{"url":"/dataset/pascal-person-part","name":"PASCAL-Part","full_name":"PASCAL-Part","description_markdown":"**PASCAL-Part** is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL object detection task by providing segmentation masks for each body part of the object. For categories that do not have a consistent set of parts (e.g., boat), it provides the silhouette annotation. \r\n\r\nIt can also serve as a set for human semantic part segmentation: It contains multiple humans per image in unconstrained poses and occlusions (1,716 for training and 1,817 for testing). It provides careful pixel-wise annotations for six body parts (i.e., head, torso, upper/lower-arms, and upper-/lower-legs).\r\n\r\nSource: [The Ultimate Theory of Human Parsing](https://arxiv.org/abs/2001.06804)\r\nImage Source: [https://www.researchgate.net/profile/Zhedong_Zheng/publication/328123707/figure/fig4/AS:704683136016384@1545020960225/Qualitative-parsing-results-on-the-Pascal-Person-Part-dataset.png](https://www.researchgate.net/profile/Zhedong_Zheng/publication/328123707/figure/fig4/AS:704683136016384@1545020960225/Qualitative-parsing-results-on-the-Pascal-Person-Part-dataset.png)","description_withheld":null,"homepage":"http://roozbehm.info/pascal-parts/pascal-parts.html","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/detect-what-you-can-detecting-and","title":"Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts","first_author":"Xianjie Chen","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Human Part Segmentation","url":"/task/human-part-segmentation","datasets_with_task":"/datasets/task/human-part-segmentation"},{"name":"Multi-Human Parsing","url":"/task/multi-human-parsing","datasets_with_task":"/datasets/task/multi-human-parsing"},{"name":"Semantic Part Detection","url":"/task/semantic-part-detection","datasets_with_task":"/datasets/task/semantic-part-detection"}],"languages":[],"variants":["PASCAL-Part","PASCAL Part 2010 - Animals"],"data_loaders":[],"num_papers_in_archive":65,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/human-part-segmentation-on-pascal-person-part","task":"Human Part Segmentation","dataset_variant":"PASCAL-Part","rows":7,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"CDCL+Pascal","paper":"/paper/cross-domain-complementary-learning-with","metrics":{"mIoU":"72.82"},"code_links":[{"title":"kevinlin311tw/CDCL-human-part-segmentation","url":"https://github.com/kevinlin311tw/CDCL-human-part-segmentation"},{"title":"ytx21cn/CDCL-human-seg","url":"https://github.com/ytx21cn/CDCL-human-seg"},{"title":"yuqizhu/covid19-classification","url":"https://github.com/yuqizhu/covid19-classification"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-human-parsing-on-pascal-person-part","task":"Multi-Human Parsing","dataset_variant":"PASCAL-Part","rows":3,"metrics":["AP 0.5"],"first_row_in_archive_order":{"model":"NAN","paper":"/paper/understanding-humans-in-crowded-scenes-deep","metrics":{"AP 0.5":"59.70%"},"code_links":[{"title":"open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose"},{"title":"ZhaoJ9014/Multi-Human-Parsing","url":"https://github.com/ZhaoJ9014/Multi-Human-Parsing"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-detection-on-pascal-part-2010-animals","task":"Object Detection","dataset_variant":"PASCAL Part 2010 - Animals","rows":1,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"Attention-based Joint Detection of Object and Semantic Part","paper":"/paper/attention-based-joint-detection-of-object-and","metrics":{"mAP@0.5":"87.5"},"code_links":[{"title":"kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch","url":"https://github.com/kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semantic-part-detection-on-pascal-part-2010","task":"Semantic Part Detection","dataset_variant":"PASCAL Part 2010 - Animals","rows":1,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"Attention-based Joint Detection of Object and Semantic Part","paper":"/paper/attention-based-joint-detection-of-object-and","metrics":{"mAP@0.5":"52.0"},"code_links":[{"title":"kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch","url":"https://github.com/kevalmorabia97/Object-and-Semantic-Part-Detection-pyTorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/attention-based-joint-detection-of-object-and","title":"Attention-based Joint Detection of Object and Semantic Part","date":"2020-07-05","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-correction-for-human-parsing","title":"Self-Correction for Human Parsing","date":"2019-10-22","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cross-domain-complementary-learning-with","title":"Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation","date":"2019-07-11","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/weakly-and-semi-supervised-human-body-part","title":"Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer","date":"2018-05-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/understanding-humans-in-crowded-scenes-deep","title":"Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing","date":"2018-04-10","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/holistic-instance-level-human-parsing","title":"Holistic, Instance-Level Human Parsing","date":"2017-09-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-multi-person-pose-estimation-and","title":"Joint Multi-Person Pose Estimation and Semantic Part Segmentation","date":"2017-08-10","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/instance-aware-semantic-segmentation-via","title":"Instance-aware Semantic Segmentation via Multi-task Network Cascades","date":"2015-12-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/zoom-better-to-see-clearer-human-and-object","title":"Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net","date":"2015-11-21","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":15,"samples_ran":5,"samples_unverified":10,"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."}