《Computer vision》笔记 shufflenet(10)
作者:石文华 编辑:陈人和 前 言 卷积神经网络在计算机视觉任务中表现优异,但由于嵌入式设备内存空间小,能耗要求低,因此需要使用更加高效的模型。例如,与单纯的堆叠卷积层相比GoogLeNet增加了网络的宽度但降低了复杂度,SqueezeNet在保持精度的同时大大减少参数和计算量,ResNet利用高效的bottleneck结构实现惊人的效果,Xception中提出深度可分卷积概括了Inception序列,MobileNet利用深度可分卷积构建的轻量级模型获得了不错的效果,Senet(取得了 ImageNet 2017 的冠军)引入了一个
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