Tensorflow: loss decreasing, but accuracy stable

Question

My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience

Answer 1

Here are my suggestions, one of the possible problems is that your network start to memorize data, yes you should increase regularization,

update: Here I want to mention one more problem that may cause this: The balance ratio in the validation set is much far away from what you have in the training set. I would recommend, at first step try to understand what is your test data (real-world data, the one your model will face in inference time) descriptive look like, what is its balance ratio, and other similar characteristics. Then try to build such a train/validation set almost with the same descriptive you achieve for real data.