| --- |
| 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(1.08GB) contains 412 prompts and 3.2K images and |
| the test(1.14GB) split contains 466 prompts and 3.4K images. The information on the train split in different scales is as follows: |
|
|
| |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, 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|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("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} |
| } |
| ``` |
