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.
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 _, 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.
Data Instances
For instance, below is the image of 1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp and its information in train_1.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 |
Dataset Creation
Curation Rationale
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Source Data
Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
Annotation process
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Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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Discussion of Biases
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Other Known Limitations
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Additional Information
Dataset Curators
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Licensing Information
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Citation Information
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Contributions
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