We present the Fashion144k dataset, consisting of 144,169 user posts with images and their associated metadata, for predicting fashionability, that is, how fashionable the user and their outfit is in an image. We exploit the votes given to each post by different users to obtain measure of fashionability, and provide diverse metadata to perform analysis and predictions.

For a related dataset, please see Fashion550k.

We collected a total of 144,169 posts to create a dataset to be able to create our models. The posts are distributed all over the world and allow us to get a glimpse of the state of global fashion. However, the data is not uniformly distributed as seen below in the density map.

Density Map Density map of the Fashion144k dataset. Darker colours indicate a larger amount of posts in that location.

We use the votes given by users to each post as a proxy for the absolute fashionability of the post, which is something subjective that can not be directly measured. While this is not a perfect metric, it is something that allows us to teach a computer to rate and recommend fashion images. This opens up the possibility to automatic rating of outfit of the day posts, and having a personal fashion advisor in your cellphone.

We have run an analysis on the most fashionable cities in the dataset. While some results are surprising such as Manila having a very high mean fashionability, other results are in concordance to what one would think. Such as Los Angeles, Paris, New York or London being above average, and Barcelona being trendier than Madrid.

List of cities with over 1000 posts with their mean fashionability score. The fashionability score is on a 1 to 10 scale.
City Name Posts Fashionability
Manila 4269 6.627
Los Angeles 8275 6.265
Melbourne 1092 6.176
Montreal 1129 6.144
Paris 2118 6.070
Amsterdam 1111 6.059
Barcelona 1292 5.845
Toronto 1471 5.765
Bucharest 1385 5.667
New York 4984 5.514
London 3655 5.444
San Francisco 2880 5.392
Madrid 1747 5.371
Vancouver 1468 5.266
Jakarta 1156 4.398

We can also do a higher level analysis and try to relate the fashionability scores with country attributes such as the mean economy and income class, the Gross Domestic Product (GDP) and the population. It is interesting to see that sadly as expected, countries with more developed economies have larger fashionability scores. Also, in general, countries with larger populations score better on average.

Relation between high level attributes and fashionability in the dataset. Economy and income class are on a scale such that smaller numbers corresponds to most developed country and larger numbers to less developed country. Negative values mean that low values are correlated with high fashionability while high values indicate that high values are correlated with high fashionability.
Attribute Correlation Interpretation
Economy class -0.137 Countries with stronger economies tend to have more trendy inhabitants.
Income class -0.111 Countries with higher mean income tend to have more fashionable inhabitants.
log(GDP) 0.258 Countries with higher Gross Domestic Product tend to have more trendy inhabitants.
log(Population) 0.231 Countries with larger populations tend to have more fashionable inhabitants.


If you use this dataset, please cite

  author    = {Edgar Simo-Serra and Sanja Fidler and Francesc Moreno-Noguer and Raquel Urtasun},
  title     = {{Neuroaesthetics in Fashion: Modeling the Perception of Fashionability}},
  booktitle = "Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR)",
  year      = 2015,