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Datasets:
dwb2023
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filtered-coyo-700M-beta

Tasks:
Text-to-Image
Image-to-Text
Zero-Shot Classification
Modalities:
Image
Tabular
Text
Formats:
parquet
Sub-tasks:
image-captioning
Languages:
English
Size:
10K - 100K
Tags:
image-text pairs
medical
Libraries:
Datasets
pandas
License:
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metadata
annotations_creators:
  - no-annotation
language:
  - en
language_creators:
  - other
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: filterred-coyo-700m-beta
size_categories:
  - 100M<n<1B
source_datasets:
  - original
tags:
  - image-text pairs
  - medical
task_categories:
  - text-to-image
  - image-to-text
  - zero-shot-classification
task_ids:
  - image-captioning

Dataset Card for filterred-coyo-700M-beta

Table of Contents

Dataset Description

  • Homepage:
  • Repository:
  • Paper:
  • Leaderboard:
  • Point of Contact:

Dataset Summary

Supported Tasks and Leaderboards

Languages

The texts in the COYO-700M dataset consist of English.

Dataset Structure

Data Instances

Each instance in COYO-700M represents single image-text pair information with meta-attributes:

{
  'id': 841814333321,
  'url': 'https://blog.dogsof.com/wp-content/uploads/2021/03/Image-from-iOS-5-e1614711641382.jpg',
  'text': 'A Pomsky dog sitting and smiling in field of orange flowers',
  'width': 1000,
  'height': 988,
  'image_phash': 'c9b6a7d8469c1959',
  'text_length': 59,
  'word_count': 11,
  'num_tokens_bert': 13,
  'num_tokens_gpt': 12,
  'num_faces': 0,
  'clip_similarity_vitb32': 0.4296875,
  'clip_similarity_vitl14': 0.35205078125,
  'nsfw_score_opennsfw2': 0.00031447410583496094,
  'nsfw_score_gantman': 0.03298913687467575,
  'watermark_score': 0.1014641746878624,
  'aesthetic_score_laion_v2': 5.435476303100586
}

Data Fields

name type description
id long Unique 64-bit integer ID generated by monotonically_increasing_id()
url string The image URL extracted from the src attribute of the <img> tag
text string The text extracted from the alt attribute of the <img> tag
width integer The width of the image
height integer The height of the image
image_phash string The perceptual hash(pHash) of the image
text_length integer The length of the text
word_count integer The number of words separated by spaces.
num_tokens_bert integer The number of tokens using BertTokenizer
num_tokens_gpt integer The number of tokens using GPT2TokenizerFast
num_faces integer The number of faces in the image detected by SCRFD
clip_similarity_vitb32 float The cosine similarity between text and image(ViT-B/32) embeddings by OpenAI CLIP
clip_similarity_vitl14 float The cosine similarity between text and image(ViT-L/14) embeddings by OpenAI CLIP
nsfw_score_opennsfw2 float The NSFW score of the image by OpenNSFW2
nsfw_score_gantman float The NSFW score of the image by GantMan/NSFW
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

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 sources in HTML documents in CommonCrawl from Oct. 2020 to Aug. 2021. and eliminated uninformative pairs through the image and/or text level filtering process with minimal cost.

Image Level

  • Included all image formats that Pillow library can decode. (JPEG, WEBP, PNG, BMP, ...)
  • Removed images less than 5KB image size.
  • Removed images with an aspect ratio greater than 3.0.
  • Removed images with min(width, height) < 200.
  • Removed images with a score of OpenNSFW2 or GantMan/NSFW higher than 0.5.
  • Removed all duplicate images based on the image pHash value from external public datasets.
    • ImageNet-1K/21K, Flickr-30K, MS-COCO, CC-3M, CC-12M

Text Level

  • Collected only English text using cld3.
  • Replaced consecutive whitespace characters with a single whitespace and removed the whitespace before and after the sentence. (e.g. "\n \n Load image into Gallery viewer, valentine&amp;#39;s day roses\n \n" → "Load image into Gallery viewer, valentine&amp;#39;s day roses")
  • Removed texts with a length of 5 or less.
  • Removed texts that do not have a noun form.
  • Removed texts with less than 3 words or more than 256 words and texts over 1000 in length.
  • Removed texts appearing more than 10 times. (e.g. “thumbnail for”, “image for”, “picture of”)
  • Removed texts containing NSFW words collected from profanity_filter, better_profanity, and google_twunter_lol.

Image-Text Level

  • Removed duplicated samples based on (image_phash, text). (Different text may exist for the same image URL.)

Who are the source language producers?

Common Crawl 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

Disclaimer & Content Warning

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

License

The COYO dataset of Kakao Brain is licensed under CC-BY-4.0 License. The full license can be found in the LICENSE.cc-by-4.0 file. 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.

Obligation to use

While Open Source may be free to use, that does not mean it is free of obligation. To determine whether your intended use of the COYO dataset is suitable for the CC-BY-4.0 license, please consider the license guide. If you violate the license, you may be subject to legal action such as the prohibition of use or claim for damages depending on the use.

Citation Information

Based on the following dataset:

@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

  • Don Branson