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) …

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。 …

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 …

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 …

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 …

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 …

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 …

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 …

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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 …