{"url":"/dataset/imgedit-data","name":"ImgEdit-Data","full_name":null,"description_markdown":"ImgEdit is a large-scale, high-quality image-editing dataset comprising 1.2 million carefully curated edit pairs, which contain both novel and complex single-turn edits, as well as challenging multi-turn tasks.","description_withheld":null,"homepage":"https://github.com/PKU-YuanGroup/ImgEdit","introduced_date":"2025-05-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/imgedit-a-unified-image-editing-dataset-and","title":"ImgEdit: A Unified Image Editing Dataset and Benchmark","first_author":"Yang Ye","url":null},"license":{"name":"MIT","url":"https://github.com/PKU-YuanGroup/ImgEdit"},"modalities":[],"tasks":[{"name":"Image Editing","url":"/task/image-editing","datasets_with_task":"/datasets/task/image-editing"},{"name":"Text-based Image Editing","url":"/task/text-based-image-editing","datasets_with_task":"/datasets/task/text-based-image-editing"}],"languages":[],"variants":["ImgEdit-Data"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-editing-on-imgedit-data","task":"Image Editing","dataset_variant":"ImgEdit-Data","rows":9,"metrics":["Overall","Add","Adjust","Extract","Replace","Remove","Background","Style","Hybrid","Action"],"first_row_in_archive_order":{"model":"BAGEL-NHR-EDIT","paper":"/paper/nohumansrequired-autonomous-high-quality","metrics":{"Action":"3.95","Add":"4.19","Adjust":"3.55","Background":"3.42","Extract":"1.62","Hybrid":"2.94","Overall":"3.39","Remove":"3.18","Replace":"3.77","Style":"4.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/nohumansrequired-autonomous-high-quality","title":"NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining","date":"2025-07-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/uniworld-v1-high-resolution-semantic-encoders","title":"UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation","date":"2025-06-03","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/emerging-properties-in-unified-multimodal","title":"Emerging Properties in Unified Multimodal Pretraining","date":"2025-05-20","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":9,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/step1x-edit-a-practical-framework-for-general","title":"Step1X-Edit: A Practical Framework for General Image Editing","date":"2025-04-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/anyedit-edit-any-knowledge-encoded-in","title":"AnyEdit: Edit Any Knowledge Encoded in Language Models","date":"2025-02-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/ultraedit-instruction-based-fine-grained","title":"UltraEdit: Instruction-based Fine-Grained Image Editing at Scale","date":"2024-07-07","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":10,"samples_unverified":7,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/in-context-editing-learning-knowledge-from","title":"In-Context Editing: Learning Knowledge from Self-Induced Distributions","date":"2024-06-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/magicbrush-a-manually-annotated-dataset-for","title":"MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing","date":"2023-06-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/instructpix2pix-learning-to-follow-image","title":"InstructPix2Pix: Learning to Follow Image Editing Instructions","date":"2022-11-17","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":15,"samples_unverified":5,"pointer_only_for_licence":0,"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":5,"samples_harvested":70,"samples_ran":43,"samples_unverified":27,"pointer_only_for_licence":25,"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; 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