{"url":"/dataset/lip","name":"LIP","full_name":"Look into Person","description_markdown":"The **LIP** (**Look into Person**) dataset is a large-scale dataset focusing on semantic understanding of a person. It contains 50,000 images with elaborated pixel-wise annotations of 19 semantic human part labels and 2D human poses with 16 key points. The images are collected from real-world scenarios and the subjects appear with challenging poses and view, heavy occlusions, various appearances and low resolution.\r\n\r\nSource: [http://sysu-hcp.net/lip/](http://sysu-hcp.net/lip/)\r\nImage Source: [http://sysu-hcp.net/lip/](http://sysu-hcp.net/lip/)","description_withheld":null,"homepage":"http://sysu-hcp.net/lip/","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/look-into-person-self-supervised-structure","title":"Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing","first_author":"Ke Gong","url":null},"license":{"name":"Custom (research, non-research, non-commercial)","url":"http://sysu-hcp.net/lip/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["LIP val","LIP"],"data_loaders":[{"repo":"https://github.com/Graviti-AI/datasets","url":"https://gas.graviti.com/dataset/hellodataset/LIP","frameworks":["tf","pytorch"]}],"num_papers_in_archive":61,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-lip-val","task":"Semantic Segmentation","dataset_variant":"LIP val","rows":13,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"Hulk(Finetune, ViT-L)","paper":"/paper/hulk-a-universal-knowledge-translator-for","metrics":{"mIoU":"66.02%"},"code_links":[{"title":"opengvlab/humanbench","url":"https://github.com/opengvlab/humanbench"},{"title":"opengvlab/hulk","url":"https://github.com/opengvlab/hulk"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hulk-a-universal-knowledge-translator-for","title":"Hulk: A Universal Knowledge Translator for Human-Centric Tasks","date":"2023-12-04","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":12,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/beyond-appearance-a-semantic-controllable","title":"Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks","date":"2023-03-30","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unihcp-a-unified-model-for-human-centric","title":"UniHCP: A Unified Model for Human-Centric Perceptions","date":"2023-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/object-contextual-representations-for","title":"Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation","date":"2019-09-24","rows_on_this_dataset":3,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":4,"samples_unverified":5,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/high-resolution-representations-for-labeling","title":"High-Resolution Representations for Labeling Pixels and Regions","date":"2019-04-09","rows_on_this_dataset":1,"code_links":39,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":3,"samples_unverified":15,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/devil-in-the-details-towards-accurate-single","title":"Devil in the Details: Towards Accurate Single and Multiple Human Parsing","date":"2018-09-17","rows_on_this_dataset":1,"code_links":2,"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."}},{"paper":"/paper/mutual-learning-to-adapt-for-joint-human","title":"Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation","date":"2018-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/macro-micro-adversarial-network-for-human","title":"Macro-Micro Adversarial Network for Human Parsing","date":"2018-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/look-into-person-joint-body-parsing-pose","title":"Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark","date":"2018-04-05","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/look-into-person-self-supervised-structure","title":"Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing","date":"2017-03-16","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":67,"samples_ran":28,"samples_unverified":39,"pointer_only_for_licence":8,"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."}