RefCOCOplus

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<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
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# Large-scale Multi-modality Models Evaluation Suite

> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`

🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)

# This Dataset

This is a formatted version of [RefCOCO+](https://github.com/lichengunc/refer). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.

```
@inproceedings{kazemzadeh-etal-2014-referitgame,
title = "{R}efer{I}t{G}ame: Referring to Objects in Photographs of Natural Scenes",
author = "Kazemzadeh, Sahar and
Ordonez, Vicente and
Matten, Mark and
Berg, Tamara",
editor = "Moschitti, Alessandro and
Pang, Bo and
Daelemans, Walter",
booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
month = oct,
year = "2014",
address = "Doha, Qatar",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D14-1086",
doi = "10.3115/v1/D14-1086",
pages = "787--798",
}
```

<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>

# 大规模多模态模型评测套件

> 借助`lmms-eval`加速大规模多模态模型（Large-scale Multi-modality Models, LMMs）的研发

🏠 [主页](https://lmms-lab.github.io/) | 📚 [文档](docs/README.md) | 🤗 [Huggingface 数据集](https://huggingface.co/lmms-lab)

# 本数据集

本数据集是[RefCOCO+](https://github.com/lichengunc/refer)的格式化版本，被集成于我们的`lmms-eval`评测管线中，可实现大规模多模态模型的一键式评测。

@inproceedings{kazemzadeh-etal-2014-referitgame,
title = "{R}efer{I}t{G}ame: 指代自然场景照片中的物体",
author = "Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara",
editor = "Moschitti, Alessandro and Pang, Bo and Daelemans, Walter",
booktitle = "2014年自然语言处理经验方法会议（EMNLP）论文集",
month = "10月",
year = "2014",
address = "卡塔尔多哈",
publisher = "国际计算语言学协会",
url = "https://aclanthology.org/D14-1086",
doi = "10.3115/v1/D14-1086",
pages = "787--798",
}