Update README.md

As per the feedback, I have tried to update README file

Author: OjasChaudhari23

recognition/s4694189_UNET/README.md

@@ -18,14 +18,16 @@ Following diagram exactly depicts the architecture
 
 The neural network starts with giving an input layer of 256*256*3. The actual size of the images are 128*128. The images are then resized, normalized and given as array to the network. The neural network starts with contraction at start. It starts with 16 layers and increases in each step. Each step consists of two equal features. After it reaches to 256, Expansion starts. In this project Transposecv has been used for the expansion. In this code I have used dropout(0.1) as it was giving me a good result but it can be changed.
 
+The program starts with importing the data and making the appropriate pre-processing. The Unet structure takes the input in size 256*256*3. In downsampling part, the depth increases to 256 while in upsampling part, the model outputs the image in the same dimension as that of input. Maxpooling is used after each depth to half the size of image in downsampling part. The model has been trained on the whole training data, it is then validated with validation data and the results are observed on test data. Training data has not been split into train-validation-test data.  
+
 <h2>Running the program: </h2>
 Please download the modules.ipynb file to run the program or alternatively download modules.py, dataset.py, train.py and predict.py files and run train and predict files. The modules.ipynb has all the result data with the merged code and all other files have been created according to their functionality.
 
 <h2> File structure </h2>
-* <b> dataset.py </b>:- This file loads the dataset and give the x and y array values with the pre-processing of images.
-* <b> module.py </b>:- This file has UNET neural network defined in it and it returns the model
-* <b> train.py </b> :- This file trains the model and plots the result. The model uses dice similarity measure to evaluate the model's performance.
-* <b> predict.py </b> :- This file evaluates the performance on test data.
+ * <b> dataset.py </b>:- This file loads the dataset and give the x and y array values with the pre-processing of images.
+ * <b> module.py </b>:- This file has UNET neural network defined in it and it returns the model
+ * <b> train.py </b> :- This file trains the model and plots the result. The model uses dice similarity measure to evaluate the model's performance.
+ * <b> predict.py </b> :- This file evaluates the performance on test data.
 
 <h2> Plots and visualizations</h2>