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