The package works for the task.
First, install the split-folders package.
pip install split-folders
The original folder should be organized as follows:
input/ class1/ img1.jpg img2.jpg ... class2/ imgWhatever.jpg ... ...
import splitfolders
# Split with a ratio.
# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.
splitfolders.ratio("input_folder", output="output",
seed=1337, ratio=(.8, .1, .1), group_prefix=None, move=False) # default values
# Split val/test with a fixed number of items, e.g. `(100, 100)`, for each set.
# To only split into training and validation set, use a single number to `fixed`, i.e., `10`.
# Set 3 values, e.g. `(300, 100, 100)`, to limit the number of training values.
splitfolders.fixed("input_folder", output="output",
seed=1337, fixed=(100, 100), oversample=False, group_prefix=None, move=False) # default values
The output structure will be organized as follows:
output/ train/ class1/ img1.jpg ... class2/ imga.jpg ... val/ class1/ img2.jpg ... class2/ imgb.jpg ... test/ class1/ img3.jpg ... class2/ imgc.jpg ...
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