https://github.com/jfilter/split-folders
🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders
https://github.com/jfilter/split-folders
dataset deep-learning machine-learning oversampling python python-package splitting test training validation
Last synced: 7 days ago
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🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders
- Host: GitHub
- URL: https://github.com/jfilter/split-folders
- Owner: jfilter
- License: mit
- Created: 2018-10-04T12:33:35.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2023-03-08T05:05:34.000Z (about 2 years ago)
- Last Synced: 2025-04-01T14:51:48.944Z (14 days ago)
- Topics: dataset, deep-learning, machine-learning, oversampling, python, python-package, splitting, test, training, validation
- Language: Python
- Homepage:
- Size: 88.9 KB
- Stars: 421
- Watchers: 7
- Forks: 69
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-rainmana - jfilter/split-folders - 🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders (Python)
README
# `split-folders` [](https://github.com/jfilter/split-folders/actions/workflows/test.yml) [](https://pypi.org/project/split-folders/) [](https://pypi.org/project/split-folders/) [](https://pypistats.org/packages/split-folders)
Split folders with files (e.g. images) into **train**, **validation** and **test** (dataset) folders.
The input folder should 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.
- Split files into a training set and a validation set (and optionally a test set).
- Works on any file types.
- The files get shuffled.
- A [seed](https://docs.python.org/3/library/random.html#random.seed) makes splits reproducible.
- Allows randomized [oversampling](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis) for imbalanced datasets.
- Optionally group files by prefix.
- (Should) work on all operating systems.
## Install
This package is Python only and there are no external dependencies.
```bash
pip install split-folders
```
Optionally, you may install [tqdm](https://github.com/tqdm/tqdm) to get a progress bar when moving files.
```bash
pip install split-folders[full]
```
## Usage
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.
Oversampling is only applied to the _train_ folder since having duplicates in _val_ or _test_ would be considered cheating.
### Module
```python
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
```
Occasionally, you may have things that comprise more than a single file (e.g. picture (.png) + annotation (.txt)).
`splitfolders` lets you split files into equally-sized groups based on their prefix.
Set `group_prefix` to the length of the group (e.g. `2`).
But now _all_ files should be part of groups.
Set `move=True` if you want to move the files instead of copying.
### CLI
```
Usage:
splitfolders [--output] [--ratio] [--fixed] [--seed] [--oversample] [--group_prefix] [--move] folder_with_images
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`.
Set 3 values, e.g. `300 100 100`, to limit the number of training values.
--seed set seed value for shuffling the items. defaults to 1337.
--oversample enable oversampling of imbalanced datasets, works only with --fixed.
--group_prefix split files into equally-sized groups based on their prefix
--move move the files instead of copying
Example:
splitfolders --ratio .8 .1 .1 -- folder_with_images
```
Because of some [Python quirks](https://github.com/jfilter/split-folders/issues/19) you have to prepend ` --` after using `--ratio`.
Instead of the command `splitfolders` you can also use `split_folders` or `split-folders`.
## Development
Install and use [poetry](https://python-poetry.org/).
## Contributing
If you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/split-folders/issues).
**Pull requests** are especially welcomed when they fix bugs or improve the code quality.
## License
MIT