody>
</table>
B
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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>
B
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854 855 856 
<table>
<thead>
<tr>
B
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857 858 859 
<th align="center">模型</th>
<th align="center">压缩方法</th>
<th align="center">mIoU</th>
B
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860 
<th align="center">模型体积（MB）</th>
B
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861 
<th align="center">下载</th>
B
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862 863 864 865 
</tr>
</thead>
<tbody>
<tr>
B
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866 867 868 869 870 871 872 873 874 
<td align="center">DeepLabv3+/MobileNetv1</td>
<td align="center">-</td>
<td align="center">63.26</td>
<td align="center">xx</td>
<td align="center"><a href="">下载链接</a></td>
</tr>
<tr>
<td align="center">DeepLabv3+/MobileNetv1</td>
<td align="center">quant_post</td>
B
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875 876 
<td align="center">xx</td>
<td align="center">xx</td>
B
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877 
<td align="center"><a href="">下载链接</a></td>
B
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878 
</tr>
B
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879 
<tr>
B
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880 881 882 883 884 
<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>
B
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</tr>
<tr>
B
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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>
B
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</tr>
<tr>
B
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894 895 896 897 898 
<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>
B
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</tr>
<tr>
B
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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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</tr>
</tbody>
</table>
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>
<tr>
B
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<th align="center">模型</th>
<th align="center">压缩方法</th>
<th align="center">mIoU</th>
B
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<th align="center">模型体积（MB）</th>
B
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<th align="center">GFLOPs</th>
B
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<th align="center">下载</th>
B
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</tr>
</thead>
<tbody>
<tr>
B
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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>
B
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<td align="center"><a href="">下载链接</a></td>
B
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929 930 
</tr>
<tr>
B
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931 932 933 934 935 936 937 938 939 940 941 942 943 
<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>
<td align="center"><a href="">下载链接</a></td>
</tr>
<tr>
<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="">下载链接</a></td>
B
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</tr>
B
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946 947 948 949 950 951 952 953 954 
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