YoloDataLabeler V1.0.2

🎉 Release Notes for V1.0.2 🎉

🏗 Architectural Overhaul

• MVP Transition: We've elevated the architecture of our application by transitioning to the robust Model-View-Presenter (MVP) structure. This shift ensures a more modular, maintainable, and testable codebase, setting a solid foundation for future features and improvements.

📊 Enhanced Data Labeling Capabilities

• Stratified Data Splitting: With an aim to enhance accuracy in data labeling, we've incorporated the stratify feature from scikit-learn's train_test_split. This ensures that the resulting splits have a consistent representation of target labels, leading to better and more reliable labeling outcomes.

🐞 Quality & Maintenance

• Bug Exterminations: We've identified and addressed several bugs from previous releases, ensuring a smoother and more reliable user experience.

🛠 Under The Hood

• Built on PyQt5: Our commitment to delivering a seamless UI experience continues with the integration of PyQt5, offering users an intuitive and efficient data labeling journey.