Large Logo Dataset For Generative Model Training

The LLD dataset contains over 600,000 logos crawled from the web for exploring machine learning in creative design tasks. It was created by author diwank and used to train Generative Adversarial Networks for logo synthesis and manipulation. The dataset was last updated in August 2022.

Use Cases

• Train Generative Adversarial Networks on over 600,000 logo images to generate new, plausible logos.
• Apply clustering techniques on the multi-modal logo data to obtain synthetic labels for stabilizing GAN training.
• Explore latent space manipulation techniques using the dataset for interactive logo design assistance.
• Validate GAN training methodologies, such as clustered GAN training, on a large-scale, real-world image dataset.

Strengths

• Contains over 600,000 logo images, providing substantial scale for model training.
• Designed for and validated in research on GANs for creative synthesis, as described in the associated paper.
• Dataset is categorized as having between 100K and 1M entries, indicating a large size category.

Limitations

• The multi-modal nature of logo data can lead to training instability and mode collapse in some GAN architectures.
• Specific image attributes, categories, or metadata for the 600k+ logos are not detailed in the provided description.
• The dataset's geographic and source bias is unknown, as logos were crawled from the web without specified filters.

Provenance

Source

Logos crawled from the world wide web.

Collection Method

Crawled from the web for research on machine learning in design.

Freshness

Last updated on 2022-08-09.