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@ -41,8 +41,8 @@ src="docs/images/models/mobile_arm_top1.png" width="1000">
模型蒸馏是指使用教师模型(teacher model)去指导学生模型(student model)学习特定任务，保证小模型在参数量不变的情况下，得到比较大的效果提升，甚至获得与大模型相似的精度指标。PaddleCLS提供了一种简单的半监督标签模型蒸馏方案（SSLD，Simple Semi-supervised Label Distillation），使用该方案大幅提升了ResNet50_vd、MobileNetV1和MobileNetV3在ImageNet数据集上分类效果。该蒸馏方案的框架图和蒸馏模型效果如下图所示，详细的蒸馏方法介绍以及使用持续更新中。
<center class="half">
<img
src="docs/images/distillation/ppcls_distillation_v1.png" width="450"> <img
src="docs/images/distillation/distillation_perform.png" width="300">
src="docs/images/distillation/ppcls_distillation_v1.png" width="400"> <img
src="docs/images/distillation/distillation_perform.png" width="250">
</center>
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