  Grounding-DINO is a state-of-the-art open-set detection model that tackles
multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase
Grounding (PG), and Referring Expression Comprehension (REC). Its effectiveness
has led to its widespread adoption as a mainstream architecture for various
downstream applications. However, despite its significance, the original
Grounding-DINO model lacks comprehensive public technical details due to the
unavailability of its training code. To bridge this gap, we present
MM-Grounding-DINO, an open-source, comprehensive, and user-friendly baseline,
which is built with the MMDetection toolbox. It adopts abundant vision datasets
for pre-training and various detection and grounding datasets for fine-tuning.
We give a comprehensive analysis of each reported result and detailed settings
for reproduction. The extensive experiments on the benchmarks mentioned
demonstrate that our MM-Grounding-DINO-Tiny outperforms the Grounding-DINO-Tiny
baseline. We release all our models to the research community. Codes and
trained models are released at
https://github.com/open-mmlab/mmdetection/tree/main/configs/mm_grounding_dino.
