We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using only the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%.

The Multi-Modal Universe of Fast-Fashion: The Visuelle 2.0 Benchmark

Skenderi, Geri
;
Joppi, Christian;Denitto, Matteo;Cristani, Marco
2022

Abstract

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using only the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%.
Fashion, Sales, Forecasting
File in questo prodotto:
File Dimensione Formato  
Skenderi_The_Multi-Modal_Universe_of_Fast-Fashion_The_Visuelle_2.0_Benchmark_CVPRW_2022_paper.pdf

accesso aperto

Descrizione: Paper
Tipologia: Documento in Post-print
Licenza: Creative commons
Dimensione 1.16 MB
Formato Adobe PDF
1.16 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/1071086
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact