Same validation accuracy, different train accuracy for two neural networks models I'm performing emotion classification over FER2013 dataset. I'm trying to measure different models performance, and when I checked ImageDataGenerator with a model I had already used I came up with the following situation:

Model without data augmentation got:


train_accuracy = 0.76
val_accuracy = 0.70


Model with data augmentation got:


train_accuracy = 0.86
val_accuracy = 0.70


As you can see, validation accuracy is the same in both models, but train accuracy is significantly different. In this case:


Should I go with the model which uses data augmentation, as it's
train accuracy is higher?
Should I expect overfitting from it, and choose the model without
data augmentation as it's accuracy values are closer?
Third option. Should I perform more comprobations? If so, which ones?


Thanks for your time.
Based on this, both models generalize equally well. However both have overfit, the second one more significantly. You simply want to avoid that. For example, I'd expect the best validation loss to be closer to training loss if you're using early stopping. You can turn up things like dropout.

If you do so, I think you'll find the augmented model ends up producing a better model (lower validation loss).