{"url":"/dataset/visuelle","name":"VISUELLE","full_name":null,"description_markdown":"VISUELLE is a repository build upon the data of a real fast fashion company, Nunalie, and is composed of 5577 new products and about 45M sales related to fashion seasons from 2016-2019. Each product in VISUELLE is equipped with multimodal information: its image, textual metadata, sales after the first release date, and three related Google Trends describing category, color and fabric popularity.\r\n\r\nDownload  <a href=\"https://drive.google.com/file/d/11Bn2efKfO_PbtdqsSqj8U6y6YgBlRcP6/view?usp=sharing\">here</a>\r\n\r\nImage source: [https://arxiv.org/pdf/2109.09824v1.pdf](https://arxiv.org/pdf/2109.09824v1.pdf)","description_withheld":null,"homepage":"https://github.com/HumaticsLAB/GTM-Transformer","introduced_date":"2021-09-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/well-googled-is-half-done-multimodal","title":"Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends","first_author":"Geri Skenderi","url":null},"license":{"name":"MIT License","url":"https://github.com/HumaticsLAB/GTM-Transformer/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"New Product Sales Forecasting","url":"/task/new-product-sales-forecasting","datasets_with_task":"/datasets/task/new-product-sales-forecasting"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["VISUELLE"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/new-product-sales-forecasting-on-visuelle","task":"New Product Sales Forecasting","dataset_variant":"VISUELLE","rows":5,"metrics":["MAE","WAPE"],"first_row_in_archive_order":{"model":"GTM-Transformer [POP]","paper":"/paper/pop-mining-potential-performance-of-new","metrics":{"MAE":"28.62","WAPE":"52.39"},"code_links":[{"title":"humaticslab/pop-mining-potential-performance","url":"https://github.com/humaticslab/pop-mining-potential-performance"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/pop-mining-potential-performance-of-new","title":"POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion","date":"2022-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multimodal-quasi-autoregression-forecasting","title":"Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products","date":"2022-04-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/well-googled-is-half-done-multimodal","title":"Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends","date":"2021-09-20","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"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."}