{"url":"/dataset/lapa","name":"LaPa","full_name":null,"description_markdown":"A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.\r\n\r\nSource: [LaPa](https://github.com/JDAI-CV/lapa-dataset)","description_withheld":null,"homepage":"https://github.com/JDAI-CV/lapa-dataset","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Face Parsing","url":"/task/face-parsing","datasets_with_task":"/datasets/task/face-parsing"}],"languages":[],"variants":["LaPa"],"data_loaders":[{"repo":"https://github.com/JDAI-CV/lapa-dataset","url":"https://github.com/JDAI-CV/lapa-dataset","frameworks":[]}],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-parsing-on-lapa","task":"Face Parsing","dataset_variant":"LaPa","rows":11,"metrics":["Mean F1"],"first_row_in_archive_order":{"model":"FaRL-B","paper":"/paper/general-facial-representation-learning-in-a","metrics":{"Mean F1":"93.88"},"code_links":[{"title":"FacePerceiver/FaRL","url":"https://github.com/FacePerceiver/FaRL"},{"title":"willyfh/farl-face-segmentation","url":"https://github.com/willyfh/farl-face-segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/segface-face-segmentation-of-long-tail","title":"SegFace: Face Segmentation of Long-Tail Classes","date":"2024-12-11","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/decoupled-multi-task-learning-with-cyclical","title":"Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing","date":"2022-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/general-facial-representation-learning-in-a","title":"General Facial Representation Learning in a Visual-Linguistic Manner","date":"2021-12-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fake-it-till-you-make-it-face-analysis-in-the","title":"Fake It Till You Make It: Face analysis in the wild using synthetic data alone","date":"2021-09-30","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/roi-tanh-polar-transformer-network-for-face","title":"RoI Tanh-polar Transformer Network for Face Parsing in the Wild","date":"2021-02-04","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/adaptive-graph-representation-learning-and","title":"AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing","date":"2021-01-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/edge-aware-graph-representation-learning-and","title":"Edge-aware Graph Representation Learning and Reasoning for Face Parsing","date":"2020-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ehanet-an-effective-hierarchical-aggregation","title":"EHANet: An Effective Hierarchical Aggregation Network for Face Parsing","date":"2020-04-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/a-new-dataset-and-boundary-attention-semantic","title":"A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing","date":"2020-04-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/accurate-facial-image-parsing-at-real-time","title":"Accurate facial image parsing at real-time speed","date":"2019-04-09","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":4,"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."}