The past year has witnessed the rapid development of applying the Transformer
module to vision problems. While some researchers have demonstrated that
Transformer-based models enjoy a favorable ability of fitting data, there are
still growing number of evidences showing that these models suffer over-fitting
especially when the training data is limited. This paper offers an empirical
study by performing step-by-step operations to gradually transit a
Transformer-based model to a convolution-based model. The results we obtain
during the transition process deliver useful messages for improving visual
recognition. Based on these observations, we propose a new architecture named
Visformer, which is abbreviated from the `Vision-friendly Transformer'. With
the same computational complexity, Visformer outperforms both the
Transformer-based and convolution-based models in terms of ImageNet
classification accuracy, and the advantage becomes more significant when the
model complexity is lower or the training set is smaller. The code is available
at this https URL

通过将 Transformer 模型逐步转化为基于卷积的模型，进行经验证实验表明，命名为 Visformer 的新架构比 Transformer 模型和卷积模型在 ImageNet 分类准确性方面表现更佳，尤其是当模型复杂度较低或者训练集较小时。