# PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture

**Authors:** Kai Han, Jianyuan Guo, Yehui Tang, Yunhe Wang  
**arXiv:** [2201.00978](https://arxiv.org/abs/2201.00978)  
**Date:** 2022-01-04  
**Summary page:** https://www.summarizepaper.com/fr/paper/2201.00978v1/

## Abstract

Transformer networks have achieved great progress for computer vision tasks. Transformer-in-Transformer (TNT) architecture utilizes inner transformer and outer transformer to extract both local and global representations. In this work, we present new TNT baselines by introducing two advanced designs: 1) pyramid architecture, and 2) convolutional stem. The new "PyramidTNT" significantly improves the original TNT by establishing hierarchical representations. PyramidTNT achieves better performances than the previous state-of-the-art vision transformers such as Swin Transformer. We hope this new baseline will be helpful to the further research and application of vision transformer. Code will be available at https://github.com/huawei-noah/CV-Backbones/tree/master/tnt_pytorch.