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 <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>
+<p><a name="trans2">[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>
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 <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>
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 <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>
+<p><a name="trans3">[3]</a>：<a href="">ResNet34-YOLOv3-VOC</a>预训练模型的Box AP为82.6</p>
+<p><a name="trans4">[4]</a>：<a href="">ResNet34-YOLOv3-COCO</a>预训练模型的Box AP为36.2</p>
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