On the left you can see the plots and a table of accuracy per class for the CNN model before data augmentation and on the right same information for CNN after data augmentation. As shown in the left table, the CNN model is performing poorly on detecting the disgust expression as it’s only detecting 2 out of 177 images. We also can see a lot of fluctuations in the test loss and test accuracy plots. On the other hand, after augmenting the disgust class, we observed that training loss drops faster than before and we experience less fluctuation in the test loss and test accuracy plots. In the paper, they mention that they were able to get a 5-10% accuracy on the whole dataset after augmentation. While we were not able to replicate these results since overall accuracy stayed almost the same after augmentation, the accuracy in disgust class improved by 10 percent.