oplex/POPE
收藏Hugging Face2026-03-29 更新2026-04-12 收录
下载链接：
https://hf-mirror.com/datasets/oplex/POPE
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资源简介：
---
dataset_info:
- config_name: Full
features:
- name: id
dtype: string
- name: question_id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: image_source
dtype: string
- name: image
dtype: image
- name: category
dtype: string
splits:
- name: adversarial
num_bytes: 490408158.0
num_examples: 3000
- name: popular
num_bytes: 490397000.0
num_examples: 3000
- name: random
num_bytes: 490394976.0
num_examples: 3000
download_size: 255022914
dataset_size: 1471200134.0
- config_name: default
features:
- name: id
dtype: string
- name: question_id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: image_source
dtype: string
- name: image
dtype: image
- name: category
dtype: string
splits:
- name: test
num_bytes: 1471200135.0
num_examples: 9000
download_size: 255022914
dataset_size: 1471200135.0
configs:
- config_name: Full
data_files:
- split: adversarial
path: Full/adversarial-*
- split: popular
path: Full/popular-*
- split: random
path: Full/random-*
- 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 [POPE](https://github.com/RUCAIBox/POPE). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{li2023evaluating,
title={Evaluating object hallucination in large vision-language models},
author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2305.10355},
year={2023}
}
```
提供机构：
oplex
