{"url":"/dataset/vidit","name":"VIDIT","full_name":"Virtual Image Dataset for Illumination Transfer","description_markdown":"**VIDIT**  is a reference evaluation benchmark and to push forward the development of illumination manipulation methods. VIDIT includes 390 different Unreal Engine scenes, each captured with 40 illumination settings, resulting in 15,600 images. The illumination settings are all the combinations of 5 color temperatures (2500K, 3500K, 4500K, 5500K and 6500K) and 8 light directions (N, NE, E, SE, S, SW, W, NW). Original image resolution is 1024x1024.\r\n\r\nSource: [VIDIT: Virtual Image Dataset for Illumination Transfer](/paper/vidit-virtual-image-dataset-for-illumination)\r\n\r\nImage source: [https://github.com/majedelhelou/VIDIT](https://github.com/majedelhelou/VIDIT)","description_withheld":null,"homepage":"https://github.com/majedelhelou/VIDIT","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/vidit-virtual-image-dataset-for-illumination","title":"VIDIT: Virtual Image Dataset for Illumination Transfer","first_author":"Majed El Helou","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Image-to-Image Translation","url":"/task/image-to-image-translation","datasets_with_task":"/datasets/task/image-to-image-translation"},{"name":"Image Relighting","url":"/task/image-relighting","datasets_with_task":"/datasets/task/image-relighting"},{"name":"SSIM","url":"/task/ssim","datasets_with_task":"/datasets/task/ssim"}],"languages":[],"variants":["VIDIT"," VIDIT’20 validation set"],"data_loaders":[{"repo":"https://github.com/majedelhelou/VIDIT","url":"https://github.com/majedelhelou/VIDIT","frameworks":[]}],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-relighting-on-vidit20-validation-set","task":"Image Relighting","dataset_variant":"VIDIT’20 validation set","rows":5,"metrics":["PSNR","SSIM","LPIPS","MPS","Runtime(s)"],"first_row_in_archive_order":{"model":"OIDDR-Net","paper":"/paper/physically-inspired-dense-fusion-networks-for","metrics":{"LPIPS":"0.2733","MPS":"0.6956","PSNR":"17.62","Runtime(s)":"0.53","SSIM":"0.6645"},"code_links":[{"title":"yazdaniamir38/Depth-guided-Image-Relighting","url":"https://github.com/yazdaniamir38/Depth-guided-Image-Relighting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/physically-inspired-dense-fusion-networks-for","title":"Physically Inspired Dense Fusion Networks for Relighting","date":"2021-05-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dsrn-an-efficient-deep-network-for-image","title":"DSRN: an Efficient Deep Network for Image Relighting","date":"2021-02-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/wdrn-a-wavelet-decomposed-relightnet-for","title":"WDRN : A Wavelet Decomposed RelightNet for Image Relighting","date":"2020-09-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-relighting-networks-for-image-light","title":"Deep Relighting Networks for Image Light Source Manipulation","date":"2020-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scale-recurrent-network-for-deep-image","title":"Scale-recurrent Network for Deep Image Deblurring","date":"2018-02-06","rows_on_this_dataset":1,"code_links":4,"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."}