Gal_Co:

the accuracy on the validation slightly improves to 43% while the accuracy on the training set goes up to around 97


IMHO, this awnser really depends on your data and your loss curves (training and validation) and not solely on accuracy. These are the ones that will tell you better whether you will have or not overfitted your data.
Only watching accuracy is not great, since validation loss sometimes do not actually reflex on a great accuracy. For exemple, in imbalanced problems, your model can have an excelent accuracy by only learning the most present class of the dataset and rejecting others. Your val loss would be high, but the accuracy would also be “good”.
At the end of the day, I would choose the model that has the lowest validation loss.