Creating dataset - imbalanced or balanced? I'm trying to make an image classification model and I have 5 classes - A, B, C, D, E. The goal is to get the highest possible classification accuracy.
I have a database of images and I'm selecting the number of images I will use for each class for my model. I'm trying to figure out how many images I should pick for each class if the distribution of available data is as shown below.
Should I randomly choose something like 8,000 images for each class, in order to avoid class imbalance? Or should I just use as many images per class?
Images available per class:

A - 100,000
B - 70,000
C - 40,000
D - 10,000
E - 8,000
The easiest option to close your approach is too just run the model twice in both variations. But in my experience this specific data is bound to be imbalanced.
Your idea to fix the class imbalance is technically known as under-sampling. This is one technique which is used. You can try out a few other techniques which are mentioned here: https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html