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---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
pretty_name: ImageReward Dataset
size_categories:
- 1K<n<10K
---
# ImageRewardDB
## Dataset Description
- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB**
- **Repository: TBD**
- **Paper: TBD**
### Dataset Summary
ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference.
It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB.
To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and
annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now public available at
[🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB).
### Languages
The text in the dataset is all in English.
### Four Subsets
Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs.
For all subsets, the validation and test splits remain the same. The validation split(1.08GB) contains 412 prompts and 3.2K images and
the test(1.14GB) split cotains 466 prompts and 3.4K images. The information of the train split in different scales is as following:
|Subset|Num of Images|Num of Prompts|Size|
|:--|--:|--:|--:|
|ImageRewardDB 1K|7.8K|1K|2.7GB|
|ImageRewardDB 2K|15.6K|2K|5.4GB|
|ImageRewardDB 4K|31.4K|4K|10.6GB|
|ImageRewardDB 8K|62.6K|8K|20.6GB|
## Dataset Structure
All the data in this repository is stored in a well organized way. The 62.6K images in ImageRewardDB are split into several folders,
stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, its corresponding
images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation.
The file structure is as following:
```
# ImageRewardDB
./
├── images
│ ├── train
│ │ ├── train_1
│ │ │ ├── 0a1ed3a5-04f6-4a1b-aee6-d584e7c8ed9c.webp
│ │ │ ├── 0a58cfa8-ff61-4d31-9757-27322aec3aaf.webp
│ │ │ ├── [...]
│ │ │ └── train_1.json
│ │ ├── train_2
│ │ ├── train_3
│ │ ├── [...]
│ │ └── train_32
│ ├── validation
│ │ └── [...]
│ └── test
│ └── [...]
├── metadata-train.parquet
├── metadata-validation.parquet
└── metadata-test.parquet
```
The sub-folders have the name of <split_name>_<part_id>, and the JSON file have the same name as the sub-folder.
Each image is a lossless WebP file, and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).
### Data Instances
For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp` and its information in train_1.json.
```json
{
"image_path": "images/train/train_1/0280642d-f69f-41d1-8598-5a44e296aa8b.webp",
"prompt_id": "000864-0061",
"prompt": "painting of a holy woman, decorated, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k ",
"classification": "People",
"image_amount_in_total": 9,
"rank": 5,
"overall_rating": 4,
"image_text_alignment_rating": 3,
"fidelity_rating": 4
}
```
### Data Fields
* image: The image object
* prompt_id: The id of the corresponding prompt
* prompt: The text of the corresponding prompt
* classification: The classification of the corresponding prompt
* image_amount_in_total: Total amount of images related to the prompt
* rank: The relative rank of the image in all related images
* overall_rating: The overall score of this image
* image_text_alignment_rating: The score of how well the generated image matchs the given text
* fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have
### Data Splits
As we mentioned above, all scales of the subsets we provided have three spilts of "train", "validtion", and "test".
And all the subsets share the same validation and test splits.
### Dataset Metadata
We also include three metadata tables `metadata-train.parquet`, `metadata-validation.parquet`, and `metadata-test.parquet` to
help you access and comprehend ImageRewardDB without downloading the Zip files.
All the tables share the same schema, and each row refers to an image. The schema is shown below,
and actually the JSON files we mentioned above share the same schema:
|Column|Type|Description|
|:---|:---|:---|
|`image_path`|`string`|The relative path of the image in the repository.|
|`prompt_id`|`string`|The id of the corresponding prompt.|
|`prompt`|`string`|The text of the corresponding prompt.|
|`classification`|`string`| The classification of the corresponding prompt.|
|`image_amount_in_total`|`int`| Total amount of images related to the prompt.|
|`rank`|`int`| The relative rank of the image in all related images.|
|`overall_rating`|`int`| The overall score of this image.
|`image_text_alignment_rating`|`int`|The score of how well the generated image matchs the given text.|
|`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.|
Below are an example row from metadata-train.parquet.
|image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating|
|:---|:---|:---|:---|:---|:---|:---|:---|:---|
|images/train/train_1/1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp|001324-0093|a magical forest that separates the good world from the dark world, fantasy art by greg rutkowski, loish, rhads, ferdinand knab, makoto shinkai and lois van baarle, ilya kuvshinov, rossdraws, tom bagshaw, global illumination, radiant light, detailed and intricate environment|Outdoor Scenes|9|3|6|6|6|
## Loading ImageRewardDB
You can use the Hugging Face [Datasets](https://huggingface.co/docs/datasets/quickstart) library to easily load the ImageRewardDB.
As we mentioned before, we provide four subsets in the scales of 1k, 2k, 4k, and 8k. You can load them using as following:
```python
from datasets import load_dataset
# Load the 1K-scale dataset
dataset = load_dataset("wuyuchen/ImageRewardDB", "1k")
# Load the 2K-scale dataset
dataset = load_dataset("wuyuchen/ImageRewardDB", "2k")
# Load the 4K-scale dataset
dataset = load_dataset("wuyuchen/ImageRewardDB", "4K")
# Load the 8K-scale dataset
dataset = load_dataset("wuyuchen/ImageRewardDB", "8k")
```
## Additional Information
### Licensing Information
The ImageRewardDB dataset is available under the [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
### Citation Information
```
@misc{xu2023imagereward,
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation},
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
year={2023},
eprint={2304.05977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |