  We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of
understanding spatial referring of any shape or granularity within an image and
accurately grounding open-vocabulary descriptions. To unify referring and
grounding in the LLM paradigm, Ferret employs a novel and powerful hybrid
region representation that integrates discrete coordinates and continuous
features jointly to represent a region in the image. To extract the continuous
features of versatile regions, we propose a spatial-aware visual sampler, adept
at handling varying sparsity across different shapes. Consequently, Ferret can
accept diverse region inputs, such as points, bounding boxes, and free-form
shapes. To bolster the desired capability of Ferret, we curate GRIT, a
comprehensive refer-and-ground instruction tuning dataset including 1.1M
samples that contain rich hierarchical spatial knowledge, with 95K hard
negative data to promote model robustness. The resulting model not only
achieves superior performance in classical referring and grounding tasks, but
also greatly outperforms existing MLLMs in region-based and
localization-demanded multimodal chatting. Our evaluations also reveal a
significantly improved capability of describing image details and a remarkable
alleviation in object hallucination. Code and data will be available at
https://github.com/apple/ml-ferret
