Dataset Splitter

Split image datasets into train, validation, and test sets with options for random or stratified splits, custom ratios, and annotation support.

Split image datasets into train/val/test sets. Supports random split, stratified split, and custom ratios. Use when user needs to split dataset for machine learning training.

Features

• Random Split: Randomly shuffle and split
• Stratified Split: Maintain class distribution
• Custom Ratios: Configurable train/val/test ratios
• Annotation Support: Split images and corresponding annotations together
• YOLO Format: Generate YOLO format dataset structure
• Reproducible: Set random seed for reproducibility

Usage

# Simple split (80/10/10)
python scripts/splitter.py split /path/to/images/ --ratios 80 10 10

# With annotations
python scripts/splitter.py split /path/to/images/ --annotations /path/to/labels/

# YOLO format output
python scripts/splitter.py split /path/to/images/ --output /path/to/dataset/ --yolo

# Stratified by class
python scripts/splitter.py split /path/to/images/ --annotations labels/ --stratify

Examples

$ python scripts/splitter.py split ./images --ratios 80 10 10

Splitting dataset...
Total images: 1000
Train: 800 (80%)
Val: 100 (10%)
Test: 100 (10%)

✓ Created train/ (800 images)
✓ Created val/ (100 images)
✓ Created test/ (100 images)

Installation

pip install pillow

Options

• --ratios: Split ratios (train val test), default: 80 10 10
• --seed: Random seed for reproducibility
• --annotations: Path to annotations (will be split together)
• --output: Output directory
• --yolo: Output in YOLO dataset format
• --stratify: Maintain class distribution
• --copy: Copy files instead of moving