{"url":"/dataset/lpr4m","name":"LPR4M","full_name":"Livestreaming Product Recognition 4M","description_markdown":"**LPR4M** is a large-scale live commerce dataset, offering a significantly broader coverage of categories and diverse modalities such as video, image, and text. It contains 4M exactly matched〈clip, image〉pairs of 4M live clips, and 332k shop images. Each image has 12 clips with different product variations, e.g., viewpoint, scale, and occlusion.","description_withheld":null,"homepage":"https://github.com/adxcreative/rice","introduced_date":"2023-08-09","introduced_date_note":null,"introduced_by":{"paper":"/paper/cross-view-semantic-alignment-for","title":"Cross-view Semantic Alignment for Livestreaming Product Recognition","first_author":"Wenjie Yang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["LPR4M"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}