Coyo 700m Image Text Pair Dataset Github Kakaobrain Coyo 700m

COYO-700M: Image-Text Pair Dataset

One-line decision

Use this skill when you need a large-scale open image-text dataset from Common Crawl as an alternative to LAION with transparent collection methodology. Avoid it when LAION-5B or other existing datasets are sufficient for your needs.

Skill metadata

• Skill type: korean-web-image-text-collection
• Paper kind: operational-method
• Actionability: high
• Evidence quality: full_paper

Goal

Collect 700 million image-text pairs from Common Crawl with transparent methodology and comprehensive metadata, providing an open alternative to proprietary image-text datasets.

Problem signature

• Modality: image-text pairs from Common Crawl with quality metadata.
• Data state: web-crawled image-text pairs with CLIP scores, language tags, and quality metadata.
• Scale regime: 700 million image-text pairs.
• Model requirement: No model required for collection; used for vision-language pretraining.

Use when

• You need a large-scale open image-text dataset.
• You want an alternative to LAION with transparent methodology.
• You need comprehensive metadata for filtering.

Do not use when

• LAION-5B or DataComp already meet your needs.
• You need a smaller, curated dataset.
• You need domain-specific image-text data.

Required inputs

• common_crawl: Common Crawl web archive.
• processing_pipeline: Pipeline for extracting and filtering image-text pairs.
• metadata_computation: CLIP scores, aesthetic scores, safety scores.

Optional inputs

• additional_filters: Domain-specific or quality filters.

Outputs

• coyo_700m: 700M image-text pairs with metadata.
• metadata_fields: CLIP score, aesthetic score, safety score, language tag per pair.

Assumptions and prerequisites

• Common Crawl contains sufficient image-text co-occurrences.
• Transparent methodology enables reproducible research.
• Metadata enables flexible downstream filtering.

Procedure

1. Extract image-text from Common Crawl Action: Parse web pages to extract co-occurring images and alt-text. Why: Common Crawl is the primary source of web-scale image-text data. Note: See paper for details.
2. Download and validate images Action: Download images and verify validity. Why: Many URLs are broken or return invalid images. Note: See paper for details.
3. Compute metadata Action: Calculate CLIP scores, aesthetic scores, safety scores, and language tags. Why: Metadata enables flexible filtering by downstream users. Note: See paper for details.
4. Release with documentation Action: Package dataset with metadata and transparent methodology documentation. Why: Transparency enables reproducible and responsible use. Note: See paper for details.

Parameters to set

• clip_score_filter — Role: Minimum CLIP score for initial filtering. How to set: Use a permissive threshold; let users filter further. Default/range: Permissive. Effect: Lower threshold keeps more data for user-side filtering.
• metadata_fields — Role: Metadata provided per pair. How to set: Include CLIP, aesthetic, safety, language. Default/range: 4+ fields. Effect: More metadata enables finer-grained filtering.

Validation checks

• CLIP models trained on COYO should be competitive with LAION-trained models.
• Metadata should accurately reflect pair quality.
• The dataset should cover diverse visual content.

Failure modes

• URL decay reduces dataset reproducibility over time.
• Quality may be lower than more aggressively filtered datasets.
• Non-English content may dominate certain segments.

Adaptation notes for VLM training

• COYO-700M is used as pretraining data for CLIP and VLMs.
• Sub-filter using metadata for domain-specific applications.
• Combine with other datasets for broader coverage.

Implementation notes

• Use img2dataset for efficient downloading.
• Store in parquet format for efficient querying.
• Provide metadata for all pairs to enable user-side filtering.

Evidence from the paper

• COYO-700M provides 700 million image-text pairs from Common Crawl with transparent methodology.
• The dataset includes CLIP scores, aesthetic scores, safety scores, and language tags.
• COYO enables reproducible vision-language pretraining research.
• The dataset has been used to train competitive CLIP and VLM models.

Source paper

• Title: COYO-700M: Image-Text Pair Dataset
• Year: 2022
• Venue: GitHub
• Paper ID: github-kakaobrain-coyo-700m
• URL: https://github.com/kakaobrain/coyo-dataset
• arXiv ID: N/A