diff --git a/doc/tutorials/imagenet_model/resnet_model_en.md b/doc/tutorials/imagenet_model/resnet_model_en.md
index 5403ab9f17d2399fee878d0f3c512cb166aba06f..93864b82ec290c08262a8113037adcaaeb2e4c0d 100644
--- a/doc/tutorials/imagenet_model/resnet_model_en.md
+++ b/doc/tutorials/imagenet_model/resnet_model_en.md
@@ -52,7 +52,7 @@ See ```demo/model_zoo/resnet/resnet.py```. This config contains network of 50, 1
 
 ### Network Visualization
 
-You can get a diagram of ResNet network by running the following commands. The script generates dot file and then converts dot file to PNG file, which uses installed draw_dot tool in our server. If you can not access the server, just install graphviz to convert dot file.
+You can get a diagram of ResNet network by running the following commands. The script generates dot file and then converts dot file to PNG file, which needs to install graphviz to convert.
 
 ```
 cd demo/model_zoo/resnet
@@ -238,8 +238,6 @@ python classify.py \
 * \--output_layer="xxx,xxx":   specify layers to extract features.
 * \--output_dir=features:      output diretcoty.
 
-Note, since the convolution layer in these ResNet models is suitable for the cudnn implementation which only support GPU. It not support CPU mode because of compatibility issue and we will fix later.
-
 If run successfully, you will see features saved in `features/batch_0`, this file is produced with cPickle. You can use `load_feature_py` interface in `load_feature.py` to open the file, and it returns a dictionary as follows:
 
 ```