{"url":"/dataset/wflw","name":"WFLW","full_name":"Wider Facial Landmarks in the Wild","description_markdown":"The **Wider Facial Landmarks in the Wild** or **WFLW** database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions.\r\n\r\nSource: [Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion](https://arxiv.org/abs/2004.06558)\r\nImage Source: [https://wywu.github.io/projects/LAB/WFLW.html](https://wywu.github.io/projects/LAB/WFLW.html)","description_withheld":null,"homepage":"https://wywu.github.io/projects/LAB/WFLW.html","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/look-at-boundary-a-boundary-aware-face","title":"Look at Boundary: A Boundary-Aware Face Alignment Algorithm","first_author":"Wayne Wu","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Face Alignment","url":"/task/face-alignment","datasets_with_task":"/datasets/task/face-alignment"},{"name":"Facial Landmark Detection","url":"/task/facial-landmark-detection","datasets_with_task":"/datasets/task/facial-landmark-detection"},{"name":"Head Pose Estimation","url":"/task/head-pose-estimation","datasets_with_task":"/datasets/task/head-pose-estimation"}],"languages":[],"variants":["WFLW","WFW (Extra Data)"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/wflw-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":108,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-alignment-on-wflw","task":"Face Alignment","dataset_variant":"WFLW","rows":36,"metrics":["NME (inter-ocular)","AUC@10 (inter-ocular)","FR@10 (inter-ocular)"],"first_row_in_archive_order":{"model":"SH-FAN","paper":"/paper/subpixel-heatmap-regression-for-facial","metrics":{"AUC@10 (inter-ocular)":"63.81","FR@10 (inter-ocular)":"1.55","NME (inter-ocular)":"3.72"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-alignment-on-wfw-extra-data","task":"Face Alignment","dataset_variant":"WFW (Extra Data)","rows":11,"metrics":["NME (inter-ocular)","AUC@10 (inter-ocular)","FR@10 (inter-ocular)"],"first_row_in_archive_order":{"model":"SH-FAN","paper":"/paper/subpixel-heatmap-regression-for-facial","metrics":{"AUC@10 (inter-ocular)":"63.1","FR@10 (inter-ocular)":"1.55","NME (inter-ocular)":"3.72"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-landmark-detection-on-wflw-1","task":"Facial Landmark Detection","dataset_variant":"WFLW","rows":3,"metrics":["NME","NME (inter-ocular)","AUC@10 (inter-ocular)","FR@10 (inter-ocular)"],"first_row_in_archive_order":{"model":"D-ViT","paper":"/paper/cascaded-dual-vision-transformer-for-accurate","metrics":{"AUC@10 (inter-ocular)":"63.7","FR@10 (inter-ocular)":"1.76","NME":"3.75","NME (inter-ocular)":"3.75"},"code_links":[{"title":"Human3DAIGC/AccurateFacialLandmarkDetection","url":"https://github.com/Human3DAIGC/AccurateFacialLandmarkDetection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/head-pose-estimation-on-wflw","task":"Head Pose Estimation","dataset_variant":"WFLW","rows":2,"metrics":["MAE mean (º)","MAE yaw (º)","MAE pitch (º)","MAE roll (º)"],"first_row_in_archive_order":{"model":"SPIGA","paper":"/paper/shape-preserving-facial-landmarks-with-graph","metrics":{"MAE mean (º)":"1.52","MAE pitch (º)":"1.86","MAE roll (º)":"0.93","MAE yaw (º)":"1.78"},"code_links":[{"title":"andresprados/spiga","url":"https://github.com/andresprados/spiga"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cascaded-dual-vision-transformer-for-accurate","title":"Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection","date":"2024-11-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fiducial-focus-augmentation-for-facial","title":"Fiducial Focus Augmentation for Facial Landmark Detection","date":"2024-02-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/automated-detection-of-cat-facial-landmarks","title":"Automated Detection of Cat Facial Landmarks","date":"2023-10-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/star-loss-reducing-semantic-ambiguity-in-1","title":"STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection","date":"2023-06-05","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":4,"claim":"Per-sample execution on synthesized fixtures; 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