--- annotations_creators: - no-annotation language: - en language_creators: - other license: - cc-by-4.0 multilinguality: - monolingual pretty_name: COYO-700M size_categories: - 100M` tag | | text | string | The text extracted from the `alt` attribute of the `` tag | | width | integer | The width of the image | | height | integer | The height of the image | | image_phash | string | The [perceptual hash(pHash)](http://www.phash.org/) of the image | | text_length | integer | The length of the text | | word_count | integer | The number of words seperated by spaces. | | num_tokens_bert | integer | The number of tokens using [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer) | | num_tokens_gpt | integer | The number of tokens using [GPT2TokenizerFast](https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2TokenizerFast) | | num_faces | integer | The number of faces in the image detected by [SCRFD](https://insightface.ai/scrfd) | | clip_similarity_vitb32 | float | The cosine similarity between text and image(ViT-B/32) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP) | | clip_similarity_vitl14 | float | The cosine similarity between text and image(ViT-L/14) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP) | | nsfw_score_opennsfw2 | float | The NSFW score of the image by [OpenNSFW2](https://github.com/bhky/opennsfw2) | | nsfw_score_gantman | float | The NSFW score of the image by [GantMan/NSFW](https://github.com/GantMan/nsfw_model) | | watermark_score | float | The watermark probability of the image by our internal model | | aesthetic_score_laion_v2 | float | The aesthetic score of the image by [LAION-Aesthetics-Predictor-V2](https://github.com/christophschuhmann/improved-aesthetic-predictor) | ### Data Splits Data was not split, since the evaluation was expected to be performed on more widely used downstream task(s). ## Dataset Creation ### Curation Rationale Similar to most vision-and-language datasets, our primary goal in the data creation process is to collect many pairs of alt-text and image sources in HTML documents crawled from the web. Therefore, We attempted to eliminate uninformative images or texts with minimal cost and improve our dataset's usability by adding various meta-attributes. Users can use these meta-attributes to sample a subset from COYO-700M and use it to train the desired model. For instance, the *num_faces* attribute could be used to make a subset like *COYO-Faces* and develop a privacy-preserving generative model. ### Source Data #### Initial Data Collection and Normalization We collected about 10 billion pairs of alt-text and image source in HTML documents in [CommonCrawl](https://commoncrawl.org/) from Oct. 2020 to Aug. 2021. and eliminated uninformative pairs through the image and/or text level filtering process with minimal cost. **Image Level** * Include all image formats that Pillow library can decode * Less than 5KB image size are dropped * Images with aspect ratio is greater than 3.0 are dropped * Images with min(width, height) < 200 are dropped * Images are dropped if the score of [OpenNSFW2](https://github.com/yahoo/open_nsfw) or [GantMan/NSFW](https://github.com/GantMan/nsfw_model) is higher than 0.5 * Based on the image [pHash](http://www.phash.org/) value, we removed all duplicate images from external public datasets. (ImageNet-1K/21K, Flickr-30K, MS-COCO, CC-3M, CC-12M) **Text Level** * We collected only english text using [cld3](https://github.com/google/cld3) * Consecutive whitespace characters are replaced with a single whitespace and whitespace before and after the sentence are removed (e.g. `"\n \n Load image into Gallery viewer, valentine&#39;s day roses\n \n" → "Load image into Gallery viewer, valentine&#39;s day roses"`) * Any text with a length of 5 or less has been dropped * Text that does not have a noun form has been dropped * Text less than 3 words or more than 256 words and text over 1000 words were dropped * All texts appearing more than 10 times have been dropped (e.g. `“thumbnail for”, “image for”, “picture of”`) **Image-Text Level** * Based on (image_phash, text), duplicated samples have been dropped (Different text may exist for the same image URL.) #### Who are the source language producers? [Common Crawl](https://commoncrawl.org/) is the data source for COYO-700M. ### Annotations #### Annotation process The dataset was built in a fully automated process that did not require human annotation. #### Who are the annotators? No human annotation ### Personal and Sensitive Information The COYO dataset is recommended to be used for research purposes. Kakao Brain tried to construct a "Safe" dataset when building the COYO dataset. (See Data Filtering Section) Kakao Brain is constantly making efforts to create more "Safe" datasets. However, despite these efforts, this large-scale dataset was not hand-picked by humans to avoid the risk due to its very large size (over 700M). Keep in mind that the unscreened nature of the dataset means that the collected images can lead to strongly discomforting and disturbing content for humans. The COYO dataset may contain some inappropriate data, and any problems resulting from such data are the full responsibility of the user who used it. Therefore, it is strongly recommended that this dataset be used only for research, keeping this in mind when using the dataset, and Kakao Brain does not recommend using this dataset as it is without special processing to clear inappropriate data to create commercial products. ## Considerations for Using the Data ### Social Impact of Dataset It will be described in a paper to be released soon. ### Discussion of Biases It will be described in a paper to be released soon. ### Other Known Limitations It will be described in a paper to be released soon. ## Additional Information ### Dataset Curators COYO dataset was released as an open source in the hope that it will be helpful to many research institutes and startups for research purposes. We look forward to contacting us from various places who wish to cooperate with us. [coyo@kakaobrain.com](mailto://coyo@kakaobrain.com) ### Licensing Information The COYO dataset of Kakao Brain is licensed under [CC-BY-4.0 License](https://creativecommons.org/licenses/by/4.0/). The dataset includes “Image URL” and “Text” collected from various sites by analyzing Common Crawl data, an open data web crawling project. The collected data (images and text) is subject to the license to which each content belongs. ### Citation Information If you apply this dataset to any project and research, please cite our code: ``` @misc{kakaobrain2022coyo-700m, title = {COYO-700M: Image-Text Pair Dataset}, author = {Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim}, year = {2022}, howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}}, } ``` ### Contributions - Minwoo Byeon ([@mwbyeon](https://github.com/mwbyeon)) - Beomhee Park ([@beomheepark](https://github.com/beomheepark)) - Haecheon Kim ([@HaecheonKim](https://github.com/HaecheonKim)) - Sungjun Lee ([@justhungryman](https://github.com/justHungryMan)) - Woonhyuk Baek ([@wbaek](https://github.com/wbaek)) - Saehoon Kim ([@saehoonkim](https://github.com/saehoonkim)) - and Kakao Brain Large-Scale AI Studio