A dataset with 500k+ logos crawled from the internet
This project is a work-in-progress, but we are releasing a preliminary version of our dataset.The goal of this project is to explore to what extent, artificial intelligence can solve the creative task of designing logos. For this we train Generative Adversarial Networks on our proposed dataset and obtain very promising results.The dataset currently consists of 548210 favicons and was crawled from the the Alexa 1M websites list on April 7th 2017.The main features of LLD 0.1 are:Standardized resolution: 32 x 32 pixels Training-friendly format: a sequence of binary python pickle files, each containing 100.000 logos (except for the last one) as numpy arrays in a random permutation of the data. The arrays are of standardized shape (32, 32, 3) ready for use in TensorFlow or similar machine learning frameworks. Single-file format: Single PNG files for maximum flexibility of use LLD 0.1 is available for download now The favicon dataset is provided in two versions:Full version containing all favicons we where able to crawl, with duplicates removed. Size: 548.210 images Clean version, optimized for use with GAN's: Here we attempted to remove all non logo-like images, especially natural images like faces as well as very complex and empty logos. Size: 486.377 images The LLD v1 will contain:Crawled high resolution logos (> 100x100 px) of >100.000 websites Associated meta-information Training friendly formats Our GAN models for high resolution logo generation We will release LLD v1 as soon as we can!