详细介绍《EfficientNetV2: Smaller Models and Faster Training》
时间: 2023-02-11 09:12:38 浏览: 82
《EfficientNetV2: Smaller Models and Faster Training》是由 Google Research 在 2021 年发表的一篇论文,主要介绍了 EfficientNetV2 模型的设计思想和实验结果。
EfficientNetV2 是在原有 EfficientNet 模型的基础上进行改进的版本,主要通过在模型结构、权重初始化和训练方式上的优化来提高模型的性能。
在模型结构上,EfficientNetV2 通过在原有的 EfficientNet 模型上增加了更多的深度和宽度,并且使用了更高效的卷积层(如 MixConv 和 CondConv),这样可以在不增加模型参数量的情况下提高模型的性能。
在权重初始化上,EfficientNetV2 使用了一种新的初始化方法,称为 "Weight Standardization",可以使模型在训练过程中更快地收敛。
在训练方式上,EfficientNetV2 通过使用 "Stochastic Weight Averaging"(SWA)和 "Ranger" 优化器来加速模型的训练过程。
实验结果表明,EfficientNetV2 在 ImageNet 数据集上取得了非常优秀的结果,在模型参数量和计算复杂度相同的情况下,EfficientNetV2 的精度要高于其他现有的模型,并且在训练速度上也有了很大的提升。
相关问题
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