How should I interpret or intuitively explain the following results for my CNN model?
The model is reaching 100% training accuracy when validation accuracy is still only 25%. A 75% gap between training and validation accuracy is enormous and indicates the model is overfitting to the training data, likely due to the small size of the training data set (2000). Data augmentation would likely significantly reduce overfitting and improve validation accuracy - I would start with random cropping, brightness, and saturation. Collecting more training data with varied backgrounds, orientations, and lighting conditions would also help.
I am training a CNN model that has to classify 4 objects. 3 mugs (white, black, blue) and 1 glass. When I train my model for only 10 epochs, I get a validation accuracy of 25%, where everything is being labeled as the white mug. However when I would train the same model for longer, it eventually diverts from this 25% accuracy and climbs up to 80%, the only problem it has, is classifying the white mug.
In other words, if I am able to find why my classifier classifies the white mug wrongly, then I could potentially reach validation_accuracy of 90%. My question thus is, what are some things I could try to find out why it mispredicts, or things to improve. I have already used LIME to check why my model classifies something, but I could not get any wiser from it.
Some specs of the model:

No data augmentation
5 convolutional layers (32, 64, 128, 256, 512) -> into GlobalMaxPooling, Flatten, and 2 dense layers (128, 4)
Activation layers (relu)
2000 training images, 1000 validation images (classes are balanced)

Extra: The model gets 100% accuracy on the training data after 2 epochs, and slowly climbs up to 80% on the validation data (after about 40-50 epochs).
Extra 2: Sometimes the model gets 80%, sometimes only 74%