How should I interpret or intuitively explain the following results for my CNN model?
There could be multiple issues here. It looks like your network is overfitting, so first off you may want to add regularization or dropout to your training process. Secondly, you'll also want to make sure your images come from the same source, i.e. if your training/test examples all come from Google images or have vastly different qualities, angles, colors, etc, this might harm the network's ability to classify the mug correctly.
Finally, you might want to add a softmax layer at the end of your network since you're doing a multiclass classification. By doing this, you'll be able to see what the probability is for the white mug compared to the other objects, which can help you with debugging.
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%