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--- |
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license: apache-2.0 |
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task_categories: |
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- text-to-image |
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language: |
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- en |
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pretty_name: ImageReward Dataset |
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size_categories: |
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- 100K<n<1M |
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--- |
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# ImageRewardDB |
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## Dataset Description |
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- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB** |
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- **Repository: https://github.com/THUDM/ImageReward** |
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- **Paper: https://arxiv.org/abs/2304.05977** |
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### Dataset Summary |
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ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. |
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It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. |
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To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and |
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annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now publicly available at |
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[🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB). |
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Notice: All images in ImageRewardDB are collected from DiffusionDB, and in addition, we gathered together images corresponding to the same prompt. |
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### Languages |
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The text in the dataset is all in English. |
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### Four Subsets |
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Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs. |
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For all subsets, the validation and test splits remain the same. The validation split(1.08GB) contains 412 prompts and 3,255 images (10.5K pairs) and |
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the test(1.14GB) split contains 466 prompts and 3,461 images (10.6K pairs). The information on the train split in different scales is as follows: |
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|Subset|Num of Pairs|Num of Images|Num of Prompts|Size| |
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|:--|--:|--:|--:|--:| |
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|ImageRewardDB 1K|25.1K|7.8K|1K|2.7GB| |
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|ImageRewardDB 2K|50.5K|15.7K|2K|5.4GB| |
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|ImageRewardDB 4K|101.0K|31.4K|4K|10.6GB| |
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|ImageRewardDB 8K|201.1K|62.7K|8K|20.6GB| |
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## Dataset Structure |
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All the data in this repository is stored in a well-organized way. The 62.6K images in ImageRewardDB are split into several folders, |
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stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, their corresponding |
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images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation. |
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The file structure is as follows: |
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``` |
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# ImageRewardDB |
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./ |
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├── images |
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│ ├── train |
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│ │ ├── train_1 |
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│ │ │ ├── 0a1ed3a5-04f6-4a1b-aee6-d584e7c8ed9c.webp |
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│ │ │ ├── 0a58cfa8-ff61-4d31-9757-27322aec3aaf.webp |
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│ │ │ ├── [...] |
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│ │ │ └── train_1.json |
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│ │ ├── train_2 |
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│ │ ├── train_3 |
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│ │ ├── [...] |
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│ │ └── train_32 |
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│ ├── validation |
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│ │ └── [...] |
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│ └── test |
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│ └── [...] |
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├── metadata-train.parquet |
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├── metadata-validation.parquet |
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└── metadata-test.parquet |
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``` |
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The sub-folders have the name of {split_name}_{part_id}, and the JSON file has the same name as the sub-folder. |
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Each image is a lossless WebP file and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier). |
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### Data Instances |
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For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp` and its information in train_1.json. |
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```json |
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{ |
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"image_path": "images/train/train_1/0280642d-f69f-41d1-8598-5a44e296aa8b.webp", |
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"prompt_id": "000864-0061", |
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"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 ", |
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"classification": "People", |
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"image_amount_in_total": 9, |
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"rank": 5, |
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"overall_rating": 4, |
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"image_text_alignment_rating": 3, |
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"fidelity_rating": 4 |
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} |
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``` |
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### Data Fields |
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* image: The image object |
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* prompt_id: The id of the corresponding prompt |
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* prompt: The text of the corresponding prompt |
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* classification: The classification of the corresponding prompt |
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* image_amount_in_total: Total amount of images related to the prompt |
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* rank: The relative rank of the image in all related images |
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* overall_rating: The overall score of this image |
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* image_text_alignment_rating: The score of how well the generated image matches the given text |
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* fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have |
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### Data Splits |
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As we mentioned above, all scales of the subsets we provided have three splits of "train", "validation", and "test". |
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And all the subsets share the same validation and test splits. |
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### Dataset Metadata |
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We also include three metadata tables `metadata-train.parquet`, `metadata-validation.parquet`, and `metadata-test.parquet` to |
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help you access and comprehend ImageRewardDB without downloading the Zip files. |
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All the tables share the same schema, and each row refers to an image. The schema is shown below, |
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and actually, the JSON files we mentioned above share the same schema: |
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|Column|Type|Description| |
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|:---|:---|:---| |
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|`image_path`|`string`|The relative path of the image in the repository.| |
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|`prompt_id`|`string`|The id of the corresponding prompt.| |
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|`prompt`|`string`|The text of the corresponding prompt.| |
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|`classification`|`string`| The classification of the corresponding prompt.| |
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|`image_amount_in_total`|`int`| Total amount of images related to the prompt.| |
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|`rank`|`int`| The relative rank of the image in all related images.| |
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|`overall_rating`|`int`| The overall score of this image. |
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|`image_text_alignment_rating`|`int`|The score of how well the generated image matches the given text.| |
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|`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.| |
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Below is an example row from metadata-train.parquet. |
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|image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating| |
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|:---|:---|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---|:---|:---|:---|:---|:---| |
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|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| |
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## Loading ImageRewardDB |
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You can use the Hugging Face [Datasets](https://huggingface.co/docs/datasets/quickstart) library to easily load the ImageRewardDB. |
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As we mentioned before, we provide four subsets in the scales of 1k, 2k, 4k, and 8k. You can load them using as following: |
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```python |
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from datasets import load_dataset |
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# Load the 1K-scale dataset |
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dataset = load_dataset("THUDM/ImageRewardDB", "1k") |
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# Load the 2K-scale dataset |
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dataset = load_dataset("THUDM/ImageRewardDB", "2k") |
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# Load the 4K-scale dataset |
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dataset = load_dataset("THUDM/ImageRewardDB", "4K") |
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# Load the 8K-scale dataset |
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dataset = load_dataset("THUDM/ImageRewardDB", "8k") |
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``` |
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## Additional Information |
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### Licensing Information |
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The ImageRewardDB dataset is available under the [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html). |
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The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE). |
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### Citation Information |
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``` |
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@misc{xu2023imagereward, |
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title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation}, |
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author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong}, |
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year={2023}, |
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eprint={2304.05977}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
