This is a computer vision research project that trains a unified object detector across multiple large-scale datasets. The detector uses Cascade-RCNN architecture with backbones like ResNeSt200 to achieve state-of-the-art performance on detection benchmarks. The key innovation is learning a unified label space that enables zero-shot transfer to novel datasets outside the training domain.