Simple tool to split a multi-label coco annotation dataset with preserving class distributions among train and test sets.
The code is an updated version from akarazniewicz/cocosplit original repo, where the functionality of splitting multi-class data while preserving distributions is added.
Installation
cocosplit requires python 3 and basic set of dependencies:
specifically, in addition to the requirements of the original repo, (scikit-multilearn) is required, it is included the requirements.txt file
pip install -r requirements
Usage
The same as the original repo, with adding an argument (--multi-class) to preserve class distributions
The argument is optional to ensure backward compatibility
$ python cocosplit.py -h
usage: cocosplit.py [-h] -s SPLIT [--having-annotations]
coco_annotations train test
Splits COCO annotations file into training and test sets.
positional arguments:
coco_annotations Path to COCO annotations file.
train Where to store COCO training annotations
test Where to store COCO test annotations
optional arguments:
-h, --help show this help message and exit
-s SPLIT A percentage of a split; a number in (0, 1)
--having-annotations Ignore all images without annotations. Keep only these
with at least one annotation
--multi-class Split a multi-class dataset while preserving class
distributions in train and test sets
Running
$ python cocosplit.py --having-annotations --multi-class -s 0.8 /path/to/your/coco_annotations.json train.json test.json
will split coco_annotation.json into train.json and test.json with ratio 80%/20% respectively. It will skip all
images (--having-annotations) without annotations.