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                <h2 id="1">1. 量化<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
<h3 id="11">1.1 图象分类<a class="headerlink" href="#11" title="Permanent link">#</a></h3>
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<p>数据集：ImageNet1000类</p>
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<p>评价指标：Top-1/Top-5准确率</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
B
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<td align="center">MobileNetV1 FP32</td>
B
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<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
B
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<td align="center"><a href="">xx%/xx%</a></td>
<td align="center"><a href="">xx%/xx%</a></td>
B
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</tr>
<tr>
B
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<td align="center">MobileNetV2 FP32</td>
B
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<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
B
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<td align="center"></td>
<td align="center"></td>
B
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</tr>
<tr>
B
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<td align="center">ResNet50 FP32</td>
B
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<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar">76.50%/93.00%</a></td>
B
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<td align="center"></td>
<td align="center"></td>
B
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</tr>
</tbody>
</table>
<p>量化训练前后，模型大小的变化对比如下：</p>
<table>
<thead>
<tr>
<th align="left">Model</th>
<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">MobileNetV1</td>
<td align="center">17M</td>
B
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<td align="center"><a href="">xx%/xx%</a></td>
<td align="center"><a href="">xx%/xx%</a></td>
B
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</tr>
<tr>
<td align="left">MobileNetV2</td>
B
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<td align="center">xxM</td>
B
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<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="left">ResNet50</td>
<td align="center">99M</td>
<td align="center"></td>
<td align="center"></td>
</tr>
</tbody>
</table>
<h3 id="12">1.2 目标检测<a class="headerlink" href="#12" title="Permanent link">#</a></h3>
B
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<p>数据集：COCO 2017 </p>
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<table>
<thead>
<tr>
<th align="center">Model</th>
B
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<th align="center">输入尺寸</th>
<th align="center">Image/GPU</th>
<th align="center">FP32 BoxAP</th>
<th align="center">离线量化 BoxAP</th>
<th align="center">量化训练 BoxAP</th>
B
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</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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<td align="center">608</td>
<td align="center">8</td>
B
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
B
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<td align="center"><a href="">xx</a></td>
<td align="center"><a href="">xx</a></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
B
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</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
B
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<td align="center">608</td>
<td align="center"></td>
B
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar">41.4</a></td>
B
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<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
B
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</tr>
</tbody>
</table>
B
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<p>数据集：WIDER-FACE</p>
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<p>评价指标：Easy/Medium/Hard mAP</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
B
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<th align="center">输入尺寸</th>
<th align="center">Image/GPU</th>
B
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<th align="center">FP32</th>
<th align="center">离线量化</th>
<th align="center">量化训练</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">BlazeFace</td>
B
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<td align="center">640</td>
<td align="center">8</td>
B
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar">0.915/0.892/0.797</a></td>
B
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<td align="center"><a href="">xx/xx/xx</a></td>
<td align="center"><a href="">xx/xx/xx</a></td>
B
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</tr>
<tr>
<td align="center">BlazeFace-Lite</td>
B
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<td align="center">640</td>
<td align="center"></td>
B
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar">0.909/0.885/0.781</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">BlazeFace-NAS</td>
B
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<td align="center">640</td>
<td align="center"></td>
B
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<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar">0.837/0.807/0.658</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
</tbody>
</table>
<h3 id="13">1.3 图像分割<a class="headerlink" href="#13" title="Permanent link">#</a></h3>
B
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<p>数据集：Cityscapes</p>
B
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<table>
<thead>
<tr>
<th align="center">Model</th>
B
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378 379 380 
<th align="center">FP32 mIoU</th>
<th align="center">离线量化 mIoU</th>
<th align="center">量化训练 mIoU</th>
B
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</tr>
</thead>
<tbody>
<tr>
<td align="center">DeepLabv3+/MobileNetv1</td>
<td align="center"><a href="">63.26</a></td>
B
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<td align="center"><a href="">xx</a></td>
<td align="center"><a href="">xx</a></td>
B
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</tr>
<tr>
<td align="center">DeepLabv3+/MobileNetv2</td>
<td align="center"><a href="https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz">69.81</a></td>
<td align="center"></td>
<td align="center"></td>
</tr>
</tbody>
</table>
<h2 id="2">2. 剪枝<a class="headerlink" href="#2" title="Permanent link">#</a></h2>
<h3 id="21">2.1 图像分类<a class="headerlink" href="#21" title="Permanent link">#</a></h3>
B
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<p>数据集：ImageNet1000类</p>
B
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<table>
<thead>
<tr>
<th align="center">Model</th>
B
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<th align="center">Top-1/Top-5</th>
B
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</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
B
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<tr>
<td align="center">MobileNetV1 uniform -50%</td>
<td align="center"></td>
</tr>
<tr>
<td align="center">MobileNetV1 sensitive -xx%</td>
<td align="center"></td>
B
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<td align="center">MobileNetV2</td>
<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
B
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<tr>
<td align="center">MobileNetV2 uniform -50%</td>
<td align="center"></td>
</tr>
<tr>
<td align="center">MobileNetV2 sensitive -xx%</td>
<td align="center"></td>
B
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<tr>
<td align="center">ResNet34</td>
<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar">74.57%/92.14%</a></td>
B
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436 437 438 439 440 441 442 443 
</tr>
<tr>
<td align="center">ResNet34 uniform -50%</td>
<td align="center"></td>
</tr>
<tr>
<td align="center">ResNet34 auto -50%</td>
<td align="center"></td>
B
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444 445 446 447 
</tr>
</tbody>
</table>
<h3 id="22">2.2 目标检测<a class="headerlink" href="#22" title="Permanent link">#</a></h3>
B
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448 
<p>数据集：Pasacl VOC &amp; COCO 2017</p>
B
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449 450 451 452 453 
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">数据集</th>
B
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454 455 456 457 
<th align="center">输入尺寸</th>
<th align="center">Image/GPU</th>
<th align="center">baseline mAP</th>
<th align="center">敏感度剪枝 mAP</th>
B
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458 459 460 461 462 463 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
B
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464 465 
<td align="center">608</td>
<td align="center">8</td>
B
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466 
<td align="center"><a href="">76.2</a></td>
B
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467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 
<td align="center"><a href="">77.59</a> (-50%)</td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"><a href="">76.7</a></td>
<td align="center"><a href="">xx</a></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"><a href="">75.2</a></td>
<td align="center"><a href="">xx</a></td>
B
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484 485 486 487 
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
B
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488 489 
<td align="center">608</td>
<td align="center"></td>
B
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490 
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
B
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491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 
<td align="center"><a href="">29.56</a> (-20%)</td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"><a href="">29.3</a></td>
<td align="center"><a href="">xx</a></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"><a href="">27.1</a></td>
<td align="center"><a href="">xx</a></td>
B
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508 509 510 511 
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">COCO</td>
B
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512 513 
<td align="center">608</td>
<td align="center"></td>
B
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514 
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar">41.4</a></td>
B
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515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 
<td align="center"><a href="">37.8</a> (-30%)</td>
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">COCO</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">COCO</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
B
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532 533 534 535 
</tr>
</tbody>
</table>
<h3 id="23">2.3 图像分割<a class="headerlink" href="#23" title="Permanent link">#</a></h3>
B
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536 
<p>数据集：Cityscapes</p>
B
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537 538 539 540 
<table>
<thead>
<tr>
<th align="center">Model</th>
B
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541 542 
<th align="center">Baseline mIoU</th>
<th align="center">xx剪枝 mIoU</th>
B
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543 544 545 546 547 548 
</tr>
</thead>
<tbody>
<tr>
<td align="center">DeepLabv3+/MobileNetv2</td>
<td align="center"><a href="https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz">69.81</a></td>
B
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549 
<td align="center"><a href="">xx</a></td>
B
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550 551 552 553 554 
</tr>
</tbody>
</table>
<h2 id="3">3. 蒸馏<a class="headerlink" href="#3" title="Permanent link">#</a></h2>
<h3 id="31">3.1 图象分类<a class="headerlink" href="#31" title="Permanent link">#</a></h3>
B
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555 
<p>数据集：ImageNet1000类</p>
B
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556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 
<p>评价指标：Top-1/Top-5准确率</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">baseline</th>
<th align="center">蒸馏后</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">70.99%/89.68%</a></td>
<td align="center"><a href="">72.79%/90.69%</a> (teacher: ResNet50_vd<sup><a href="#trans1">1</a></sup>)</td>
</tr>
<tr>
<td align="center">MobileNetV2</td>
<td align="center"><a href="https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar">72.15%/90.65%</a></td>
<td align="center"><a href="">74.30%/91.52%</a> (teacher: ResNet50_vd)</td>
</tr>
<tr>
<td align="center">ResNet50</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar">76.50%/93.00%</a></td>
<td align="center"><a href="">77.40%/93.48%</a> (teacher: ResNet101<sup><a href="#trans2">2</a></sup>)</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
B
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585 586 
<p><a name="trans1">  [1]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar">ResNet50_vd</a>预训练模型Top-1/Top-5准确率分别为79.12%/94.44%</p>
<p>带_vd后缀代表开启了Mixup训练，Mixup相关介绍参考<a href="https://arxiv.org/abs/1710.09412">mixup: Beyond Empirical Risk Minimization</a></p>
B
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587 588 589 
<p><a name="trans1">[2]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar">ResNet101</a>预训练模型Top-1/Top-5准确率分别为77.56%/93.64%</p>
</div>
<h3 id="32">3.2 目标检测<a class="headerlink" href="#32" title="Permanent link">#</a></h3>
B
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590 
<p>数据集：Pasacl VOC &amp; COCO 2017</p>
B
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591 592 593 594 595 
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">数据集</th>
B
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596 597 
<th align="center">输入尺寸</th>
<th align="center">Image/GPU</th>
B
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598 
<th align="center">baseline</th>
B
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599 
<th align="center">蒸馏后 mAP</th>
B
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600 601 602 603 604 605 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
B
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606 607 
<td align="center">640</td>
<td align="center">16</td>
B
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608 609 610 611 612 
<td align="center"><a href="">76.2</a></td>
<td align="center"><a href="">79.0</a> (teacher: ResNet34-YOLOv3-VOC<sup><a href="#trans3">3</a></sup>)</td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 
<td align="center">Pasacl VOC</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"><a href="">76.7</a></td>
<td align="center"><a href="">78.2</a></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"><a href="">75.2</a></td>
<td align="center"><a href="">75.5</a></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
B
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629 
<td align="center">COCO</td>
B
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630 631 
<td align="center">640</td>
<td align="center"></td>
B
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632 633 634 
<td align="center"><a href="https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar">29.3</a></td>
<td align="center"><a href="">31.0</a> (teacher: ResNet34-YOLOv3-COCO<sup><a href="#trans4">4</a></sup>)</td>
</tr>
B
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635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
<td align="center">416</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
<td align="center">320</td>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
B
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651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a name="trans1">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
<p><a name="trans1">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
</div>
              
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