ImageDataSpliter 0.1.1

This utility splits an image dataset into train, validation, and test subsets while preserving the class folder structure.

Dataset Splitter

This utility splits an image dataset into train, validation, and test subsets while preserving the class folder structure.

Description

The split_dataset function takes a list of source folders, where each folder represents a class containing image files. The function randomly splits the images into training, validation, and testing sets and copies them into a new output directory.

The resulting directory structure looks like this:

output_folder/ train/ class_name/ val/ class_name/ test/ class_name/

The split is random but reproducible due to a fixed random seed.

Supported Image Formats

.jpg .jpeg .png .bmp .gif .tiff .webp

Usage

from dataset_splitter import split_dataset

source_folders = [ "data/class_1", "data/class_2" ]

output_folder = "dataset_split"

split_dataset( source_folders=source_folders, output_folder=output_folder, train_ratio=0.7, val_ratio=0.15, test_ratio=0.15, seed=42 )

Function Signature

split_dataset( source_folders: list[str], output_folder: str, train_ratio: float = 0.7, val_ratio: float = 0.15, test_ratio: float = 0.15, seed: int = 42 )

Parameters

source_folders List of paths to class directories containing images.

output_folder Path to the directory where the split dataset will be saved.

train_ratio Fraction of images used for training.

val_ratio Fraction of images used for validation.

test_ratio Fraction of images used for testing.

seed Random seed to ensure reproducible splits.

The sum of train_ratio, val_ratio, and test_ratio must equal 1.0.

Output

Image files are copied, not moved. Original data remains unchanged. Class folder names are preserved. Progress information is printed to the console.

Requirements

Python 3.8 or newer. Uses only Python standard library modules.