Update README.md

README.md

@@ -59,11 +59,11 @@ Now we will determine what is the baseline without referencing the dataset but s
 #### [4.1 Underfit, Overfit and Regularizing the Model]
 To create the optimal model we need to perform underfitting and overfitting and lastly. After creating a model that overfits, we need to regularize the model as to discourage the complexity of the model and avoid overfitting in the future so as to achieve a perfect balance for the model.
 
-#### [4.2 Final Model]
+#### [4.2 Final Model - (Confusion Matrix Test)]
 Hopefully, we have created a model of good-fit, now we will perform training on the complete set instead of the split set. The `confusion matrix` below should be a good indicator of the performance of the model(Predicted vs Truth)
 
 
-<img src="https://github.com/RoninSanta/Machine_Learning-Classical_JAP_Literature_Text_Recognition/assets/109457795/07360837-087f-4a34-a14a-61b4992b8d6a" width="450" height="450">
+<img src="https://github.com/RoninSanta/Machine_Learning-Classical_JAP_Literature_Text_Recognition/assets/109457795/07360837-087f-4a34-a14a-61b4992b8d6a" width="500" height="500">
 
 ## [5. Iterative K-Fold]
 Normally, the most common method used is Hold-Out validation however this method is highly dependent on data points within training and testing sets, which in turn is highly reliant on the splitting of dataset into training and test sets.