---
title: JourneyDB
canonical_url: "https://www.modelscope.cn/datasets/AI-ModelScope/JourneyDB"
md_url: "https://www.modelscope.cn/datasets/AI-ModelScope/JourneyDB.md"
repository: AI-ModelScope/JourneyDB
chinese_name: JourneyDB
last_updated: 2025-11-26
license: "Apache License 2.0"
storage_size: "3.0 TB"
downloads: 8771
stars: 2
task_categories:
  - image-to-text
language:
  - en
size_categories:
  - "1M<n<10M"
---

# JourneyDB

> JourneyDB - AI-ModelScope 在 ModelScope 开源的数据集。[[Project Page]](https://journeydb.github.io) [[Paper]](https://arxiv.org/abs/2307.00716) [[Code]](https://github.com/JourneyDB/JourneyDB) [[HuggingFace]](https://huggingface.co/datasets/JourneyDB/JourneyDB)…

AI-ModelScope/JourneyDB 是 ModelScope 魔搭社区上的数据集，存储大小 3.0 TB，采用 Apache License 2.0 许可。

- **Repository**: AI-ModelScope/JourneyDB
- **License**: Apache License 2.0
- **Storage size**: 3.0 TB
- **Downloads**: 8771
- **Stars**: 2
- **Last updated**: 2025-11-26

Source: https://www.modelscope.cn/datasets/AI-ModelScope/JourneyDB

---

# JourneyDB

[[Project Page]](https://journeydb.github.io) [[Paper]](https://arxiv.org/abs/2307.00716) [[Code]](https://github.com/JourneyDB/JourneyDB) [[HuggingFace]](https://huggingface.co/datasets/JourneyDB/JourneyDB) [[OpenDataLab]]()

![image](./assets/jdb_teaser_small.jpg)

## Dataset Description

### Summary

**JourneyDB** is a large-scale generated image understanding dataset that contains **4,429,295** high-resolution Midjourney images, annotated with corresponding **text prompt**, **image caption** and **visual question answering**.

### Supported Tasks

**JourneyDB** supports **4** downstream tasks, i.e. **Prompt Inversion**, **Style Retrieval**, **Image Caption**, and **Visual Question Answering**. We evaluate many existing methods on these tasks and provide a comprehensive benchmark. Please see our [Paper](https://arxiv.org/abs/2307.00716) for more details.

## Dataset Details

### Data Collection
For each image instance, we acquire the corresponding text prompts used to generate the images with Midjourney. Furthermore, we employ GPT3.5 to generate the caption and VAQ groundtruth.
![image](./assets/jdb_data_collection.jpg)

### Data Instances

We provide several examples to show the contents of each dataset instance. 
![image](./assets/jdb_samples_small.jpeg)

### Data Splits
We provide detailed statistics for each split subset in the following table. We randomly split the whole dataset into roughly 20 : 1 to obtain the training and validation set. The training set contains 4,189,737 labeled images and 1,385,317 labeled prompts. The validation set contains 235,156 images and 82,093 prompts. And we additionally sample a testing set for manual filtering. The testing set contains 5,402 images and 5,171 prompts.

|                |   Image   |   Prompt  | Labeled Image | Labeled Prompt |  Style QA | Content QA |
|----------------|:---------:|:---------:|:-------------:|:--------------:|:---------:|:----------:|
| Training Set   | 4,453,193 | 1,643,375 |   4,189,737   |    1,385,317   | 7,056,394 |  8,775,971 |
| Validation Set |  234,156  |   82,093  |    234,156    |     82,093     |  311,569  |   374,310  |
| Testing Set    |   5,402   |   5,171   |     5,402     |      5,171     |   10,040  |   11,369   |
| Total          | 4,692,751 | 1,730,639 |   4,429,295   |    1,472,581   | 7,378,003 |  9,161,650 |

## Acquirements

### License

The JourneyDB dataset is available under the customised [Terms of Usage](./assets/Terms_of_Usage.md).

### Citation

```
@article{sun2023journeydb,
  title={Journeydb: A benchmark for generative image understanding},
  author={Sun, Keqiang and Pan, Junting and Ge, Yuying and Li, Hao and Duan, Haodong and Wu, Xiaoshi and Zhang, Renrui and Zhou, Aojun and Qin, Zipeng and Wang, Yi and others},
  journal={Advances in neural information processing systems},
  volume={36},
  pages={49659--49678},
  year={2023}
}
```

### Contributions

[Junting Pan](https://junting.github.io)\*, [Keqiang Sun](https://keqiangsun.github.io)\*, [Yuying Ge](https://geyuying.github.io), [Hao Li](https://cpsxhao.github.io), [Haodong Duan](https://kennymckormick.github.io), [Xiaoshi Wu](https://github.com/tgxs002), [Renrui Zhang](https://github.com/ZrrSkywalker), [Aojun Zhou](https://scholar.google.com/citations?user=cC8lXi8AAAAJ&hl=en), [Zipeng Qin](https://www.linkedin.cn/incareer/in/zipeng-bruce-qin-846a65119), [Yi Wang](https://shepnerd.github.io), [Jifeng Dai](https://jifengdai.org), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Hongsheng Li](https://www.ee.cuhk.edu.hk/~hsli/)<sup>+</sup>

(\* equal contribution, <sup>+</sup> corresponding author)

### Contact
If you have any problem or suggestion, please feel free to open an issue or send emails to the contributors.