Updated readme

README.md

 # Indian Classical Dance Classification from Dance Poses
 ### Authors:
-Aarushi Aiyyar : 203050045\
-Bhavesh Yadav : 193050052\
-Khyati Oswal : 203050058\
-Raj Gite : 203050092\
-Smit Gangurde : 203050108\
+Aarushi Aiyyar : 203050045  
+Bhavesh Yadav : 193050052  
+Khyati Oswal : 203050058  
+Raj Gite : 203050092  
+Smit Gangurde : 203050108  
 Yavnika Bhagat : 203050041
 ### Problem Statement:
-Identify the type of Indian dance form from a dance pose image. Use multiple techniques to classify the images and compare the various techniques.\
+Identify the type of Indian dance form from a dance pose image. Use multiple techniques to classify the images and compare the various techniques.  
 ### Dataset:
- https://www.kaggle.com/somnath796/indian-dance-form-recognition\
- Train Images: 364 | Test Images: 156\
+ https://www.kaggle.com/somnath796/indian-dance-form-recognition  
+ Train Images: 364 | Test Images: 156  
  Number of classes: 8
 ### Techniques used and their test accuracies:
 |Implementation |Test Accuracy  |
@@ -23,14 +23,14 @@ Implemented Cross Validation.
 ### Running the code:
 Python Notebooks along with the necessary code are provided in the repository, in the folder 'Notebooks/'.
 
- 1. Custom CNN notebook:\
+ 1. Custom CNN notebook:  
 Open the notebook, import the 'helpers' directory and run the cells.
- 2. VGG16:\
+ 2. VGG16:  
 Open the notebook, and run the cells.
- 3. ResNet152 using Monk:\
-i. You can directly download the ipynb file and run it on kaggle\
-ii. Upload dataset with appropriate data structure\
-iii. Download the uploaded .py file, according to the numbers given in the file, execute commands on Kaggle notebook.\
+ 3. ResNet152 using Monk:  
+i. You can directly download the ipynb file and run it on kaggle  
+ii. Upload dataset with appropriate data structure  
+iii. Download the uploaded .py file, according to the numbers given in the file, execute commands on Kaggle notebook.  
 
 ### References: