We present GLEE in this work, an object-level foundation model for locatingand identifying objects in images and videos. Through a unified framework, GLEEaccomplishes detection, segmentation, tracking, grounding, and identificationof arbitrary objects in the open world scenario for various object perceptiontasks. Adopting a cohesive learning strategy, GLEE acquires knowledge fromdiverse data sources with varying supervision levels to formulate generalobject representations, excelling in zero-shot transfer to new data and tasks.Specifically, we employ an image encoder, text encoder, and visual prompter tohandle multi-modal inputs, enabling to simultaneously solve variousobject-centric downstream tasks while maintaining state-of-the-art performance.Demonstrated through extensive training on over five million images fromdiverse benchmarks, GLEE exhibits remarkable versatility and improvedgeneralization performance, efficiently tackling downstream tasks without theneed for task-specific adaptation. By integrating large volumes ofautomatically labeled data, we further enhance its zero-shot generalizationcapabilities. Additionally, GLEE is capable of being integrated into LargeLanguage Models, serving as a foundational model to provide universalobject-level information for multi-modal tasks. We hope that the versatilityand universality of our method will mark a significant step in the developmentof efficient visual foundation models for AGI systems. The model and code willbe released at https://glee-vision.github.io .