【推荐理由】本文介绍了FastViT，这是一种混合视觉变换器架构，可以实现最先进的延迟-准确性权衡。
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
Pavan Kumar, Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan
【论文链接】https://arxiv.org/pdf/2303.14189.pdf
【摘要】最近将Transformer和卷积设计融合在一起，导致模型的准确性和效率稳步提高。在本文中，作者介绍了FastViT，一种混合视觉Transformer体系结构，可以获得最先进的延迟-准确度权衡。为此，作者引入了一种新的令牌混合运算符RepMixer，它是FastViT的构建块，使用结构重参数化来降低网络中跳过连接的内存访问成本。作者进一步应用训练时过参数化和大内核卷积来提高准确性，并经验证明这些选择对延迟的影响极小。展示了本文的模型比最近的最先进的混合Transformer体系结构CMT快3.5倍，比EfficientNet快4.9倍，比ConvNeXt快1.9倍，在ImageNet数据集上获得相同的准确性。在类似的延迟下，该模型在ImageNet上获得比MobileOne更好的Top-1准确度。该模型在几个任务上始终优于竞争体系结构 – 图像分类，检测，分割和3D网格回归，在移动设备和桌面GPU上具有显着的延迟改进。此外，该模型对于超出分布和损坏的样本非常稳健，优于竞争的稳健模型。
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