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            <li><a class="toctree-l3" href="#12">1.2 目标检测</a></li>
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                <h2 id="1">1. 量化<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
<h3 id="11">1.1 图象分类<a class="headerlink" href="#11" title="Permanent link">#</a></h3>
<p>数据集：ImageNet1000类</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">Top-1/Top-5</th>
<th align="center">模型大小（MB）</th>
B
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<th align="center">下载</th>
B
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218 219 220 221 222 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1 FP32</td>
B
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<td align="center">70.99%/89.68%</td>
B
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<td align="center">xx</td>
B
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<td align="center"><a href="">下载链接</a></td>
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</tr>
<tr>
<td align="center">MobileNetV1 quant_post</td>
B
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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>
B
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<tr>
<td align="center">MobileNetV1 quant_aware</td>
B
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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>
B
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<tr>
<td align="center">MobileNetV2 FP32</td>
B
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<td align="center">72.15%/90.65%</td>
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<td align="center">xx</td>
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<td align="center"><a href="">下载链接</a></td>
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<tr>
<td align="center">MobileNetV2 quant_post</td>
B
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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>
B
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<tr>
<td align="center">MobileNetV2 quant_aware</td>
B
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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>
B
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<tr>
<td align="center">ResNet50 FP32</td>
B
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<td align="center">76.50%/93.00%</td>
B
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<td align="center">xx</td>
B
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<td align="center"><a href="">下载链接</a></td>
B
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<tr>
<td align="center">ResNet50 quant_post</td>
B
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<td align="center">xx%/xx%</td>
<td align="center">xx</td>
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B
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<tr>
<td align="center">ResNet50 quant_aware</td>
B
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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>
B
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</tr>
</tbody>
</table>
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<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>
<table>
<thead>
<tr>
<th align="center">Model</th>
<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">模型大小(MB)</th>
B
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<th align="center">下载</th>
B
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</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3 FP32</td>
<td align="center">8</td>
B
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<td align="center">29.3</td>
<td align="center">29.3</td>
<td align="center">27.1</td>
B
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<td align="center">xx</td>
B
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<td align="center"><a href="">下载链接</a></td>
B
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</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3 quant_post</td>
B
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<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">MobileNet-V1-YOLOv3 quant_aware</td>
B
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<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>
<td align="center">R50-dcn-YOLOv3 FP32</td>
B
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<td align="center">8</td>
<td align="center">41.4</td>
<td align="center">-</td>
<td align="center">-</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 quant_post</td>
B
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<td align="center">8</td>
<td align="center">xx</td>
<td align="center">-</td>
<td align="center">-</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 quant_aware</td>
B
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<td align="center">8</td>
<td align="center">xx</td>
<td align="center">-</td>
<td align="center">-</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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<p>数据集：WIDER-FACE</p>
B
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<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">Image/GPU</th>
B
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<th align="center">输入尺寸</th>
<th align="center">Easy/Medium/Hard</th>
<th align="center">模型大小（MB）</th>
<th align="center">下载</th>
B
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</tr>
</thead>
<tbody>
<tr>
B
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<td align="center">BlazeFace FP32</td>
B
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<td align="center">8</td>
B
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<td align="center">640</td>
<td align="center">0.915/0.892/0.797</td>
B
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<td align="center">xx</td>
B
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<td align="center"><a href="">下载链接</a></td>
B
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</tr>
<tr>
B
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<td align="center">BlazeFace 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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<td align="center">BlazeFace quant_aware</td>
B
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<td align="center">8</td>
B
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<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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<td align="center">BlazeFace-Lite FP32</td>
<td align="center">8</td>
B
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<td align="center">640</td>
B
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<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>
B
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<td align="center">BlazeFace-Lite quant_post</td>
<td align="center">8</td>
B
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<td align="center">640</td>
B
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<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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400 401 
</tr>
<tr>
B
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402 403 404 405 406 407 
<td align="center">BlazeFace-Lite 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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408 409 
</tr>
<tr>
B
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410 411 412 413 414 415 
<td align="center">BlazeFace-NAS FP32</td>
<td align="center">8</td>
<td align="center">640</td>
<td align="center">0.837/0.807/0.658</td>
<td align="center">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
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416 417 
</tr>
<tr>
B
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418 419 420 421 422 423 
<td align="center">BlazeFace-NAS 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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424 425 
</tr>
<tr>
B
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426 427 428 429 430 431 
<td align="center">BlazeFace-NAS 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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432 433 434 
</tr>
</tbody>
</table>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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435 
<h3 id="_1"><a class="headerlink" href="#_1" title="Permanent link">#</a></h3>
B
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436 437 438 439 440 441 
<h3 id="13">1.3 图像分割<a class="headerlink" href="#13" title="Permanent link">#</a></h3>
<p>数据集：Cityscapes</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
B
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442 443 444 
<th align="center">mIoU</th>
<th align="center">模型大小（MB）</th>
<th align="center">下载</th>
B
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445 446 447 448 449 
</tr>
</thead>
<tbody>
<tr>
<td align="center">DeepLabv3+/MobileNetv1</td>
B
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450 451 452 
<td align="center">63.26</td>
<td align="center">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
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453 454 
</tr>
<tr>
B
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455 456 457 458 
<td align="center">DeepLabv3+/MobileNetv1 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
Deployed 9bcff00 with MkDocs version: 1.0.4  
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459 460 
</tr>
<tr>
B
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461 462 463 464 
<td align="center">DeepLabv3+/MobileNetv1 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
Deployed 9bcff00 with MkDocs version: 1.0.4  
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465 466 
</tr>
<tr>
B
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467 468 469 470 
<td align="center">DeepLabv3+/MobileNetv2</td>
<td align="center">69.81</td>
<td align="center">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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471 472 
</tr>
<tr>
B
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473 474 475 476 477 478 479 480 481 482 
<td align="center">DeepLabv3+/MobileNetv2 quant_post</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">DeepLabv3+/MobileNetv2 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
Deployed 9bcff00 with MkDocs version: 1.0.4  
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483 484 485 
</tr>
</tbody>
</table>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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486 
<h3 id="_2"><a class="headerlink" href="#_2" title="Permanent link">#</a></h3>
B
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487 488 489 490 491 492 493 494 
<h2 id="2">2. 剪枝<a class="headerlink" href="#2" title="Permanent link">#</a></h2>
<h3 id="21">2.1 图像分类<a class="headerlink" href="#21" title="Permanent link">#</a></h3>
<p>数据集：ImageNet1000类</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">Top-1/Top-5</th>
B
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495 496 497 
<th align="center">模型大小（MB）</th>
<th align="center">FLOPs</th>
<th align="center">下载</th>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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498 499 500 501 502 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1</td>
B
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503 504 505 506 
<td align="center">70.99%/89.68%</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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507 508 509 
</tr>
<tr>
<td align="center">MobileNetV1 uniform -50%</td>
B
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510 511 512 513 
<td align="center">xx%/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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514 515 516 
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<tr>
<td align="center">MobileNetV1 sensitive -xx%</td>
B
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517 518 519 520 
<td align="center">xx%/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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521 522 523 
</tr>
<tr>
<td align="center">MobileNetV2</td>
B
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524 525 526 527 
<td align="center">72.15%/90.65%</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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528 529 530 
</tr>
<tr>
<td align="center">MobileNetV2 uniform -50%</td>
B
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531 532 533 534 
<td align="center">xx%/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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535 536 537 
</tr>
<tr>
<td align="center">MobileNetV2 sensitive -xx%</td>
B
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538 539 540 541 
<td align="center">xx%/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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542 543 544 
</tr>
<tr>
<td align="center">ResNet34</td>
B
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545 546 547 548 
<td align="center">74.57%/92.14%</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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549 550 551 
</tr>
<tr>
<td align="center">ResNet34 uniform -50%</td>
B
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552 
<td align="center">xx%/xx%</td>
B
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553 554 
<td align="center">xx</td>
<td align="center">xx</td>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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555 
<td align="center"><a href="">下载链接</a></td>
B
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556 557 
</tr>
<tr>
B
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558 559 
<td align="center">ResNet34 auto -50%</td>
<td align="center">xx%/xx%</td>
B
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560 561 
<td align="center">xx</td>
<td align="center">xx</td>
B
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562 
<td align="center"><a href="">下载链接</a></td>
B
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563 564 565 
</tr>
</tbody>
</table>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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566 
<h3 id="_3"><a class="headerlink" href="#_3" title="Permanent link">#</a></h3>
B
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567 568 569 570 571 572 573 574 
<h3 id="22">2.2 目标检测<a class="headerlink" href="#22" title="Permanent link">#</a></h3>
<p>数据集：Pasacl VOC &amp; COCO 2017</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">数据集</th>
<th align="center">Image/GPU</th>
B
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575 576 577 578 579 580 
<th align="center">输入608 mAP</th>
<th align="center">输入416 mAP</th>
<th align="center">输入320  mAP</th>
<th align="center">模型大小（MB）</th>
<th align="center">FLOPs</th>
<th align="center">下载</th>
B
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581 582 583 584 585 586 587 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
<td align="center">8</td>
B
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588 589 590 591 592 593 
<td align="center">76.2</td>
<td align="center">76.7</td>
<td align="center">75.3</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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594 595 
</tr>
<tr>
B
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596 
<td align="center">MobileNet-V1-YOLOv3 prune xx%</td>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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597 
<td align="center">Pasacl VOC</td>
B
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598 599 600 601 602 603 604 
<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">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
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605 606 607 608 
</tr>
<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
B
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609 610 611 612 613 614 615 
<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">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
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616 617 
</tr>
<tr>
B
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618 
<td align="center">MobileNet-V1-YOLOv3 prune xx%</td>
B
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619 
<td align="center">COCO</td>
B
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620 621 622 623 624 625 626 
<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">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
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627 628 629 630 
</tr>
<tr>
<td align="center">R50-dcn-YOLOv3</td>
<td align="center">COCO</td>
B
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631 632 633 634 
<td align="center">8</td>
<td align="center">41.4</td>
<td align="center">-</td>
<td align="center">-</td>
B
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635 636 
<td align="center">xx</td>
<td align="center">xx</td>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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637 
<td align="center"><a href="">下载链接</a></td>
B
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638 639 
</tr>
<tr>
B
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640 641 642 643 644 645 
<td align="center">R50-dcn-YOLOv3 prune xx%</td>
<td align="center">COCO</td>
<td align="center">8</td>
<td align="center">xx</td>
<td align="center">-</td>
<td align="center">-</td>
B
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646 647 
<td align="center">xx</td>
<td align="center">xx</td>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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648 
<td align="center"><a href="">下载链接</a></td>
B
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649 650 651 652 653 654 655 656 657 
</tr>
</tbody>
</table>
<h3 id="23">2.3 图像分割<a class="headerlink" href="#23" title="Permanent link">#</a></h3>
<p>数据集：Cityscapes</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
B
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658 659 660 661 
<th align="center">mIoU</th>
<th align="center">模型大小（MB）</th>
<th align="center">FLOPs</th>
<th align="center">下载</th>
B
Deployed 9bcff00 with MkDocs version: 1.0.4  
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662 663 664 665 666 
</tr>
</thead>
<tbody>
<tr>
<td align="center">DeepLabv3+/MobileNetv2</td>
B
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667 668 669 670 
<td align="center">69.81</td>
<td align="center">xx</td>
<td align="center">xx</td>
<td align="center"><a href="">下载链接</a></td>
B
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671 672 
</tr>
<tr>
B
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673 674 
<td align="center">DeepLabv3+/MobileNetv2 prune xx%</td>
<td align="center">xx</td>
B
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675 676 
<td align="center">xx</td>
<td align="center">xx</td>
B
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677 
<td align="center"><a href="">下载链接</a></td>
B
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678 679 680 681 682 683 684 685 686 687 688 
</tr>
</tbody>
</table>
<h2 id="3">3. 蒸馏<a class="headerlink" href="#3" title="Permanent link">#</a></h2>
<h3 id="31">3.1 图象分类<a class="headerlink" href="#31" title="Permanent link">#</a></h3>
<p>数据集：ImageNet1000类</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">baseline</th>
B
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689 
<th align="center">下载</th>
B
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690 691 692 693 694 
</tr>
</thead>
<tbody>
<tr>
<td align="center">MobileNetV1</td>
B
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695 696 697 698 699 700 701 
<td align="center">70.99%/89.68%</td>
<td align="center"><a href="http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar">下载链接</a></td>
</tr>
<tr>
<td align="center">MobileNetV1 distilled (teacher: ResNet50_vd<sup><a href="#trans1">1</a></sup>)</td>
<td align="center">72.79%/90.69%</td>
<td align="center"><a href="">下载链接</a></td>
B
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702 703 704 
</tr>
<tr>
<td align="center">MobileNetV2</td>
B
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705 706 707 708 709 710 711 
<td align="center">72.15%/90.65%</td>
<td align="center"><a href="">下载链接</a></td>
</tr>
<tr>
<td align="center">MobileNetV2 distilled (teacher: ResNet50_vd)</td>
<td align="center">74.30%/91.52%</td>
<td align="center"><a href="">下载链接</a></td>
B
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712 713 714 
</tr>
<tr>
<td align="center">ResNet50</td>
B
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715 716 717 718 719 720 721 
<td align="center">76.50%/93.00%</td>
<td align="center"><a href="">下载链接</a></td>
</tr>
<tr>
<td align="center">ResNet50 distilled (teacher: ResNet101<sup><a href="#trans2">2</a></sup>)</td>
<td align="center">77.40%/93.48%</td>
<td align="center"><a href="">下载链接</a></td>
B
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722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 
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</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a name="trans1">  [1]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar">ResNet50_vd</a>预训练模型Top-1/Top-5准确率分别为79.12%/94.44%</p>
<p>带_vd后缀代表开启了Mixup训练，Mixup相关介绍参考<a href="https://arxiv.org/abs/1710.09412">mixup: Beyond Empirical Risk Minimization</a></p>
<p><a name="trans1">[2]</a>：<a href="https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar">ResNet101</a>预训练模型Top-1/Top-5准确率分别为77.56%/93.64%</p>
</div>
<h3 id="32">3.2 目标检测<a class="headerlink" href="#32" title="Permanent link">#</a></h3>
<p>数据集：Pasacl VOC &amp; COCO 2017</p>
<table>
<thead>
<tr>
<th align="center">Model</th>
<th align="center">数据集</th>
<th align="center">Image/GPU</th>
B
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739 740 741 742 
<th align="center">输入640 mAP</th>
<th align="center">输入416 mAP</th>
<th align="center">输入320 mAP</th>
<th align="center">下载链接</th>
B
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743 744 745 746 747 748 749 
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<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">Pasacl VOC</td>
<td align="center">16</td>
B
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750 751 752 753 
<td align="center">76.2</td>
<td align="center">76.7</td>
<td align="center">75.3</td>
<td align="center"><a href="">下载链接</a></td>
B
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754 755 
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<tr>
B
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756 
<td align="center">MobileNet-V1-YOLOv3 distilled (teacher: ResNet34-YOLOv3-VOC<sup><a href="#trans3">3</a></sup>)</td>
B
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757 
<td align="center">Pasacl VOC</td>
B
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758 759 760 761 762 
<td align="center">16</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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763 764 765 766 
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<tr>
<td align="center">MobileNet-V1-YOLOv3</td>
<td align="center">COCO</td>
B
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767 768 769 770 771 
<td align="center">16</td>
<td align="center">29.3</td>
<td align="center">29.3</td>
<td align="center">27.1</td>
<td align="center"><a href="">下载链接</a></td>
B
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772 773 
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<tr>
B
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774 
<td align="center">MobileNet-V1-YOLOv3 distilled (teacher: ResNet34-YOLOv3-COCO<sup><a href="#trans4">4</a></sup>)</td>
B
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775 
<td align="center">COCO</td>
B
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776 777 778 779 780 
<td align="center">16</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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781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a name="trans1">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
<p><a name="trans1">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
</div>
              
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