Visuelle 2.0

The first, publicly available, multi-modal fashion forecasting dataset based on real-world data.

Accepted at the 5th Workshop on Computer Vision for Fashion, Art, and Design @ CVPR22

Dataset description

Visuelle 2.0 is a dataset containing real data for 5355 clothing products of the retail fast-fashion Italian company, Nuna Lie. Specifically, Visuelle 2.0 provides data from 6 fashion seasons (partitioned in Autumn-Winter and Spring-Summer) from 2017-2019, right before the Covid-19 pandemic.

Each product in our dataset is accompanied by an HD image, textual tags and more. The time series data are disaggregated at the shop level, and include the sales, inventory stock, max-normalized prices (for the sake of confidentiality} and discounts. Exogenous time series data is also provided, in the form of Google Trends based on the textual tags and multivariate weather conditions of the stores’ locations. Finally, we also provide purchase data for 667K customers whose identity has been anonymized, to capture personal preferences. With these data, Visuelle 2.0 allows to cope with several problems which characterize the activity of a fast fashion company: new product demand forecasting, short-observation new product sales forecasting, and product recommendation.

Different products and their data, sales, inventory and discount series

Examples of different products and their data

Examples of the exogenous Google Trend and weather data

Example of the exogenous Google Trend data Extract of the exogenous weather data

Purchases for 10 different customers

Purchases for 10 different customers

Data and code

You can download the Visuelle 2.0 dataset by filling out this simple form (the download link will be provided upon completion)

The code demonstrating how to use the dataset for the forecasting tasks described in the paper and also for the RNN-based model we used to obtain the best results is available here.

Citation

If you use Visuelle 2.0, please cite our paper. Do not hesitate to contact any of the authors with any comments or feedback.

@InProceedings{Skenderi_2022_CVPR,
    author    = {Skenderi, Geri and Joppi, Christian and Denitto, Matteo and Scarpa, Berniero and Cristani, Marco},
    title     = {The Multi-Modal Universe of Fast-Fashion: The Visuelle 2.0 Benchmark},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2022},
    pages     = {2241-2246}
}