---
task_categories:
- image-to-text
language:
- en
size_categories:
- 1M<n<10M
---


# 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

```
@misc{pan2023journeydb,
      title={JourneyDB: A Benchmark for Generative Image Understanding}, 
      author={Junting Pan and Keqiang Sun and Yuying Ge and Hao Li and Haodong Duan and Xiaoshi Wu and Renrui Zhang and Aojun Zhou and Zipeng Qin and Yi Wang and Jifeng Dai and Yu Qiao and Hongsheng Li},
      year={2023},
      eprint={2307.00716},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

### 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.
arxiv:2307.00716 region:us
{"extra_gated_prompt": "You have carefully read the [Terms of Usage](https://journeydb.github.io/assets/Terms_of_Usage.html) and agree with the listed terms.", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Affiliation": "text", "I agree with our JourneyDB usage terms and I will obey the terms when using the JourneyDB dataset": "checkbox"}}
TAGS
#arxiv-2307.00716 #region-us 



---


task\_categories:


* image-to-text
language:
* en
size\_categories:
* 1M<n<10M




---


JourneyDB
=========


[[Project Page]](URL) [[Paper]](URL [[Code]](URL [[HuggingFace]](URL [OpenDataLab]


!image


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 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


### Data Instances


We provide several examples to show the contents of each dataset instance.
!image


### 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.



Acquirements
------------


### License


The JourneyDB dataset is available under the customised Terms of Usage.


### Contributions


Junting Pan\*, Keqiang Sun\*, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang, Aojun Zhou, Zipeng Qin, Yi Wang, Jifeng Dai, Yu Qiao, Hongsheng Li+


(\* equal contribution, + corresponding author)


### Contact


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



### 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 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 ### Data Instances


We provide several examples to show the contents of each dataset instance.
!image ### 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.



Acquirements
------------ ### License


The JourneyDB dataset is available under the customised Terms of Usage. ### Contributions


Junting Pan\*, Keqiang Sun\*, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang, Aojun Zhou, Zipeng Qin, Yi Wang, Jifeng Dai, Yu Qiao, Hongsheng Li+


(\* equal contribution, + corresponding author) ### Contact


If you have any problem or suggestion, please feel free to open an issue or send emails to the contributors.
TAGS
#arxiv-2307.00716 #region-us 
 ### 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 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 ### Data Instances


We provide several examples to show the contents of each dataset instance.
!image ### 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.



Acquirements
------------ ### License


The JourneyDB dataset is available under the customised Terms of Usage. ### Contributions


Junting Pan\*, Keqiang Sun\*, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang, Aojun Zhou, Zipeng Qin, Yi Wang, Jifeng Dai, Yu Qiao, Hongsheng Li+


(\* equal contribution, + corresponding author) ### Contact


If you have any problem or suggestion, please feel free to open an issue or send emails to the contributors.
passage: TAGS
#arxiv-2307.00716 #region-us 
### 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 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### Data Instances


We provide several examples to show the contents of each dataset instance.
!image### 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.



Acquirements
------------### License


The JourneyDB dataset is available under the customised Terms of Usage.### Contributions


Junting Pan\*, Keqiang Sun\*, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang, Aojun Zhou, Zipeng Qin, Yi Wang, Jifeng Dai, Yu Qiao, Hongsheng Li+


(\* equal contribution, + corresponding author)### Contact


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