# MM Grounding DINO
> [An Open and Comprehensive Pipeline for Unified Object Grounding and Detection](https://arxiv.org/abs/2401.02361)
<!-- [ALGORITHM] -->
## Abstract
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.
<div align=center>
<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/4214e282-a553-4abf-b8a4-84ea566851c9"/>
</div>
<div align=center>
<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/fb14d1ee-5469-44d2-b865-aac9850c429c"/>
</div>
## Dataset Preparation
Please refer to [dataset_prepare.md](dataset_prepare.md) or [ä¸æçæ°æ®åå¤](dataset_prepare_zh-CN.md)
## ⨠What's New
ð **We have released the pre-trained weights for Swin-B and Swin-L, welcome to try and give feedback.**
## Usage
Please refer to [usage.md](usage.md) or [ä¸æçç¨æ³è¯´æ](usage_zh-CN.md)
## Zero-Shot COCO Results and Models
| Model | Backbone | Style | COCO mAP | Pre-Train Data | Config | Download |
| :----------: | :------: | :-------: | :--------: | :----------------------: | :------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| GDINO-T | Swin-T | Zero-shot | 46.7 | O365 | | |
| GDINO-T | Swin-T | Zero-shot | 48.1 | O365,GoldG | | |
| GDINO-T | Swin-T | Zero-shot | 48.4 | O365,GoldG,Cap4M | [config](../grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth) |
| MM-GDINO-T | Swin-T | Zero-shot | 48.5(+1.8) | O365 | [config](grounding_dino_swin-t_pretrain_obj365.py) | |
| MM-GDINO-T | Swin-T | Zero-shot | 50.4(+2.3) | O365,GoldG | [config](grounding_dino_swin-t_pretrain_obj365_goldg.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602-4ea751ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602.log.json) |
| MM-GDINO-T | Swin-T | Zero-shot | 50.5(+2.1) | O365,GoldG,GRIT | [config](grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818-169cc352.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818.log.json) |
| MM-GDINO-T | Swin-T | Zero-shot | 50.6(+2.2) | O365,GoldG,V3Det | [config](grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741-e316e297.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741.log.json) |
| MM-GDINO-T | Swin-T | Zero-shot | 50.4(+2.0) | O365,GoldG,GRIT,V3Det | [config](grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047.log.json) |
| MM-GDINO-B | Swin-B | Zero-shot | 52.5 | O365,GoldG,V3Det | [config](grounding_dino_swin-b_pret
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使用mmdetection训练目标检测算法-支持fasterrcnn+retinanet等算法-项目源码 (2000个子文件)
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