Metadata-Version: 2.1
Name: split-folders
Version: 0.2.0
Summary: 🗂 Split folders with files (e.g. images) into training, validation and test (dataset) folders.
Home-page: https://github.com/jfilter/split-folders
Author: Johannes Filter
Author-email: hi@jfilter.de
License: MIT
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown

# Split Folders [![Build Status](https://travis-ci.com/jfilter/split-folders.svg?branch=master)](https://travis-ci.com/jfilter/split-folders) [![PyPI](https://img.shields.io/pypi/v/split-folders.svg)](https://pypi.org/project/split-folders/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/split-folders.svg)](https://pypi.org/project/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](https://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set) 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](https://docs.python.org/3/library/random.html#random.seed) lets you reproduce the splits.
-   Works on any file types.
-   Allows randomized [oversampling](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis) for imbalanced datasets.
-   (Should) work on all operating systems.

## Install

```bash
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

```python
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.


