|
--- |
|
license: apache-2.0 |
|
task_categories: |
|
- text-to-image |
|
language: |
|
- en |
|
pretty_name: ImageReward Dataset |
|
size_categories: |
|
- 100K<n<1M |
|
--- |
|
|
|
# ImageRewardDB |
|
|
|
## Dataset Description |
|
|
|
- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB** |
|
- **Repository: https://github.com/THUDM/ImageReward** |
|
- **Paper: https://arxiv.org/abs/2304.05977** |
|
|
|
### 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 publicly available at |
|
[🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB). |
|
|
|
Notice: All images in ImageRewardDB are collected from DiffusionDB, and in addition, we gathered together images corresponding to the same prompt. |
|
|
|
### 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(1.10GB) contains 412 prompts and 2.6K images(7.32K pairs) and |
|
the test(1.16GB) split contains 466 prompts and 2.7K images(7.23K pairs). The information on the train split in different scales is as follows: |
|
|
|
|Subset|Num of Pairs|Num of Images|Num of Prompts|Size| |
|
|:--|--:|--:|--:|--:| |
|
|ImageRewardDB 1K|17.6K|6.2K|1K|2.7GB| |
|
|ImageRewardDB 2K|35.5K|12.5K|2K|5.5GB| |
|
|ImageRewardDB 4K|71.0K|25.1K|4K|10.8GB| |
|
|ImageRewardDB 8K|141.1K|49.9K|8K|20.9GB| |
|
|
|
## 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, their corresponding |
|
images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation. |
|
The file structure is as follows: |
|
``` |
|
# 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 has 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 matches 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 splits of "train", "validation", 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 matches 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 is 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, ...|Outdoor Scenes|8|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("THUDM/ImageRewardDB", "1k") |
|
|
|
# Load the 2K-scale dataset |
|
dataset = load_dataset("THUDM/ImageRewardDB", "2k") |
|
|
|
# Load the 4K-scale dataset |
|
dataset = load_dataset("THUDM/ImageRewardDB", "4K") |
|
|
|
# Load the 8K-scale dataset |
|
dataset = load_dataset("THUDM/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} |
|
} |
|
``` |
