Datasets:

l3afai
/

youtube-thumbnails

language:
  - en
tags:
  - youtube
  - thumbnails
  - image-text
  - multimodal
  - text-to-image
  - image-to-text
  - captioning
  - weak-supervision
  - large-scale
  - computer-vision
  - nlp
  - vision-language
  - clip-training
  - diffusion
  - generative-models
  - image-generation
  - thumbnail-generation
  - social-media
  - content-creation
  - visual-design
  - high-contrast
  - faces
  - expressions
  - memes
  - clickbait
  - marketing
  - advertising
  - attention-modeling
  - representation-learning
  - embedding
  - retrieval
  - search
  - ranking
  - dataset-creation
  - public-data
  - self-supervised
  - weak-labels
  - noisy-labels
  - english
  - filtered
  - deduplicated
  - large-dataset
  - research
  - experimental
  - open-data
  - vision
  - multimodal-learning
  - image-dataset
  - text-dataset
pretty_name: Youtube Thumbnails
task_categories:
  - text-to-image
  - image-to-text
  - feature-extraction
size_categories:
  - 100K<n<1M
license: other

YouTube Thumbnails Dataset

Dataset Details

Dataset Description

This dataset contains approximately 164,000 YouTube thumbnails paired with their corresponding video titles.

The dataset was constructed by collecting public YouTube channel feeds, extracting video metadata, filtering and deduplicating entries, and downloading thumbnail images at scale.

The goal of this dataset is to support research and experimentation in:

Image generation (e.g. diffusion models)
Multimodal learning (e.g. CLIP-style models)
Thumbnail generation and optimization
Image-text representation learning

Curated by: l3af (Discord: l3afai)
Language(s): English (filtered using language detection)
License: Derived from publicly available YouTube data. Users are responsible for complying with YouTube's Terms of Service.

Dataset Sources

Source: Public YouTube RSS feeds (videos.xml)
Images: YouTube thumbnail CDN (i.ytimg.com)
Metadata: Video titles and IDs

Uses

Direct Use

This dataset is suitable for:

Training image generation models (especially thumbnail-style generation)
Training multimodal embedding models (e.g. CLIP)
Studying social-media visual patterns
Thumbnail generation or ranking systems

Out-of-Scope Use

This dataset is not recommended for:

High-quality caption-to-image generation (titles are not descriptive captions)
Tasks requiring precise semantic grounding
Sensitive or safety-critical applications

Dataset Structure

Each example contains:

video_id (string): YouTube video identifier
title (string): Video title
image (image): Thumbnail image

Dataset Creation

Curation Rationale

This dataset was created to provide a large-scale collection of real-world image-text pairs with strong visual patterns, particularly useful for studying:

Attention-grabbing design
High-contrast visual composition
Social media aesthetics

Source Data

Data Collection and Processing

The dataset was created through the following pipeline:

Collected ~22,000 YouTube channel IDs
Downloaded RSS feeds (videos.xml)
Extracted video metadata
Filtered:
- Removed Shorts content
- Removed non-English titles (via language detection)
Deduplicated titles (exact + fuzzy)
Downloaded thumbnail images (max resolution when available)
Built dataset in multiple formats (Parquet + HF dataset)

Who are the source data producers?

The source data was originally created by YouTube content creators across a wide range of domains, including entertainment, education, gaming, and news.

Annotations

No manual annotations were added. The dataset consists solely of:

Original thumbnails
Original video titles

Personal and Sensitive Information

Some thumbnails may contain human faces or identifiable individuals
Titles and images may reflect biases from content creators
No additional personal data was intentionally collected

Data Traceability

Each entry includes a video_id which uniquely identifies the original YouTube video.

Users can reconstruct the original source via:

https://www.youtube.com/watch?v={video_id}

This enables:

Attribution to original creators
Verification of data origin
Selective filtering or removal

Bias, Risks, and Limitations

Strong bias toward YouTube-style content (faces, text overlays, high contrast)
Titles are often:
- Clickbait
- Vague
- Non-descriptive
Images frequently contain embedded text (which models struggle to generate correctly)
Distribution may not reflect real-world image diversity

Recommendations

Use for style-focused tasks, not semantic grounding
Consider augmenting with caption datasets for better text alignment
Filter further if targeting specific domains

Citation

If you use this dataset, please cite:

l3afai. (2026). YouTube Thumbnails Dataset.

Dataset Card Authors

l3af (Discord: l3afai)

License and Attribution

This dataset contains images and metadata derived from publicly available YouTube content.

All rights to the original thumbnails belong to their respective creators.
This dataset does not claim ownership of any images.
Each sample includes a video_id which can be used to trace the original source: https://www.youtube.com/watch?v={video_id}

This dataset is provided for research and educational purposes only.

If you are a content owner and would like your data removed, please contact the dataset maintainer.

Takedown Policy

If you are a rights holder and wish to have content removed from this dataset, please contact the maintainer with the relevant video_id(s). The content will be removed.

Dataset Card Contact

For questions or issues, contact:

Discord: l3afai