lmms-lab/Ferret-Bench
收藏Hugging Face2024-03-08 更新2024-06-22 收录
下载链接:
https://hf-mirror.com/datasets/lmms-lab/Ferret-Bench
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资源简介:
---
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: image
dtype: image
- name: image_name
dtype: string
- name: category
dtype: string
- name: context
dtype: string
- name: gpt_answer
dtype: string
splits:
- name: test
num_bytes: 19750932.0
num_examples: 120
download_size: 11713676
dataset_size: 19750932.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
<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 [FerretBench](https://github.com/apple/ml-ferret). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{you2023ferret,
title={Ferret: Refer and Ground Anything Anywhere at Any Granularity},
author={You, Haoxuan and Zhang, Haotian and Gan, Zhe and Du, Xianzhi and Zhang, Bowen and Wang, Zirui and Cao, Liangliang and Chang, Shih-Fu and Yang, Yinfei},
journal={arXiv preprint arXiv:2310.07704},
year={2023}
}
```
---
数据集信息:
特征:
- 名称:问题ID
数据类型:字符串
- 名称:问题
数据类型:字符串
- 名称:图像
数据类型:图像
- 名称:图像名称
数据类型:字符串
- 名称:类别
数据类型:字符串
- 名称:上下文
数据类型:字符串
- 名称:GPT生成回答
数据类型:字符串
划分集:
- 名称:测试集
字节数:19750932.0
样本数量:120
下载大小:11713676
数据集总大小:19750932.0
配置项:
- 配置名称:默认配置
数据文件:
- 划分集:测试集
路径:data/test-*
---
<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
🏠 [项目主页](https://lmms-lab.github.io/) | 📚 [文档](docs/README.md) | 🤗 [Huggingface数据集仓库](https://huggingface.co/lmms-lab)
# 本数据集
本数据集是[FerretBench](https://github.com/apple/ml-ferret)的格式化版本,可集成于我们的`lmms-eval`流程中,实现大规模多模态模型的一键评测。
@article{you2023ferret,
title={Ferret:任意粒度下实现任意对象的指称与定位},
author={You, Haoxuan and Zhang, Haotian and Gan, Zhe and Du, Xianzhi and Zhang, Bowen and Wang, Zirui and Cao, Liangliang and Chang, Shih-Fu and Yang, Yinfei},
journal={arXiv preprint arXiv:2310.07704},
year={2023}
}
提供机构:
lmms-lab原始信息汇总
数据集概述
数据集信息
特征
- question_id: 字符串类型
- question: 字符串类型
- image: 图像类型
- image_name: 字符串类型
- category: 字符串类型
- context: 字符串类型
- gpt_answer: 字符串类型
数据分割
- test: 包含120个样本,总大小为19750932字节
数据大小
- 下载大小: 11713676字节
- 数据集大小: 19750932字节
配置
- default: 包含测试数据文件,路径为
data/test-*
引用
@article{you2023ferret, title={Ferret: Refer and Ground Anything Anywhere at Any Granularity}, author={You, Haoxuan and Zhang, Haotian and Gan, Zhe and Du, Xianzhi and Zhang, Bowen and Wang, Zirui and Cao, Liangliang and Chang, Shih-Fu and Yang, Yinfei}, journal={arXiv preprint arXiv:2310.07704}, year={2023} }
搜集汇总
数据集介绍

以上内容由遇见数据集搜集并总结生成



