2021年6月9日 · To effectively combine the strengths from both architectures, we present CoAtNets (pronounced "coat" nets), a family of hybrid models built from two key insights: (1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way...

2021 年 9 月 15 日，一种新的架构在 ImageNet 竞赛中的实现了最先进的性能 (SOTA)。 CoAtNet（发音为“coat”net）在庞大的 JFT-3B 数据集上实现了 90.88% 的 top-1 准确率。 CoAtNet 在使用（相对）较差的数据集 ImageNet-21K（13M 图像）进行预训练时，达到了 88.56% 的 top-1 准确率。 CoAtNet 中的“coat”来源于卷积和自注意力。 我一直在努力弄清楚这件“外套”可能象征着什么，但还没有找到解决办法。 不过这是一个非常合适的首字母缩略词。

为了有效地结合二者的长处，作者提出了 CoAtNets，它的构建主要基于两个关键想法： （1）我们可以通过简单的 relative attention（相对注意力）将 depthwise Convolution（深度卷积）和 self-Attention（自注意力）自然统一；（2）在提升泛化性、能力和效率方面，按照一定的原则，垂直摆放卷积层和注意力层会非常有效。 实验证明，CoAtNets 在多个数据集上，根据不同的资源要求，可以取得 SOTA 的效果。 例如，CoAtNet 在 ImageNet 上取得了 86.0 % top-1 准确率，无 …

2024年7月3日 · 为了有效地结合两种架构的优势，我们提出了 CoAtNets（发音为“coat”nets），这是一个基于两个关键见解构建的混合模型系列： (1)depthwise Convolution和self-Attention可以通过简单的相对注意力自然地统一起来； (2) 以有...

To effectively combine the strengths from both architectures, we present CoAtNets (pronounced "coat" nets), a family of hybrid models built from two key insights: (1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled ...

This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021. 👉 Check out MobileViT if you are interested in other Convolution + Transformer models. net = coatnet_0 () out = net (img) Try out other block combinations mentioned in the paper: out = net (img)

Based on these insights, we propose a simple yet effective network architecture named CoAtNet, which enjoys the strengths from both ConvNets and Transformers. Our CoAtNet achieves SOTA performances under comparable resource constraints across different data sizes.

比如， 无需额外数据，CoAtNet在ImageNet上取得了86%的top1精度；额外引入JFT预训练后，模型进一步提升提升到89.77%，超越了之前最佳的EfficientNetV2与NFNet。 Transformer在计算机视觉领域受到 了越多莱多的关注，但他们的性能仍落后于优秀的CNN。 在这篇文章中，我们将表明： 尽管Transformer具有非常大的模型容量，但由于缺乏正确的归纳偏置导致其泛化性能不如CNN。 为有效取两者之长，我们提出了CoAtNet，它基于以下两个关键点而构建的混合模 …

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This repository contains the official code and pretrained models for CoaT: Co-Scale Conv-Attentional Image Transformers. It introduces (1) a co-scale mechanism to realize fine-to-coarse, coarse-to-fine and cross-scale attention modeling and (2) an efficient conv-attention module to realize relative position encoding in the factorized attention.