diff --git a/README.md b/README.md
index 3248eaff0a5e0285123a54782e710e4d49f11c68..59c23c53373472d761c46935951a095140379149 100644
--- a/README.md
+++ b/README.md
@@ -219,7 +219,7 @@ Accuracy and inference time metrics of SEResNeXt and Res2Net series models are s
| SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 8.67 | 28.09 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) |
| SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) |
| SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_vd_32x4d_pretrained.tar) |
-| SE_ResNeXt101_
32x4d | 0.7912 | 0.9420 | 18.82604 | 25.31814 | 15.02 | 46.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
+| SE_ResNeXt101_
32x4d | 0.7939 | 0.9443 | 18.82604 | 25.31814 | 15.02 | 46.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
| SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 45.83 | 114.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) |
diff --git a/README_cn.md b/README_cn.md
index 1027e42b9ed8bba973d306aa49c93e3d8872f2b9..87281dfa61c0e8508d2f4aeae3f922266d54f53b 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -221,7 +221,7 @@ SEResNeXt与Res2Net系列模型的精度、速度指标如下表所示,更多
| SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 8.67 | 28.09 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) |
| SE_ResNeXt50_
32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 8.02 | 26.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) |
| SE_ResNeXt50_vd_
32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 10.76 | 26.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_vd_32x4d_pretrained.tar) |
-| SE_ResNeXt101_
32x4d | 0.7912 | 0.9420 | 18.82604 | 25.31814 | 15.02 | 46.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
+| SE_ResNeXt101_
32x4d | 0.7939 | 0.9443 | 18.82604 | 25.31814 | 15.02 | 46.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) |
| SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 45.83 | 114.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) |
diff --git a/docs/en/models/SEResNext_and_Res2Net_en.md b/docs/en/models/SEResNext_and_Res2Net_en.md
index 6af2f33c6fd24d4eeeec5c53a7c2d24acdb1444b..4ccbce59121f5790757e665a56f4924a2fbbbba8 100644
--- a/docs/en/models/SEResNext_and_Res2Net_en.md
+++ b/docs/en/models/SEResNext_and_Res2Net_en.md
@@ -53,7 +53,7 @@ At present, there are a total of 24 pretrained models of the three categories op
| SE_ResNet50_vd | 0.795 | 0.948 | | | 8.670 | 28.090 |
| SE_ResNeXt50_32x4d | 0.784 | 0.940 | 0.789 | 0.945 | 8.020 | 26.160 |
| SE_ResNeXt50_vd_32x4d | 0.802 | 0.949 | | | 10.760 | 26.280 |
-| SE_ResNeXt101_32x4d | 0.791 | 0.942 | 0.793 | 0.950 | 15.020 | 46.280 |
+| SE_ResNeXt101_32x4d | 0.7939 | 0.9443 | 0.793 | 0.950 | 15.020 | 46.280 |
| SENet154_vd | 0.814 | 0.955 | | | 45.830 | 114.290 |
diff --git a/docs/zh_CN/models/SEResNext_and_Res2Net.md b/docs/zh_CN/models/SEResNext_and_Res2Net.md
index 7754d2f7a87d320199f1bf55da258cd958586dee..f2fd1c8f5511951d87d784124d7149d1720b029a 100644
--- a/docs/zh_CN/models/SEResNext_and_Res2Net.md
+++ b/docs/zh_CN/models/SEResNext_and_Res2Net.md
@@ -52,7 +52,7 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案,该方案可
| SE_ResNet50_vd | 0.795 | 0.948 | | | 8.670 | 28.090 |
| SE_ResNeXt50_32x4d | 0.784 | 0.940 | 0.789 | 0.945 | 8.020 | 26.160 |
| SE_ResNeXt50_vd_32x4d | 0.802 | 0.949 | | | 10.760 | 26.280 |
-| SE_ResNeXt101_32x4d | 0.791 | 0.942 | 0.793 | 0.950 | 15.020 | 46.280 |
+| SE_ResNeXt101_32x4d | 0.7939 | 0.9443 | 0.793 | 0.950 | 15.020 | 46.280 |
| SENet154_vd | 0.814 | 0.955 | | | 45.830 | 114.290 |
diff --git a/ppcls/modeling/architectures/dpn.py b/ppcls/modeling/architectures/dpn.py
index 7710a73b5f0ee597c3e6fb7bcc5a3cc309f6edcf..bfdb99c1f3527e81788f95ead5ecb1124a59fe09 100644
--- a/ppcls/modeling/architectures/dpn.py
+++ b/ppcls/modeling/architectures/dpn.py
@@ -232,7 +232,7 @@ class DPN(nn.Layer):
num_filters=init_num_filter,
filter_size=init_filter_size,
stride=2,
- pad=1,
+ pad=init_padding,
act='relu',
name="conv1")