diff --git a/README.md b/README.md
index c8e71ec..b0721a7 100644
--- a/README.md
+++ b/README.md
@@ -9,14 +9,6 @@ The new label file is named **_fer2013new.csv_** and contains the same number of
 
 The format of the CSV file is as follows: usage,	neutral, happiness,	surprise, sadness, anger, disgust, fear, contempt, unknown, NF. Columns "usage" is the same as the original FER label to differentiate between training, public test and private test sets. The other columns are the vote count for each emotion with the addition of unknown and NF (Not a Face).
 
-
-## Training data
-We provide a simple script `generate_training_data.py` in python that takes **_fer2013.csv_** and **_fer2013new.csv_** as inputs, merge both CSV files and export all the images into a png files for the trainer to process.
-
-```
-python generate_training_data.py -d <dataset base folder> -fer <fer2013.csv path> -ferplus <fer2013new.csv path>
-```
-
 ## Training
 We also provide a training code with implementation for all the training modes (majority, probability, cross entropy and multi-label) described in https://arxiv.org/abs/1608.01041. The training code uses MS Cognitive Toolkit (formerly CNTK) available in: https://github.com/Microsoft/CNTK.
 
@@ -62,7 +54,14 @@ fer0000001.png
 fer0000002.png
 fer0000003.png
 ```
-The folders don't contain the actual images, you will need to download them from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data, then extract the images from the FER csv file in such a way, that all images corresponding to "Training" go to FER2013Train folder, all images corresponding to "PublicTest" go to FER2013Valid folder and all images corresponding to "PrivateTest" go to FER2013Test folder.
+The folders don't contain the actual images, you will need to download them from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data, then extract the images from the FER csv file in such a way, that all images corresponding to "Training" go to FER2013Train folder, all images corresponding to "PublicTest" go to FER2013Valid folder and all images corresponding to "PrivateTest" go to FER2013Test folder. Or you can use `generate_training_data.py` script to do all the above for you as mentioned in next section.
+
+### Training data
+We provide a simple script `generate_training_data.py` in python that takes **_fer2013.csv_** and **_fer2013new.csv_** as inputs, merge both CSV files and export all the images into a png files for the trainer to process.
+
+```
+python generate_training_data.py -d <dataset base folder> -fer <fer2013.csv path> -ferplus <fer2013new.csv path>
+```
 
 # Citation
 If you use the new FER+ label or the sample code or part of it in your research, please cite the following: