Split Folders

Split folders with files (e.g. images) into train, validation and test (dataset) folders.

The input folder shoud have the following format:

input/
    class1/
        img1.jpg
        img2.jpg
        ...
    class2/
        imgWhatever.jpg
        ...
    ...

In order to give you this:

output/
    train/
        class1/
            img1.jpg
            ...
        class2/
            imga.jpg
            ...
    val/
        class1/
            img2.jpg
            ...
        class2/
            imgb.jpg
            ...
    test/
        class1/
            img3.jpg
            ...
        class2/
            imgc.jpg
            ...

This should get you started to do some serious deep learning on your data. Read here why it's a good idea to split your data intro three different sets.

• You may only split into a training and validation set.
• The data gets split before it gets shuffled.
• A seed lets you reproduce the splits.
• Works on any file types.
• Allows randomized oversampling for imbalanced datasets.
• (Should) work on all operating systems.

Install

pip install split-folders

Usage

You you can use split_folders as Python module or as a Command Line Interface (CLI).

If your datasets is balanced (each class has the same number of samples), choose ratio otherwise fixed. NB: oversampling is turned off by default.

Module

import split_folders

# Split with a ratio.
# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.
split_folders.ratio('input_folder', output="output", seed=1337, ratio=(.8, .1, .1)) # default values

# Split val/test with a fixed number of items e.g. 100 for each set.
# To only split into training and validation set, use a single number to `fixed`, i.e., `10`.
split_folders.fixed('input_folder', output="output", seed=1337, fixed=(100, 100), oversample=False) # default values

CLI

Usage:
    split_folders folder_with_images [--output] [--ratio] [--fixed] [--seed] [--oversample]
Options:
    --output     path to the output folder. defaults to `output`. Get created if non-existent.
    --ratio      the ratio to split. e.g. for train/val/test `.8 .1 .1` or for train/val `.8 .2`.
    --fixed      set the absolute number of items per validation/test set. The remaining items constitute
                 the training set. e.g. for train/val/test `100 100` or for train/val `100`.
    --seed       set seed value for shuffling the items. defaults to 1337.
    --oversample enable oversampling of imbalanced datasets, works only with --fixed.
Example:
    split_folders imgs --ratio .8 .1 .1

License

MIT.