diff --git a/PaddleCV/PaddleDetection/slim/distillation/README.md b/PaddleCV/PaddleDetection/slim/distillation/README.md
index af1962e4cc8e4ce24346ea95003a21534ed92136..e970cc42b54c17a6131c4873662fb2be46767b60 100755
--- a/PaddleCV/PaddleDetection/slim/distillation/README.md
+++ b/PaddleCV/PaddleDetection/slim/distillation/README.md
@@ -128,6 +128,8 @@ python ../infer.py \
 
 ## 示例结果
 
+>当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
+
 ### MobileNetV1-YOLO-V3
 
 | FLOPS |Box AP|
diff --git a/PaddleCV/PaddleDetection/slim/prune/README.md b/PaddleCV/PaddleDetection/slim/prune/README.md
index 80a572f22e1ea0252a5434024061cd618d6da3d6..b06fdd2bdd6a3cd75eb00ab7952dfd546b2bfaad 100644
--- a/PaddleCV/PaddleDetection/slim/prune/README.md
+++ b/PaddleCV/PaddleDetection/slim/prune/README.md
@@ -209,6 +209,8 @@ python ../infer.py \
 
 ## 示例结果
 
+> 当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
+
 ### MobileNetV1-YOLO-V3
 
 | FLOPS |Box AP| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
diff --git a/PaddleCV/PaddleDetection/slim/quantization/README.md b/PaddleCV/PaddleDetection/slim/quantization/README.md
index 20d330715721718d451f8460d75edc89ca41a099..86ef5a5af2bcbb310a769c56375a91cc759d5374 100644
--- a/PaddleCV/PaddleDetection/slim/quantization/README.md
+++ b/PaddleCV/PaddleDetection/slim/quantization/README.md
@@ -224,6 +224,8 @@ FP32模型可使用PaddleLite进行加载预测，可参见教程[Paddle-Lite如
 
 ## 示例结果
 
+>当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
+
 ### MobileNetV1
 
 | weight量化方式 | activation量化方式| Box ap |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
diff --git a/PaddleSlim/classification/distillation/README.md b/PaddleSlim/classification/distillation/README.md
index b910497394a806496c6d90ff65adfafeb6e03b31..9f07905082c8e8d2d14b80875808c9a912391fcf 100755
--- a/PaddleSlim/classification/distillation/README.md
+++ b/PaddleSlim/classification/distillation/README.md
@@ -151,6 +151,8 @@ python infer.py \
 
 ## 示例结果
 
+> 当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
+
 ### MobileNetV1
 
 | FLOPS    | top1_acc/top5_acc |
diff --git a/PaddleSlim/classification/models/resnet.py b/PaddleSlim/classification/models/resnet.py
index df13bf259853f763d0acc357243c8e76145d5758..09e83675ed53a8c75a80ef53392221b2332043cd 100644
--- a/PaddleSlim/classification/models/resnet.py
+++ b/PaddleSlim/classification/models/resnet.py
@@ -51,12 +51,13 @@ class ResNet():
             stride=2,
             act='relu',
             name=prefix_name + conv1_name)
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
+        with fluid.name_scope("skip_quant"):
+            conv = fluid.layers.pool2d(
+                input=conv,
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
 
         if layers >= 50:
             for block in range(len(depth)):
diff --git a/PaddleSlim/classification/pruning/README.md b/PaddleSlim/classification/pruning/README.md
index 05480dedabf342ee0d639418f7bf9cc399d94d03..185903b123a36272f7c42b809279975dc887f6ee 100644
--- a/PaddleSlim/classification/pruning/README.md
+++ b/PaddleSlim/classification/pruning/README.md
@@ -140,6 +140,8 @@ python infer.py \
 
 ## 示例结果
 
+>当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
+
 ### MobileNetV1
 
 | FLOPS |top1_acc/top5_acc| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
diff --git a/PaddleSlim/classification/quantization/README.md b/PaddleSlim/classification/quantization/README.md
index c04aeeea5b124435e7305d0a60ebc9ec6addfb46..7119fef31562e62ab8b395ecb0fbfe685f3f7089 100644
--- a/PaddleSlim/classification/quantization/README.md
+++ b/PaddleSlim/classification/quantization/README.md
@@ -162,6 +162,8 @@ FP32模型可使用Paddle-Lite进行加载预测，可参见教程[Paddle-Lite
 
 ## 示例结果
 
+>当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
+
 ### MobileNetV1
 
 | weight量化方式 | activation量化方式| top1_acc/top5_acc |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)| 模型下载|