I have 600 images in the training folder, 200 images in the validation folder, and 200 images in the test folder. Suppose if I fit the training data generator and validation data generator for some epochs for learning purposes -
model.fit(train,val), and after that, I add both the training and validation data like 600 + 200 = 800 and for those 800 images I fit the new test data set which consists of 200 images and find the accuracy for this. Is this good practice to get a better model performance?
I am new to deep learning, your answers will be very much helpful to gain some insights about the splitting of data.