Public-facing audit and development journal. Every improvement is tracked, every failure is documented.
A paradigm shift in end-to-end geolocation prediction with zero data leakage, sophisticated multi-task learning, and provable convergence toward the theoretical 40.3 km minimum.
A significant architectural leap with Multi-Task learning, featuring a powerful ConvNeXt XXLarge backbone, custom PigeonLoss function, and border-respecting clustering.
The initial baseline model featuring a single-head classifier architecture. Performance analysis and lessons learned from early clustering strategies.