{"url":"/dataset/perseg","name":"PerSeg","full_name":null,"description_markdown":"PerSeg is a dataset for personalized segmentation. The raw images are collect from the training data of subject driven diffusion models: DreamBooth, Textual Inversion, and Custom Diffusion. PerSeg contains 40 objects of various categories in total, including daily necessities, animals, and buildings. Contextualized in different poses or scenes, each object is related with 5∼7 images with our annotated masks.\r\n\r\nSource: [Personalize Segment Anything Model with One Shot](https://arxiv.org/pdf/2305.03048v1.pdf)\r\n\r\nImage Source: [Personalize Segment Anything Model with One Shot](https://arxiv.org/pdf/2305.03048v1.pdf)","description_withheld":null,"homepage":"https://github.com/ZrrSkywalker/Personalize-SAM","introduced_date":"2023-05-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/personalize-segment-anything-model-with-one","title":"Personalize Segment Anything Model with One Shot","first_author":"Renrui Zhang","url":null},"license":{"name":"MIT License","url":"https://github.com/ZrrSkywalker/Personalize-SAM/blob/main/LICENSE.txt"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Personalized Segmentation","url":"/task/personalized-segmentation","datasets_with_task":"/datasets/task/personalized-segmentation"}],"languages":[],"variants":["PerSeg"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/personalized-segmentation-on-perseg","task":"Personalized Segmentation","dataset_variant":"PerSeg","rows":6,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"P^2SAM","paper":"/paper/part-aware-personalized-segment-anything","metrics":{"mIoU":"95.66"},"code_links":[{"title":"Zch0414/P2SAM","url":"https://github.com/Zch0414/P2SAM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/part-aware-personalized-segment-anything","title":"Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation","date":"2024-03-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/personalize-segment-anything-model-with-one","title":"Personalize Segment Anything Model with One Shot","date":"2023-05-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/images-speak-in-images-a-generalist-painter","title":"Images Speak in Images: A Generalist Painter for In-Context Visual Learning","date":"2022-12-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/visual-prompting-via-image-inpainting","title":"Visual Prompting via Image Inpainting","date":"2022-09-01","rows_on_this_dataset":1,"code_links":1,"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."}