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 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 makes splits reproducible.
- Allows randomized oversampling for imbalanced datasets.
- Optionally group files by prefix.
- (Should) work on all operating systems.
This package is Python only and there are no external dependencies.
pip install split-foldersOptionally, you may install tqdm to get a progress bar when moving files.
pip install split-folders[full]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.
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 valuesOccasionally, 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.
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 you have to prepend -- after using --ratio.
Instead of the command splitfolders you can also use split_folders or split-folders.
Install and use poetry.
If you have a question, found a bug or want to propose a new feature, have a look at the issues page.
Pull requests are especially welcomed when they fix bugs or improve the code quality.
MIT