{"url":"/dataset/istd-1","name":"ISTD+","full_name":null,"description_markdown":"ISTD+ consists of shadow images, shadow-free images, and shadow masks, with 1,330 training images and 540 testing images from 135 unique background scenes. ISTD suffers from color and luminosity inconsistencies between shadow and shadow-free images, which ISTD+ corrects with a color compensation mechanism to ensure uniform pixel colors across the ground-truth images.","description_withheld":null,"homepage":"https://drive.google.com/file/d/1rsCSWrotVnKFUqu9A_Nw9Uf-bJq_ryOv/view","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"Shadow Removal","url":"/task/shadow-removal","datasets_with_task":"/datasets/task/shadow-removal"}],"languages":[],"variants":["ISTD+"],"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/shadow-removal-on-istd-1","task":"Shadow Removal","dataset_variant":"ISTD+","rows":26,"metrics":["RMSE","PSNR","SSIM","LPIPS"],"first_row_in_archive_order":{"model":"RASM","paper":"/paper/regional-attention-for-shadow-removal","metrics":{"RMSE":"2.53"},"code_links":[{"title":"CalcuLuUus/RASM","url":"https://github.com/CalcuLuUus/RASM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/regional-attention-for-shadow-removal","title":"Regional Attention for Shadow Removal","date":"2024-11-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/shadowmaskformer-mask-augmented-patch","title":"ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal","date":"2024-04-29","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/homoformer-homogenized-transformer-for-image","title":"HomoFormer: Homogenized Transformer for Image Shadow Removal","date":"2024-01-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/shadowformer-global-context-helps-image","title":"ShadowFormer: Global Context Helps Image Shadow Removal","date":"2023-02-03","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/shadowdiffusion-when-degradation-prior-meets","title":"ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal","date":"2022-12-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/style-guided-shadow-removal","title":"Style-Guided Shadow Removal","date":"2022-11-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/dc-shadownet-single-image-hard-and-soft-1","title":"DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network","date":"2022-07-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/bijective-mapping-network-for-shadow-removal","title":"Bijective Mapping Network for Shadow Removal","date":"2022-01-01","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/from-shadow-generation-to-shadow-removal","title":"From Shadow Generation to Shadow Removal","date":"2021-03-24","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/auto-exposure-fusion-for-single-image-shadow","title":"Auto-Exposure Fusion for Single-Image Shadow Removal","date":"2021-03-01","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/shadow-removal-via-shadow-image-decomposition","title":"Shadow Removal via Shadow Image Decomposition","date":"2019-08-23","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":0,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mask-shadowgan-learning-to-remove-shadows","title":"Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data","date":"2019-03-26","rows_on_this_dataset":2,"code_links":5,"syntology":null},{"paper":"/paper/direction-aware-spatial-context-features-for-1","title":"Direction-aware Spatial Context Features for Shadow Detection and Removal","date":"2018-05-12","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/stacked-conditional-generative-adversarial","title":"Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal","date":"2017-12-07","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":21,"samples_ran":5,"samples_unverified":16,"pointer_only_for_licence":8,"papers_with_no_sample_that_ran":1,"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."}