# How many augmentated data points for each training image?

What are some useful rules of thumb for picking the number of augmenters per training image?

I realize this is a hyperparameter I can vary and test: I'm just trying to get a sense for reasonable ranges/choices based on people's experience with different network architecture and other particulars.

My particulars are that I have a relatively simple object detection task, with about 1100 annotated images, a single category. I am doing the transfer learning on a faster-rcnn with resnet-101 backbone.

I do an 80/20 train/test split, and even when I augment just 5x for each training image [h/v flips, small rotations, brightness changes, gaussian blur, motion blur, scale], the network is learning fairly well given all my other parameters.

For my final step I am planning to just add more augmented images per training image. I had always though 100 was a good number but I now realizes this is sort of arbitrary, and I picked it up just from some random example I used once from the internet (!). Even with 10 augmenters per image, I would have over 8k training images for the category, which should be enough, no?

Note a few people have asked about how many images there should be per class for training, but they don't take augmentation into account.

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