
Chathura Chamikara contributed to the ultralytics/yolo-flutter-app repository by delivering two core features focused on mobile computer vision. He optimized the Android build process by removing unnecessary dependencies and introducing Proguard rules, which reduced the app size and improved build stability. Leveraging C++, Dart, and TensorFlow Lite, he implemented segmentation support by integrating a new C++ segmentation processing module with the existing TFLiteDetector and updating the CMake build system. This work enabled on-device image segmentation capabilities, laying the foundation for advanced computer vision workloads. The contributions demonstrated depth in build optimization and cross-platform mobile development within a short timeframe.

May 2025 performance snapshot: Delivered key platform enhancements for ultralytics/yolo-flutter-app, focusing on reducing app size, tightening dependencies, and enabling segmentation capabilities. Strengthened build stability, reduced binary footprint, and laid groundwork for on-device segmentation workloads.
May 2025 performance snapshot: Delivered key platform enhancements for ultralytics/yolo-flutter-app, focusing on reducing app size, tightening dependencies, and enabling segmentation capabilities. Strengthened build stability, reduced binary footprint, and laid groundwork for on-device segmentation workloads.
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