--- a/README.md
+++ b/README.md
@@ -1,95 +1,85 @@
-[![PyPI version](https://badge.fury.io/py/DigiPathAI.svg)](https://badge.fury.io/py/DigiPathAI)
-[![PyPI Downloads](https://static.pepy.tech/badge/digipathai)](https://pepy.tech/projects/digipathai)
-[![arXiv](https://img.shields.io/badge/arXiv-2001.00258-<COLOR>.svg)](https://arxiv.org/abs/2001.00258)
-
-
-# DigiPathAI
-A software application built on top of [openslide](https://openslide.org/) for viewing [whole slide images (WSI)](https://www.ncbi.nlm.nih.gov/pubmed/30307746) and performing pathological analysis 
-
-### Citation
-If you find this reference implementation useful in your research, please consider citing:
-```
-@article{khened2020generalized,
-  title={A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis},
-  author={Khened, Mahendra and Kori, Avinash and Rajkumar, Haran and Srinivasan, Balaji and Krishnamurthi, Ganapathy},
-  journal={arXiv preprint arXiv:2001.00258},
-  year={2020}
-}
-```
-# Features
-- Responsive WSI image viewer 
-- State of the art cancer AI pipeline to segment and display the cancerous tissue regions
-
-# Application Overview
-<p align="center">
-  <img src="imgs/demo.gif">
-</p>
-
-# Results
-<p align="center">
-  <img width="460" height="300" src="imgs/results_1.png">
-</p>
-
-# Installation
-Running of the AI pipeline requires a GPU and several deep learning modules. However, you can run just the UI as well.
-
-## Just the UI
-### Requirements
-- `openslide`
-- `flask`
-
-The following command will install only the dependencies listed above.
-```
-pip install DigiPathAI
-```
-
-## Entire AI pipeline
-### Requirements
-- `pytorch`
-- `torchvision`
-- `opencv-python`
-- `imgaug`
-- `matplotlib`
-- `scikit-learn`
-- `scikit-image`
-- `tensorflow-gpu >=1.14,<2`
-- `pydensecrf`
-- `pandas`
-- `wget`
-
-The following command will install the dependencies mentioned
-```
-pip install "DigiPathAI[gpu]"
-```
-
-Both installation methods install the same package, just different dependencies. Even if you had installed using the earlier command, you can install the rest of the dependencies manually. 
-
-# Usage 
-## Local server
-Traverse to the directory containing the openslide images and run the following command.
-```
-digipathai <host: localhost (default)> <port: 8080 (default)>
-```
-
-## Python API usage
-The application also has an API which can be used within python to perform the segmentation. 
-```
-from DigiPathAI.Segmentation import getSegmentation
-
-prediction = getSegmentation(img_path, 
-			patch_size  = 256, 
-			stride_size = 128,
-			batch_size  = 32,
-			quick       = True,
-			tta_list    = None,
-			crf         = False,
-			save_path   = None,
-			status      = None)
-```
-
-# Contact
-- Avinash Kori (koriavinash1@gmail.com)
-- Haran Rajkumar (haranrajkumar97@gmail.com)
-
-[![ko-fi](https://www.ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/M4M132RPG)
-
+[![PyPI version](https://badge.fury.io/py/DigiPathAI.svg)](https://badge.fury.io/py/DigiPathAI)
+[![PyPI Downloads](https://static.pepy.tech/badge/digipathai)](https://pepy.tech/projects/digipathai)
+[![arXiv](https://img.shields.io/badge/arXiv-2001.00258-<COLOR>.svg)](https://arxiv.org/abs/2001.00258)
+
+
+# DigiPathAI
+A software application built on top of [openslide](https://openslide.org/) for viewing [whole slide images (WSI)](https://www.ncbi.nlm.nih.gov/pubmed/30307746) and performing pathological analysis 
+
+### Citation
+If you find this reference implementation useful in your research, please consider citing:
+```
+@article{khened2020generalized,
+  title={A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis},
+  author={Khened, Mahendra and Kori, Avinash and Rajkumar, Haran and Srinivasan, Balaji and Krishnamurthi, Ganapathy},
+  journal={arXiv preprint arXiv:2001.00258},
+  year={2020}
+}
+```
+# Features
+- Responsive WSI image viewer 
+- State of the art cancer AI pipeline to segment and display the cancerous tissue regions
+
+# Installation
+Running of the AI pipeline requires a GPU and several deep learning modules. However, you can run just the UI as well.
+
+## Just the UI
+### Requirements
+- `openslide`
+- `flask`
+
+The following command will install only the dependencies listed above.
+```
+pip install DigiPathAI
+```
+
+## Entire AI pipeline
+### Requirements
+- `pytorch`
+- `torchvision`
+- `opencv-python`
+- `imgaug`
+- `matplotlib`
+- `scikit-learn`
+- `scikit-image`
+- `tensorflow-gpu >=1.14,<2`
+- `pydensecrf`
+- `pandas`
+- `wget`
+
+The following command will install the dependencies mentioned
+```
+pip install "DigiPathAI[gpu]"
+```
+
+Both installation methods install the same package, just different dependencies. Even if you had installed using the earlier command, you can install the rest of the dependencies manually. 
+
+# Usage 
+## Local server
+Traverse to the directory containing the openslide images and run the following command.
+```
+digipathai <host: localhost (default)> <port: 8080 (default)>
+```
+
+## Python API usage
+The application also has an API which can be used within python to perform the segmentation. 
+```
+from DigiPathAI.Segmentation import getSegmentation
+
+prediction = getSegmentation(img_path, 
+			patch_size  = 256, 
+			stride_size = 128,
+			batch_size  = 32,
+			quick       = True,
+			tta_list    = None,
+			crf         = False,
+			save_path   = None,
+			status      = None)
+```
+
+# Contact
+- Avinash Kori (koriavinash1@gmail.com)
+- Haran Rajkumar (haranrajkumar97@gmail.com)
+
+[![ko-fi](https://www.ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/M4M132RPG)
+