GQA

<p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # 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 [GQA](hhttps://cs.stanford.edu/people/dorarad/gqa/about.html). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @inproceedings{hudson2019gqa, title={Gqa: A new dataset for real-world visual reasoning and compositional question answering}, author={Hudson, Drew A and Manning, Christopher D}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={6700--6709}, year={2019} } ```Dataset file metadata and data files are accessible by browsing the "Dataset Files" page. This dataset card uses the default template, and the dataset contributors have not provided more detailed descriptions of this dataset. However, you can download the dataset via the following Git Clone command or the ModelScope SDK: #### Download Methods :modelscope-code[]{type="sdk"} :modelscope-code[]{type="git"}

搜集汇总

背景与挑战

背景概述

该数据集是GQA的格式化版本，作为大规模多模态模型评估套件的一部分，用于通过'lmms-eval'工具一键评估大型多模态模型的性能。它基于原始GQA数据集，专注于真实世界视觉推理和组合问答任务，旨在加速多模态模型的开发。数据集具有Apache License 2.0许可证，大小为30.09GB，更新于2024年8月。