This is the official repo of the Griffon series (v1 & v2). Griffon is the first high-resolution (over 1K) LVLM capable of localizing everything you are interested in describing the region you specify. In the latest version, Griffon supports visual-language co-referring. You can input an image or some descriptions. Griffon achieves excellent performance in REC, object detection, object counting, visual/phrase grounding, and REG.
Griffon: Spelling out All Object Locations at Any Granuality with Large Language Model
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
Release Griffon-G in the next two weeks!
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2024.07.01🔥Griffon has been accepted to ECCV 2024. -
2024.03.15🔥Griffon v2's paper has been released in📕Arxiv. -
2024.03.11🔥We are excited to announce the arrival of Griffon v2. Griffion v2 brings fine-grained perception performance to new heights with high-resolution expert-level detection and counting and supports visual-language co-referring. Take a look at our demo first. Paper, codes, demos, and models will be released soon. -
2023.12.13🔥Ready to release the Language-prompted Localization Dataset after final approval in🤗HuggingFace. -
2023.12.06🔥Release the inference code and model in🤗HuggingFace. -
2023.11.29🔥Paper has been released in📕Arxiv.
Griffon v2 can perform localization with free-form text inputs and visual target inputs with locally cropped images now, supporting the tasks shown below. More quantitative evaluation results can be found in our paper.
- LLaVA provides the base codes and pre-trained models.
- Shikra provides insight of how to organize datasets and some base processed annotations.
- Llama provides the large language model.
- volgachen provides the basic environment setting config.
If you find Griffon useful for your research and applications, please cite using this BibTeX:
@inproceedings{zhan2025griffonv1,
title={Griffon: Spelling out all object locations at any granularity with large language models},
author={Zhan, Yufei and Zhu, Yousong and Chen, Zhiyang and Yang, Fan and Tang, Ming and Wang, Jinqiao},
booktitle={European Conference on Computer Vision},
pages={405--422},
year={2025},
organization={Springer}
}
@misc{zhan2024griffonv2,
title={Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring},
author={Yufei Zhan and Yousong Zhu and Hongyin Zhao and Fan Yang and Ming Tang and Jinqiao Wang},
year={2024},
eprint={2403.09333},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@article{zhan2024griffon-G,
title={Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models},
author={Zhan, Yufei and Zhao, Hongyin and Zhu, Yousong and Yang, Fan and Tang, Ming and Wang, Jinqiao},
journal={arXiv preprint arXiv:2410.16163},
year={2024}
}
The data and checkpoint is licensed for research use only. All of them are also restricted to uses that follow the license agreement of LLaVA, LLaMA and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.