{"url":"/dataset/reds","name":"REDS","full_name":"REalistic and Diverse Scenes dataset\nrealistic and dynamic scenes","description_markdown":"The realistic and dynamic scenes (**REDS**) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively\n\nSource: [Video Super Resolution Based on Deep Learning: A comprehensive survey](https://arxiv.org/abs/2007.12928)\nImage Source: [https://seungjunnah.github.io/Datasets/reds.html](https://seungjunnah.github.io/Datasets/reds.html)","description_withheld":null,"homepage":"https://seungjunnah.github.io/Datasets/reds.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Deblurring","url":"/task/deblurring","datasets_with_task":"/datasets/task/deblurring"},{"name":"Joint Demosaicing and Denoising","url":"/task/joint-demosaicing-and-denoising","datasets_with_task":"/datasets/task/joint-demosaicing-and-denoising"}],"languages":[],"variants":["REDS"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/deblurring-on-reds","task":"Deblurring","dataset_variant":"REDS","rows":3,"metrics":["Average PSNR"],"first_row_in_archive_order":{"model":"VRT","paper":"/paper/vrt-a-video-restoration-transformer","metrics":{"Average PSNR":"36.79"},"code_links":[{"title":"jingyunliang/vrt","url":"https://github.com/jingyunliang/vrt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/vrt-a-video-restoration-transformer","title":"VRT: A Video Restoration Transformer","date":"2022-01-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/edvr-video-restoration-with-enhanced","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","date":"2019-05-07","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":5,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deblurgan-blind-motion-deblurring-using","title":"DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks","date":"2017-11-19","rows_on_this_dataset":1,"code_links":13,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":24,"samples_ran":9,"samples_unverified":15,"pointer_only_for_licence":5,"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."}