diff --git a/README.md b/README.md
index 1d15512..f5d3175 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@ Also contains models that outperforms the above mentioned model, termed Expanded
 ## Setup
 Supports Keras with Theano and Tensorflow backend. Due to recent report that Theano will no longer be updated, Tensorflow is the default backend for this project now.
 
+Requires Pillow, imageio, sklearn, scipy, keras 2.3.1, tensorflow 1.15.0
 ## Usage
 
 **Note**: The project is going to be reworked. Therefore please refer to [Framework-Updates.md](https://github.com/titu1994/Image-Super-Resolution/blob/master/Framework-Update.md) to see the changes which will affect performance.
diff --git a/img_utils.py b/img-utils
similarity index 99%
rename from img_utils.py
rename to img-utils
index 6d4157b..dbca9c0 100644
--- a/img_utils.py
+++ b/img-utils
@@ -12,7 +12,6 @@
 
 '''
 _image_scale_multiplier is a special variable which is used to alter image size.
-
 The default image size is 32x32. If a true upscaling model is used, then the input image size is 16x16,
 which not offer adequate training samples.
 '''
diff --git a/models.py b/models.py
index da4eb6d..29795b9 100644
--- a/models.py
+++ b/models.py
@@ -31,10 +31,8 @@
 def PSNRLoss(y_true, y_pred):
     """
     PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error.
-
     It can be calculated as
     PSNR = 20 * log10(MAXp) - 10 * log10(MSE)
-
     When providing an unscaled input, MAXp = 255. Therefore 20 * log10(255)== 48.1308036087.
     However, since we are scaling our input, MAXp = 1. Therefore 20 * log10(1) = 0.
     Thus we remove that component completely and only compute the remaining MSE component.
@@ -134,7 +132,6 @@ def upscale(self, img_path, save_intermediate=False, return_image=False, suffix=
                 patch_size=8, mode="patch", verbose=True):
         """
         Standard method to upscale an image.
-
         :param img_path:  path to the image
         :param save_intermediate: saves the intermediate upscaled image (bilinear upscale)
         :param return_image: returns a image of shape (height, width, channels).