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DeskFan9 t1_isl9qfe wrote

More info would help answser your question.

How big is your dataset? Small data sets could have this issue.

What accuracy are you getting?

Are the classes balanced overall, in the training set, and in the test set? Could be that network is biased towards certain classes and the test set happens to have more of those classes.

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redditnit21 OP t1_isn2qbq wrote

There are 9 Classes in the dataset labelled as 1 - 9. These are 7916 images in training and 1979 images in testing. All the classes are equally proportioned except 1 class which has more images.

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Training accuracy is around 96% and testing is around 98%.

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