diff --git a/cv/classification/inceptionv3/pytorch/README.md b/cv/classification/inceptionv3/pytorch/README.md
index 84ba8fac5d93e15674f3e5f49186b8c72052655a..731ae81135c838904d06cb0a35ca4b019d45f9b1 100644
--- a/cv/classification/inceptionv3/pytorch/README.md
+++ b/cv/classification/inceptionv3/pytorch/README.md
@@ -3,12 +3,14 @@
 ## Model description
 Inception-v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifer to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead).
 
-## Step 1: Installing
+## Step 1: Installation
 
 ```bash
 pip3 install -r requirements.txt
 ```
 
+## Step 2: Preparing datasets
+
 Sign up and login in [ImageNet official website](https://www.image-net.org/index.php), then choose 'Download' to download the whole ImageNet dataset. Specify `/path/to/imagenet` to your ImageNet path in later training process.
 
 The ImageNet dataset path structure should look like:
@@ -27,20 +29,16 @@ imagenet
 └── val_list.txt
 ```
 
-:beers: Done!
-
-## Step 2: Training
-### Multiple GPUs on one machine (AMP)
+## Step 3: Training
 
-Set data path by `export DATA_PATH=/path/to/imagenet`. The following command uses all cards to train:
 
 ```bash
+# Set data path
+export DATA_PATH=/path/to/imagenet
+
+# Multiple GPUs on one machine (AMP)
 bash train_inception_v3_amp_dist.sh
 ```
 
-:beers: Done!
-
-
-
 ## Reference
 - [torchvision](https://github.com/pytorch/vision/tree/main/references/classification)
diff --git a/cv/classification/mobilenetv3/pytorch/README.md b/cv/classification/mobilenetv3/pytorch/README.md
index 0580a8cf9853fa8420f41f39aed61974933b99ca..1e83d9b7b7fb4658bf2ac6a18b1fcd5f14970f1e 100644
--- a/cv/classification/mobilenetv3/pytorch/README.md
+++ b/cv/classification/mobilenetv3/pytorch/README.md
@@ -3,11 +3,13 @@
 ## Model description
 MobileNetV3 is a convolutional neural network that is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm, and then subsequently improved through novel architecture advances. Advances include (1) complementary search techniques, (2) new efficient versions of nonlinearities practical for the mobile setting, (3) new efficient network design.
 
-## Step 1: Installing
+## Step 1: Installation
 ```bash
 pip3 install -r requirements.txt
 ```
 
+## Step 2: Preparing datasets
+
 Sign up and login in [ImageNet official website](https://www.image-net.org/index.php), then choose 'Download' to download the whole ImageNet dataset. Specify `/path/to/imagenet` to your ImageNet path in later training process.
 
 The ImageNet dataset path structure should look like:
@@ -26,17 +28,15 @@ imagenet
 └── val_list.txt
 ```
 
-:beers: Done!
-
-## Step 2: Training
-### Multiple GPUs on one machine (AMP)
-Set data path by `export DATA_PATH=/path/to/imagenet`. The following command uses all cards to train:
+## Step 3: Training
 
 ```bash
-bash train_mobilenet_v3_large_dist.sh
-```
+# Set data path
+export DATA_PATH=/path/to/imagenet
 
-:beers: Done!
+# Multiple GPUs on one machine (AMP)
+bash train_mobilenet_v3_large_amp_dist.sh
+```
 
 ## Reference
 - [torchvision](https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small)
diff --git a/cv/classification/vgg/pytorch/README.md b/cv/classification/vgg/pytorch/README.md
index dfff6a19b18b1c8844acb46f6f49c416df64171f..d7be136b52d47c7b9e0490bb8824b466cfb24d55 100644
--- a/cv/classification/vgg/pytorch/README.md
+++ b/cv/classification/vgg/pytorch/README.md
@@ -3,23 +3,39 @@
 VGG is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.
 
 
-## Step 1: Preparing
+## Step 1: Installation
 
-### Install requirements
 ```bash
 pip3 install -r requirements.txt
 ```
 
-### Set up dataset path
-Sign up and login in [imagenet official website](https://www.image-net.org/index.php), then choose 'Download' to download the whole imagenet dataset. Specify `/path/to/imagenet` to your imagenet path in later training process.
-:beers: Done!
+## Step 2: Preparing datasets
 
+Sign up and login in [ImageNet official website](https://www.image-net.org/index.php), then choose 'Download' to download the whole ImageNet dataset. Specify `/path/to/imagenet` to your ImageNet path in later training process.
 
-## Step 2: Training
-### Multiple GPUs on one machine
-Set data path by `export DATA_PATH=/path/to/imagenet`. The following command uses all cards to train:
+The ImageNet dataset path structure should look like:
 
 ```bash
+imagenet
+├── train
+│   └── n01440764
+│       ├── n01440764_10026.JPEG
+│       └── ...
+├── train_list.txt
+├── val
+│   └── n01440764
+│       ├── ILSVRC2012_val_00000293.JPEG
+│       └── ...
+└── val_list.txt
+```
+
+## Step 3: Training
+
+```bash
+# Set data path
+export DATA_PATH=/path/to/imagenet
+
+# Multiple GPUs on one machine
 bash train_vgg16_amp_dist.sh
 ```
 Install zlib-1.2.9 if reports "iZLIB_1.2.9 not found" when run train_vgg16_amp_dist.sh
@@ -33,10 +49,5 @@ cd ../
 rm -rf zlib-1.2.9.tar.gz zlib-1.2.9/
 ```
 
-
-
-:beers: Done!
-
-
 ## Reference
 - [torchvision](https://github.com/pytorch/vision/tree/main/references/classification)