diff --git a/docs_kneron/stdc_step_by_step.md b/docs_kneron/stdc_step_by_step.md
index 58f7a18..f116c4c 100644
--- a/docs_kneron/stdc_step_by_step.md
+++ b/docs_kneron/stdc_step_by_step.md
@@ -44,7 +44,7 @@ git clone https://github.com/kneron/kneron-mmsegmentation.git
 cd kneron-mmsegmentation
 ```
 
-### Step 1-2-4: Install Required Python Packages for Building and Installing kneron-mmsegmentation
+### Step 1-2-4: Install Required Python Libraries for Building and Installing kneron-mmsegmentation
 
 ```shell
 pip install -r requirements_kneron.txt
@@ -83,7 +83,7 @@ ln -s $(realpath path/to/your/cityscapes) kneron-mmsegmentation/data
 # Replace all "path/to/your" above with where you want to put the dataset!
 ```
 
-Then, we need *cityscapesScripts* to preprocess the CityScapes dataset. If you completely followed our [Step 1-2-4](#step-1-2-4-install-required-python-packages-for-building-and-installing-kneron-mmsegmentation), you should have python package *cityscapesScripts* installed (if no, execute `pip install cityscapesScripts` command).
+Then, we need *cityscapesScripts* to preprocess the CityScapes dataset. If you completely followed our [Step 1-2-4](#step-1-2-4-install-required-python-libraries-for-building-and-installing-kneron-mmsegmentation), you should have python library *cityscapesScripts* installed (if no, execute `pip install cityscapesScripts` command).
 
 ```shell
 # Replace "path/to/your" with where you want to put the dataset!
@@ -156,7 +156,7 @@ cd kneron-mmsegmentation
 python tools/train.py configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py
 ```
 
-And kneron-mmsegmentation will generate `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes` folder and save the configuration file and all checkpoints there.
+kneron-mmsegmentation will generate `work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes` folder and save the configuration file and all checkpoints there.
 
 # Step 3: Test Trained Model
 `tools/test.py` is a script that generates inference results from test set with our pytorch model and evaluates the results to see if our pytorch model is well trained (if `--eval` argument is given). Note that it's always good to evluate our pytorch model before deploying it.
@@ -241,19 +241,19 @@ Note that the ONNX results may differ from the PyTorch results due to some imple
 
 # Step 5: Convert ONNX File to [NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) Model for Kneron Platform
  
-### Step 5-1: Install Kneron toolchain docker:
+## Step 5-1: Install Kneron toolchain docker:
 
-* check [document](http://doc.kneron.com/docs/#toolchain/manual/#1-installation)
+* Check [Kneron Toolchain Installation Document](http://doc.kneron.com/docs/#toolchain/manual/#1-installation)
 
-### Step 5-2: Mout Kneron toolchain docker 
+## Step 5-2: Mount Kneron toolchain docker
 
 * Mount a folder (e.g. '/mnt/hgfs/Competition') to toolchain docker container as `/data1`. The converted ONNX in Step 3 should be put here. All the toolchain operation should happen in this folder.
 ```
 sudo docker run --rm -it -v /mnt/hgfs/Competition:/data1 kneron/toolchain:latest
 ```
 
-### Step 5-3: Import KTC and required lib in python shell
-* Here we demonstrate how to go through all Kneron Toolchain (KTC) flow through Python API:
+## Step 5-3: Import KTC and the required libraries in python
+
 ```python
 import ktc
 import numpy as np
@@ -262,7 +262,8 @@ import onnx
 from PIL import Image
 ```
 
-### Step 5-4: Optimize the onnx model
+## Step 5-4: Optimize the onnx model
+
 ```python
 onnx_path = '/data1/latest.onnx'
 m = onnx.load(onnx_path)
@@ -270,7 +271,7 @@ m = ktc.onnx_optimizer.onnx2onnx_flow(m)
 onnx.save(m,'latest.opt.onnx')
 ```
 
-### Step 5-5: Configure and load data necessary for ktc, and check if onnx is ok for toolchain
+## Step 5-5: Configure and load data needed for ktc, and check if onnx is ok for toolchain
 ```python 
 # npu (only) performance simulation
 km = ktc.ModelConfig((&)model_id_on_public_field, "0001", "720", onnx_model=m)
@@ -278,13 +279,13 @@ eval_result = km.evaluate()
 print("\nNpu performance evaluation result:\n" + str(eval_result))
 ```
 
-### Step 5-6: quantize the onnx model
+## Step 5-6: Quantize the onnx model
 We [sampled 3 images from Cityscapes dataset](https://www.kneron.com/tw/support/education-center/?folder=OpenMMLab%20Kneron%20Edition/misc/&download=41) (3 images) as quantization data. To test our quantized model:
-1. Download the zip file 
+1. Download the [zip file](https://www.kneron.com/tw/support/education-center/?folder=OpenMMLab%20Kneron%20Edition/misc/&download=41)
 2. Extract the zip file as a folder named `cityscapes_minitest`
 3. Put the `cityscapes_minitest` into docker mounted folder (the path in docker container should be `/data1/cityscapes_minitest`)
 
-The following script will do some preprocess(should be the same as training code) on our quantization data, and put it in a list:
+The following script will preprocess (should be the same as training code) our quantization data, and put it in a list:
 
 ```python
 import os
@@ -303,7 +304,7 @@ for (dirpath, dirnames, filenames) in walk("/data1/cityscapes_minitest"):
         img_list.append(img_data)
 ```
 
-Then perform quantization. The BIE model will be generated at `/data1/output.bie`.
+Then perform quantization. The generated BIE model will put generated at `/data1/output.bie`.
 
 ```python
 # fixed-point analysis
@@ -311,7 +312,7 @@ bie_model_path = km.analysis({"input": img_list})
 print("\nFixed-point analysis done. Save bie model to '" + str(bie_model_path) + "'")
 ```
 
-### Step 5-7: Compile
+## Step 5-7: Compile
 
 The final step is compile the BIE model into an NEF model.
 ```python
@@ -343,23 +344,23 @@ Recommend you read [Kneron PLUS official document](http://doc.kneron.com/docs/#p
 
 ### Step 7-2: Download STDC example code
 * Go to [Kneron education center](https://www.kneron.com/tw/support/education-center/)
-* Scroll down to OpenMMLab Kneron Edition table
-* Select kneron-mmsegmentation
-* Select STDC
-* Download stdc_plus_demo.zip 
-* unzip downloaded `stdc_plus_demo`
+* Scroll down to **OpenMMLab Kneron Edition** table
+* Select **kneron-mmsegmentation**
+* Select **STDC**
+* Download **stdc_plus_demo.zip**
+* unzip downloaded **stdc_plus_demo**
 
 ### Step 7-3: Test enviroment is ready (require [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1))
-In `stdc_plus_demo`, we provide a stdc example model and image for quick test. 
+In `stdc_plus_demo`, we provide a STDC-Seg example model and image for quick test. 
 * Plug in [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1) into your computer USB port
 * Go to the stdc_plus_demo folder
 ```bash
 cd /PATH/TO/stdc_plus_demo
 ```
 
-* Install required library
+* Install required python libraries
 ```bash
-pip insall -r requirements.txt
+pip install -r requirements.txt
 ```
 
 * Run example on [KL720 USB accelerator](https://www.kneo.ai/products/hardwares/HW2020122500000007/1)
@@ -368,7 +369,7 @@ python KL720DemoGenericInferenceSTDC_BypassHwPreProc.py -nef ./example_stdc_720.
 ```
 
 Then you can see the inference result is saved as output_000000000641.jpg in the same folder.
-And the expected result of the command above will be something similar to the following text:
+The expected result of the command above will be something similar to the following text:
 ```plain
 ...
 [Connect Device]
@@ -393,4 +394,4 @@ And the expected result of the command above will be something similar to the fo
 Use the same script in previous step, but now we change the input NEF model path and image to yours
 ```bash
 python KL720DemoGenericInferenceSTDC_BypassHwPreProc.py -img /PATH/TO/YOUR_IMAGE.bmp -nef /PATH/TO/YOUR/720_NEF_MODEL.nef
-```
+```
\ No newline at end of file