Update README.md

README.md

@@ -44,6 +44,7 @@ After training, the model's performance was evaluated on the test dataset. The t
 
 - Test Accuracy: **80.83%**
 - Test Loss: **0.846**
+
 The test accuracy and loss are consistent with the validation metrics, indicating that the model generalizes well to unseen data. This performance demonstrates the effectiveness of the ResNet architecture for image classification tasks on the CIFAR-10 dataset.
 
 The model achieved competitive performance, achieving over 80% accuracy on both the validation and test sets. These results validate the effectiveness of the ResNet architecture and the training strategy employed in this project.