{"url":"/dataset/visuelle2-0","name":"VISUELLE2.0","full_name":"VISUELLE2.0","description_markdown":"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 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.","description_withheld":null,"homepage":"https://humaticslab.github.io/forecasting/visuelle","introduced_date":"2022-04-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-multi-modal-universe-of-fast-fashion-the","title":"The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark","first_author":"Geri Skenderi","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Forecasting","url":"/task/time-series-forecasting","datasets_with_task":"/datasets/task/time-series-forecasting"},{"name":"New Product Sales Forecasting","url":"/task/new-product-sales-forecasting","datasets_with_task":"/datasets/task/new-product-sales-forecasting"},{"name":"Product Recommendation","url":"/task/product-recommendation","datasets_with_task":"/datasets/task/product-recommendation"},{"name":"Short-observation new product sales forecasting","url":"/task/short-observation-new-product-sales","datasets_with_task":"/datasets/task/short-observation-new-product-sales"},{"name":"Popularity Forecasting","url":"/task/popularity-forecasting","datasets_with_task":"/datasets/task/popularity-forecasting"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["VISUELLE2.0"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/new-product-sales-forecasting-on-visuelle2-0","task":"New Product Sales Forecasting","dataset_variant":"VISUELLE2.0","rows":1,"metrics":["MAE"],"first_row_in_archive_order":{"model":"Explainable Cross-Attention Multimodal RNN","paper":"/paper/attention-based-multi-modal-new-product-sales","metrics":{"MAE":"0.99"},"code_links":[{"title":"HumaticsLAB/AttentionBasedMultiModalRNN","url":"https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/short-observation-new-product-sales","task":"Short-observation new product sales forecasting","dataset_variant":"VISUELLE2.0","rows":1,"metrics":["1 step MAE","10 steps MAE"],"first_row_in_archive_order":{"model":"Explainable Cross-Attention Multimodal RNN","paper":"/paper/attention-based-multi-modal-new-product-sales","metrics":{"1 step MAE":"0.96","10 steps MAE":"0.94"},"code_links":[{"title":"HumaticsLAB/AttentionBasedMultiModalRNN","url":"https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/attention-based-multi-modal-new-product-sales","title":"Attention based Multi-Modal New Product Sales Time-series Forecasting","date":"2020-08-23","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}