I am implementing a CNN to do image classification of 4 classes representing different weathers : Haze, Sunny, Rainy, Snowy. I have as training set 3200 images, and as test set 3038 images.
The problem is that the test and training set are different set of pictures(so to be clear, I didn't split the dataset, but I have been given a training set and a test set in different folders) and they have so a different distribution one with respect to the other.
After training and evaluating, I have that my accuracy on the test set is 0.475313 and the loss always on the test set is 3.060471.
Also if I plot the hihstory of accuracy and of the loss I have the following:
what I don't understand is if I have the accuracy so low because I am overfitting, or because the distribution is different. And also, how do I interpret the plots I have posted about the histories of loss and accuracy? Is it normal that the gap between test and train accuracy is so large?
Thanks in advance.
[EDIT] By searching online, I have found that to cope with this kind of problems, it is possible to use domain adaptation, but honestly I have no idea how to implement it because I have just read about it.