{
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        "author": "Johannes Filter",
        "author_email": "hi@jfilter.de",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Utilities"
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        "description": "# 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/)\n\nSplit folders with files (e.g. images) into train, validation and test (dataset) folders.\n\nThe input folder shoud have the following format:\n\n```\ninput/\n    class1/\n        img1.jpg\n        img2.jpg\n        ...\n    class2/\n        imgWhatever.jpg\n        ...\n    ...\n```\n\nIn order to give you this:\n\n```\noutput/\n    train/\n        class1/\n            img1.jpg\n            ...\n        class2/\n            imga.jpg\n            ...\n    val/\n        class1/\n            img2.jpg\n            ...\n        class2/\n            imgb.jpg\n            ...\n    test/\n        class1/\n            img3.jpg\n            ...\n        class2/\n            imgc.jpg\n            ...\n```\n\nThis 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.\n\n-   You may only split into a training and validation set.\n-   The data gets split before it gets shuffled.\n-   A [seed](https://docs.python.org/3/library/random.html#random.seed) lets you reproduce the splits.\n-   Works on any file types.\n-   Allows randomized [oversampling](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis) for imbalanced datasets.\n-   (Should) work on all operating systems.\n\n## Install\n\n```bash\npip install split-folders\n```\n\nIf you are working with a large amount of files, you may want to get a progress bar. Install [tqdm](https://github.com/tqdm/tqdm) in order to get updates when copying the files into the new folders.\n\n```bash\npip install split-folders tqdm\n```\n\n## Usage\n\nYou you can use `split_folders` as Python module or as a Command Line Interface (CLI).\n\nIf your datasets is balanced (each class has the same number of samples), choose `ratio` otherwise `fixed`. NB: oversampling is turned off by default.\n\n### Module\n\n```python\nimport split_folders\n\n# Split with a ratio.\n# To only split into training and validation set, set a tuple to `ratio`, i.e, `(.8, .2)`.\nsplit_folders.ratio('input_folder', output=\"output\", seed=1337, ratio=(.8, .1, .1)) # default values\n\n# Split val/test with a fixed number of items e.g. 100 for each set.\n# To only split into training and validation set, use a single number to `fixed`, i.e., `10`.\nsplit_folders.fixed('input_folder', output=\"output\", seed=1337, fixed=(100, 100), oversample=False) # default values\n```\n\n### CLI\n\n```\nUsage:\n    split_folders folder_with_images [--output] [--ratio] [--fixed] [--seed] [--oversample]\nOptions:\n    --output     path to the output folder. defaults to `output`. Get created if non-existent.\n    --ratio      the ratio to split. e.g. for train/val/test `.8 .1 .1` or for train/val `.8 .2`.\n    --fixed      set the absolute number of items per validation/test set. The remaining items constitute\n                 the training set. e.g. for train/val/test `100 100` or for train/val `100`.\n    --seed       set seed value for shuffling the items. defaults to 1337.\n    --oversample enable oversampling of imbalanced datasets, works only with --fixed.\nExample:\n    split_folders imgs --ratio .8 .1 .1\n```\n\n## License\n\nMIT.\n\n\n",
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