Dataset Splitterv1.0.0

Split labeled medical images (e.g., X-rays, MRIs) into train, validation, and test sets for training diagnostic AI models. Ensures class distribution is maintained across splits to avoid bias in model evaluation.

Divide annotated road scene images into datasets for training perception systems. Supports YOLO format output to integrate with object detection pipelines, ensuring reproducible splits for consistent model testing.

Split product images with annotations into subsets for training machine learning models to identify items. Uses stratified splitting to maintain class ratios, crucial for handling imbalanced product categories.

Divide drone-captured field images into train, validation, and test sets for crop disease detection models. Supports custom ratios to allocate more data for training in resource-limited environments.

Offer the dataset splitter as a cloud-based service with API access, charging monthly fees based on dataset size and split frequency. Targets small to medium enterprises needing scalable data preprocessing.

Sell perpetual licenses to large organizations for on-premise deployment, including customization and support services. Focuses on industries like healthcare and automotive with strict data privacy requirements.

Provide a free basic version with limited features (e.g., random split only) and charge for advanced capabilities like stratified splitting and YOLO format support. Monetizes through upgrades and premium support.