diff --git a/docs/en/models/Twins_en.md b/docs/en/models/Twins_en.md
index 0096066e5fd5acc9a754438f4c4b7692b0649c06..0a9dede002e436e11b35025ea87fab4b83bca2fa 100644
--- a/docs/en/models/Twins_en.md
+++ b/docs/en/models/Twins_en.md
@@ -7,7 +7,7 @@
 
 <a name='1'></a>
 ## 1. Overview
-The Twins network includes Twins-PCPVT and Twins-SVT, which focuses on the meticulous design of the spatial attention mechanism, resulting in a simple but more effective solution. Since the architecture only involves matrix multiplication, and the current deep learning framework has a high degree of optimization for matrix multiplication, the architecture is very efficient and easy to implement. Moreover, this architecture can achieve excellent performance in a variety of downstream vision tasks such as image classification, target detection, and semantic segmentation. [Paper](https://arxiv.org/abs/2104.13840).
+The Twins network includes Twins-PCPVT and Twins-AVT-GVT, which focuses on the meticulous design of the spatial attention mechanism, resulting in a simple but more effective solution. Since the architecture only involves matrix multiplication, and the current deep learning framework has a high degree of optimization for matrix multiplication, the architecture is very efficient and easy to implement. Moreover, this architecture can achieve excellent performance in a variety of downstream vision tasks such as image classification, target detection, and semantic segmentation. [Paper](https://arxiv.org/abs/2104.13840).
 
 <a name='2'></a>
 ## 2. Accuracy, FLOPs and Parameters
diff --git a/docs/zh_CN/models/ImageNet1k/Twins.md b/docs/zh_CN/models/ImageNet1k/Twins.md
index 728a22089d748f2beef831377b288ec5c4ff9cda..d35ef8f4b63e56c9a2c6d00a33916bb8eae3b656 100644
--- a/docs/zh_CN/models/ImageNet1k/Twins.md
+++ b/docs/zh_CN/models/ImageNet1k/Twins.md
@@ -26,7 +26,7 @@
 
 ### 1.1 模型简介
 
-Twins 网络包括 Twins-PCPVT 和 Twins-SVT，其重点对空间注意力机制进行了精心设计，得到了简单却更为有效的方案。由于该体系结构仅涉及矩阵乘法，而目前的深度学习框架中对矩阵乘法有较高的优化程度，因此该体系结构十分高效且易于实现。并且，该体系结构在图像分类、目标检测和语义分割等多种下游视觉任务中都能够取得优异的性能。[论文地址](https://arxiv.org/abs/2104.13840)。
+Twins 网络包括 Twins-PCPVT 和 Twins-ALT-GVT，其重点对空间注意力机制进行了精心设计，得到了简单却更为有效的方案。由于该体系结构仅涉及矩阵乘法，而目前的深度学习框架中对矩阵乘法有较高的优化程度，因此该体系结构十分高效且易于实现。并且，该体系结构在图像分类、目标检测和语义分割等多种下游视觉任务中都能够取得优异的性能。[论文地址](https://arxiv.org/abs/2104.13840)。
 
 <a name='1.2'></a>
 
diff --git a/ppcls/arch/backbone/__init__.py b/ppcls/arch/backbone/__init__.py
index 78e9b4dc25c5132229afa1273219d94511cd11fe..ee4afdf380d5e7bbb6e965272af88280cca7afd0 100644
--- a/ppcls/arch/backbone/__init__.py
+++ b/ppcls/arch/backbone/__init__.py
@@ -59,7 +59,7 @@ from .model_zoo.swin_transformer_v2 import SwinTransformerV2_tiny_patch4_window8
 from .model_zoo.cswin_transformer import CSWinTransformer_tiny_224, CSWinTransformer_small_224, CSWinTransformer_base_224, CSWinTransformer_large_224, CSWinTransformer_base_384, CSWinTransformer_large_384
 from .model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
 from .model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0
-from .model_zoo.gvt import pcpvt_small, pcpvt_base, pcpvt_large, alt_gvt_small, alt_gvt_base, alt_gvt_large
+from .model_zoo.twins import pcpvt_small, pcpvt_base, pcpvt_large, alt_gvt_small, alt_gvt_base, alt_gvt_large
 from .model_zoo.levit import LeViT_128S, LeViT_128, LeViT_192, LeViT_256, LeViT_384
 from .model_zoo.dla import DLA34, DLA46_c, DLA46x_c, DLA60, DLA60x, DLA60x_c, DLA102, DLA102x, DLA102x2, DLA169
 from .model_zoo.rednet import RedNet26, RedNet38, RedNet50, RedNet101, RedNet152
diff --git a/ppcls/arch/backbone/model_zoo/gvt.py b/ppcls/arch/backbone/model_zoo/twins.py
similarity index 100%
rename from ppcls/arch/backbone/model_zoo/gvt.py
rename to ppcls/arch/backbone/model_zoo/twins.py