diff --git a/README.md b/README.md
index e6bdfcf22f94ffdcc574689b443ac3b9c6e3bb8a..75d156bd15e48df55fc12031949c96f5280c8884 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
## Features
-- Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 24 series of classification network structures and training configurations, 122 models' pretrained weights and their evaluation metrics.
+- Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 29 series of classification network structures and training configurations, 134 models' pretrained weights and their evaluation metrics.
- SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.
diff --git a/README_cn.md b/README_cn.md
index 01b1c6b802035403816a83c0cecdc63343bd636c..5d11380c23a220c8d90dc26f13db70a5426019ee 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -23,7 +23,7 @@
## 特性
-- 丰富的模型库：基于ImageNet1k分类数据集，PaddleClas提供了24个系列的分类网络结构和训练配置，122个预训练模型和性能评估。
+- 丰富的模型库：基于ImageNet1k分类数据集，PaddleClas提供了29个系列的分类网络结构和训练配置，134个预训练模型和性能评估。
- SSLD知识蒸馏：基于该方案蒸馏模型的识别准确率普遍提升3%以上。
diff --git a/docs/en/models/Others_en.md b/docs/en/models/Others_en.md
index ddeb489d070766762f7b03ccc1c1e9582c7203f1..4511ddb47578a5bef913701d2c45f827e2b6b4c8 100644
--- a/docs/en/models/Others_en.md
+++ b/docs/en/models/Others_en.md
@@ -24,8 +24,6 @@ DarkNet53 is designed for object detection by YOLO author in the paper. The netw
| VGG16 | 0.720 | 0.907 | 0.715 | 0.901 | 30.810 | 138.340 |
| VGG19 | 0.726 | 0.909 | | | 39.130 | 143.650 |
| DarkNet53 | 0.780 | 0.941 | 0.772 | 0.938 | 18.580 | 41.600 |
-| ResNet50_ACNet | 0.767 | 0.932 | | | 10.730 | 33.110 |
-| ResNet50_ACNet
_deploy | 0.767 | 0.932 | | | 8.190 | 25.550 |
@@ -42,9 +40,6 @@ DarkNet53 is designed for object detection by YOLO author in the paper. The netw
| VGG16 | 224 | 256 | 2.616 |
| VGG19 | 224 | 256 | 3.076 |
| DarkNet53 | 256 | 256 | 3.139 |
-| ResNet50_ACNet
_deploy | 224 | 256 | 5.626 |
-
-
## Inference speed based on T4 GPU
@@ -58,5 +53,3 @@ DarkNet53 is designed for object detection by YOLO author in the paper. The netw
| VGG16 | 224 | 256 | 3.13237 | 7.19257 | 12.50913 | 5.61769 | 16.40064 | 32.03939 |
| VGG19 | 224 | 256 | 3.69987 | 8.59168 | 15.07866 | 6.65221 | 20.4334 | 41.55902 |
| DarkNet53 | 256 | 256 | 3.18101 | 5.88419 | 10.14964 | 4.10829 | 12.1714 | 22.15266 |
-| ResNet50_ACNet | 256 | 256 | 3.89002 | 4.58195 | 9.01095 | 5.33395 | 10.96843 | 18.70368 |
-| ResNet50_ACNet_deploy | 224 | 256 | 2.6823 | 5.944 | 7.16655 | 3.49161 | 7.78374 | 13.94361 |
diff --git a/docs/en/models/models_intro_en.md b/docs/en/models/models_intro_en.md
index 475a300d604e0c5173e967f9dde960782c8e6caa..5e40babb56e864e23010b7a090fb38d91230ee53 100644
--- a/docs/en/models/models_intro_en.md
+++ b/docs/en/models/models_intro_en.md
@@ -2,7 +2,7 @@
## Overview
-Based on the ImageNet1k classification dataset, the 23 classification network structures supported by PaddleClas and the corresponding 117 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters.
+Based on the ImageNet1k classification dataset, the 29 classification network structures supported by PaddleClas and the corresponding 134 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters.
## Evaluation environment
* CPU evaluation environment is based on Snapdragon 855 (SD855).
diff --git a/docs/zh_CN/models/Others.md b/docs/zh_CN/models/Others.md
index c24f76652bc5e322df2533bbf8c59889bb420910..76e3f1e122b9c2f1ee556688c5d62cea18514a0c 100644
--- a/docs/zh_CN/models/Others.md
+++ b/docs/zh_CN/models/Others.md
@@ -22,8 +22,6 @@ DarkNet53是YOLO作者在论文设计的用于目标检测的backbone，该网
| VGG16 | 0.720 | 0.907 | 0.715 | 0.901 | 30.810 | 138.340 |
| VGG19 | 0.726 | 0.909 | | | 39.130 | 143.650 |
| DarkNet53 | 0.780 | 0.941 | 0.772 | 0.938 | 18.580 | 41.600 |
-| ResNet50_ACNet | 0.767 | 0.932 | | | 10.730 | 33.110 |
-| ResNet50_ACNet
_deploy | 0.767 | 0.932 | | | 8.190 | 25.550 |
@@ -40,7 +38,6 @@ DarkNet53是YOLO作者在论文设计的用于目标检测的backbone，该网
| VGG16 | 224 | 256 | 2.616 |
| VGG19 | 224 | 256 | 3.076 |
| DarkNet53 | 256 | 256 | 3.139 |
-| ResNet50_ACNet
_deploy | 224 | 256 | 5.626 |
@@ -56,5 +53,3 @@ DarkNet53是YOLO作者在论文设计的用于目标检测的backbone，该网
| VGG16 | 224 | 256 | 3.13237 | 7.19257 | 12.50913 | 5.61769 | 16.40064 | 32.03939 |
| VGG19 | 224 | 256 | 3.69987 | 8.59168 | 15.07866 | 6.65221 | 20.4334 | 41.55902 |
| DarkNet53 | 256 | 256 | 3.18101 | 5.88419 | 10.14964 | 4.10829 | 12.1714 | 22.15266 |
-| ResNet50_ACNet | 256 | 256 | 3.89002 | 4.58195 | 9.01095 | 5.33395 | 10.96843 | 18.70368 |
-| ResNet50_ACNet_deploy | 224 | 256 | 2.6823 | 5.944 | 7.16655 | 3.49161 | 7.78374 | 13.94361 |
diff --git a/docs/zh_CN/models/models_intro.md b/docs/zh_CN/models/models_intro.md
index c12952d9d7a6df049acbf8a5be66d5cb26161726..8c7df7bce8ca866356175ad19e8019876db85cab 100644
--- a/docs/zh_CN/models/models_intro.md
+++ b/docs/zh_CN/models/models_intro.md
@@ -2,7 +2,7 @@
## 概述
-基于ImageNet1k分类数据集，PaddleClas支持的23种系列分类网络结构以及对应的117个图像分类预训练模型如下所示，训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现。
+基于ImageNet1k分类数据集，PaddleClas支持的29种系列分类网络结构以及对应的134个图像分类预训练模型如下所示，训练技巧、每个系列网络结构的简单介绍和性能评估将在相应章节展现。
## 评估环境
* CPU的评估环境基于骁龙855（SD855）。