How many on-the-fly augmentations per image for a single-class segmentation mode [R]

I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above. We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in:

• Roll, pitch, yaw
• Camera distance
• Object coverage in the frame
• Centering and X/Y shift
• Orientation
• Perspective
• Lighting

The photos were taken with flagship iPhones. I want to use on-the-fly augmentation to simulate realistic human-hand variation and save our designer from adjusting each time to make it flat.

Augmentation Volume

Is 100 augmentation combinations per original be useful, or excessive?

Augmentation Strategy

Should the policy be: mostly isolated transforms, mostly crossover combinations such as orientation + roll + pitch + yaw + coverage + shift, or a controlled hybrid of both?

Training Goal

The goal is maximum segmentation accuracy, especially around the object boundary, not speed. I plan to train for around 300 epochs and keep validation and test images unaugmented.