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An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

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arxiv 2401.02361 v2 pith:YEMGA3H7 submitted 2024-01-04 cs.CV

An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Xiangyu Zhao , Yicheng Chen , Shilin Xu , Xiangtai Li , Xinjiang Wang , Yining Li , Haian Huang This is my paper
classification cs.CV
keywords comprehensivedetectiongroundingbaselinedatasetsgrounding-dinommdetectionmodel
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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