An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910287718776832 |
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| author | Zhao, Xiangyu Chen, Yicheng Xu, Shilin Li, Xiangtai Wang, Xinjiang Li, Yining Huang, Haian |
| author_facet | Zhao, Xiangyu Chen, Yicheng Xu, Shilin Li, Xiangtai Wang, Xinjiang Li, Yining Huang, Haian |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_02361 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Open and Comprehensive Pipeline for Unified Object Grounding and Detection Zhao, Xiangyu Chen, Yicheng Xu, Shilin Li, Xiangtai Wang, Xinjiang Li, Yining Huang, Haian Computer Vision and Pattern Recognition 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. |
| title | An Open and Comprehensive Pipeline for Unified Object Grounding and Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.02361 |