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                <h2 id="_1"><a href="../demo/distillation/distillation_demo.py">蒸馏</a><a class="headerlink" href="#_1" title="Permanent link">#</a></h2>
<p>蒸馏demo默认使用ResNet50作为teacher网络，MobileNet作为student网络，此外还支持将teacher和student换成<a href="../demo/models">models目录</a>支持的任意模型。</p>
<p>demo中对teahcer模型和student模型的一层特征图添加了l2_loss的蒸馏损失函数，使用时也可根据需要选择fsp_loss, soft_label_loss以及自定义的loss函数。</p>
<p>训练默认使用的是cifar10数据集，piecewise_decay学习率衰减策略，momentum优化器进行120轮蒸馏训练。使用者也可以简单地用args参数切换为使用ImageNet数据集，cosine_decay学习率衰减策略等其他训练配置。</p>
<h2 id="_2">量化<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
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