Update README.md

README.md

@@ -33,5 +33,9 @@ The entire dataset consist of 3500 negative images and 700 positive TB images. <
 The data was splitted in 80% for training and 20% for validation.
 
 #Training procedure
-Transfer-learning was employed using InceptionV3 as the pre-trained model. The classes were weighted during training in order to neutralize the imbalanced class.
-Evaluation results
+Transfer-learning was employed using InceptionV3 as the pre-trained model. Training was done for 20 epochs and the classes were weighted during training in order to neutralize the imbalanced class in the dataset. The training was done on Kaggle using the GPUs provided. More details of the experiments can be found (here)[https://www.kaggle.com/code/abrahamowodunni/tb-project]
+
+#Evaluation results
+The result of the evaluation are as follows: - loss: 0.0923 - binary_accuracy: 0.9857 - precision: 0.9259 - recall: 0.9843
+More information can be found in the plot below.
+