lmms-lab/COCO-Caption2017

---
dataset_info:
features:
- name: question_id
dtype: string
- name: image
dtype: image
- name: question
dtype: string
- name: answer
sequence: string
- name: id
dtype: int64
- name: license
dtype: int8
- name: file_name
dtype: string
- name: coco_url
dtype: string
- name: height
dtype: int32
- name: width
dtype: int32
- name: date_captured
dtype: string
splits:
- name: val
num_bytes: 788752747.0
num_examples: 5000
- name: test
num_bytes: 6649116198.0
num_examples: 40670
download_size: 7444321699
dataset_size: 7437868945.0
configs:
- config_name: default
data_files:
- split: val
path: data/val-*
- split: test
path: data/test-*
---

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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 [COCO-Caption-2017-version](https://cocodataset.org/#home). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.

```
@misc{lin2015microsoft,
title={Microsoft COCO: Common Objects in Context},
author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
year={2015},
eprint={1405.0312},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

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
• image: 图像类型
• question: 字符串类型
• answer: 字符串序列
• id: 64位整数类型
• license: 8位整数类型
• file_name: 字符串类型
• coco_url: 字符串类型
• height: 32位整数类型
• width: 32位整数类型
• date_captured: 字符串类型

数据分割

• val:
  • 字节数: 788752747.0
  • 样本数: 5000
• test:
  • 字节数: 6649116198.0
  • 样本数: 40670

数据大小

• 下载大小: 7444321699
• 数据集大小: 7437868945.0

配置

• default:
  • val: data/val-*
  • test: data/test-*

数据集来源

• 该数据集是COCO-Caption-2017-version的格式化版本，用于lmms-eval流水线中，以便一键评估大型多模态模型。

搜集汇总

背景与挑战

背景概述

This dataset is a curated collection of image-caption pairs from COCO-Caption-2017, optimized for evaluating multi-modality models. It includes diverse images each paired with multiple descriptive captions, facilitating comprehensive model assessment in image understanding and caption generation tasks.