How to split folder of images into test/training/validation sets with stratified sampling?

Question

I have a very large folder of images, as well as a CSV file containing the class labels for each of those images. Because it's all in one giant folder, I'd like to split them up into training/test/validation sets; maybe create three new folders and move images into each based on a Python script of some kind. I'd like to do stratified sampling so I can keep the % of classes the same across all three sets.

What would be the approach to go about making a script that can do this?

Answer 1

Use the python library split-folder.

pip install split-folders

Let all the images be stored in Data folder. Then apply as follows:

import splitfolders
splitfolders.ratio('Data', output="output", seed=1337, ratio=(.8, 0.1,0.1)) 

On running the above code snippet, it will create 3 folders in the output directory:

• train
• val
• test

The number of images in each folder can be varied using the values in the ratio argument(train:val:test).