Worked on the ultralytics/ultralytics repository to address stability and memory efficiency challenges in large-scale prediction workflows. Focused on a critical bug fix, the developer implemented lazy loading for image and video file paths, replacing eager loading to prevent out-of-memory errors during batch inference. This approach preserved input order, supported mixed input types, and reduced memory spikes, enabling more reliable production deployments. Leveraging Python for data and image processing, the solution optimized data pipelines and batch processing while maintaining compatibility with existing input sources. The work demonstrated careful memory management and collaborative refactoring to enhance performance without introducing regressions.