{"url":"/dataset/aflw","name":"AFLW","full_name":"Annotated Facial Landmarks in the Wild","description_markdown":"The **Annotated Facial Landmarks in the Wild** (**AFLW**) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image.\r\n\r\nSource: [Nose, Eyes and Ears: Head Pose Estimation by Locating Facial Keypoints](https://arxiv.org/abs/1812.00739)","description_withheld":null,"homepage":"https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/","introduced_date":"2011-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization","first_author":null,"url":"https://doi.org/10.1109/ICCVW.2011.6130513"},"license":{"name":"Custom (non-commercial)","url":"https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/#license"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Face Alignment","url":"/task/face-alignment","datasets_with_task":"/datasets/task/face-alignment"},{"name":"Low-Light Image Enhancement","url":"/task/low-light-image-enhancement","datasets_with_task":"/datasets/task/low-light-image-enhancement"},{"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"},{"name":"Unsupervised Facial Landmark Detection","url":"/task/unsupervised-facial-landmark-detection","datasets_with_task":"/datasets/task/unsupervised-facial-landmark-detection"}],"languages":[],"variants":["AFLW-PIFA (34 points)","AFLW-PIFA (21 points)","AFLW-MTFL","AFLW-LFPA","AFLW-Full","AFLW-Front","AFLW (Zhang CVPR 2018 crops)","AFLW"],"data_loaders":[{"repo":"https://github.com/open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose/blob/master/docs/tasks/2d_face_keypoint.md#aflw-dataset","frameworks":["pytorch"]}],"num_papers_in_archive":154,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/head-pose-estimation-on-aflw","task":"Head Pose Estimation","dataset_variant":"AFLW","rows":6,"metrics":["MAE"],"first_row_in_archive_order":{"model":"MNN","paper":"/paper/multi-task-head-pose-estimation-in-the-wild-1","metrics":{"MAE":"3.22"},"code_links":[{"title":"bobetocalo/bobetocalo_pami20","url":"https://github.com/bobetocalo/bobetocalo_pami20"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-landmark-detection-on-aflw-full","task":"Facial Landmark Detection","dataset_variant":"AFLW-Full","rows":5,"metrics":["Mean NME ","Mean NME","NME"],"first_row_in_archive_order":{"model":"FiFA","paper":"/paper/fiducial-focus-augmentation-for-facial","metrics":{"Mean NME":"0.92","Mean NME ":"0.92","NME":"0.92"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-facial-landmark-detection-on-2","task":"Unsupervised Facial Landmark Detection","dataset_variant":"AFLW (Zhang CVPR 2018 crops)","rows":4,"metrics":["NME"],"first_row_in_archive_order":{"model":"Conditional Image Generation","paper":"/paper/unsupervised-learning-of-object-landmarks","metrics":{"NME":"6.31"},"code_links":[{"title":"tomasjakab/imm","url":"https://github.com/tomasjakab/imm"},{"title":"hqng/imm-pytorch","url":"https://github.com/hqng/imm-pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-alignment-on-aflw","task":"Face Alignment","dataset_variant":"AFLW","rows":3,"metrics":["Mean NME"],"first_row_in_archive_order":{"model":"SynergyNet","paper":"/paper/synergy-between-3dmm-and-3d-landmarks-for","metrics":{"Mean NME":"4.06"},"code_links":[{"title":"tomas-gajarsky/facetorch","url":"https://github.com/tomas-gajarsky/facetorch"},{"title":"choyingw/SynergyNet","url":"https://github.com/choyingw/SynergyNet"},{"title":"gai-shaoyan/mind3d","url":"https://gitee.com/gai-shaoyan/mind3d"},{"title":"Hikaylee/SynergyNet","url":"https://github.com/Hikaylee/SynergyNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-alignment-on-aflw-lfpa","task":"Face Alignment","dataset_variant":"AFLW-LFPA","rows":3,"metrics":["Mean NME ","NME"],"first_row_in_archive_order":{"model":"FPN","paper":"/paper/joint-3d-face-reconstruction-and-dense","metrics":{"Mean NME ":"2.93%"},"code_links":[{"title":"YadiraF/PRNet","url":"https://github.com/YadiraF/PRNet"},{"title":"jimmy0087/faceai-master","url":"https://github.com/jimmy0087/faceai-master"},{"title":"minoring/PRNet","url":"https://github.com/minoring/PRNet"},{"title":"heathentw/prnet-tf2","url":"https://github.com/heathentw/prnet-tf2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-landmark-detection-on-aflw-front","task":"Facial Landmark Detection","dataset_variant":"AFLW-Front","rows":3,"metrics":["Mean NME","Mean NME ","NME"],"first_row_in_archive_order":{"model":"FiFA","paper":"/paper/fiducial-focus-augmentation-for-facial","metrics":{"Mean NME":"0.80","Mean NME ":"0.80","NME":"0.80"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-facial-landmark-detection-on-3","task":"Unsupervised Facial Landmark Detection","dataset_variant":"AFLW-MTFL","rows":3,"metrics":["NME"],"first_row_in_archive_order":{"model":"DVE","paper":"/paper/unsupervised-learning-of-landmarks-by","metrics":{"NME":"7.53"},"code_links":[{"title":"jamt9000/DVE","url":"https://github.com/jamt9000/DVE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-alignment-on-aflw-full-1","task":"Face Alignment","dataset_variant":"AFLW-Full","rows":2,"metrics":["Mean NME ","Mean NME"],"first_row_in_archive_order":{"model":"Binary Face Alignment","paper":"/paper/binarized-convolutional-landmark-localizers","metrics":{"Mean NME ":"2.85"},"code_links":[{"title":"1adrianb/binary-human-pose-estimation","url":"https://github.com/1adrianb/binary-human-pose-estimation"},{"title":"1adrianb/binary-face-alignment","url":"https://github.com/1adrianb/binary-face-alignment"},{"title":"1adrianb/binary-networks-pytorch","url":"https://github.com/1adrianb/binary-networks-pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-alignment-on-aflw-pifa-21-points-1","task":"Face Alignment","dataset_variant":"AFLW-PIFA (21 points)","rows":1,"metrics":["NME"],"first_row_in_archive_order":{"model":"Face alignment","paper":"/paper/convolutional-aggregation-of-local-evidence","metrics":{"NME":"2.63%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-alignment-on-aflw-pifa-34-points-1","task":"Face Alignment","dataset_variant":"AFLW-PIFA (34 points)","rows":1,"metrics":["NME"],"first_row_in_archive_order":{"model":"Face alignment","paper":"/paper/convolutional-aggregation-of-local-evidence","metrics":{"NME":"2.96%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/low-light-image-enhancement-on-aflw-zhang","task":"Low-Light Image Enhancement","dataset_variant":"AFLW (Zhang CVPR 2018 crops)","rows":1,"metrics":["14 gestures accuracy"],"first_row_in_archive_order":{"model":"enligh","paper":"/paper/enlightengan-deep-light-enhancement-without","metrics":{"14 gestures accuracy":"1"},"code_links":[{"title":"kritiksoman/GIMP-ML","url":"https://github.com/kritiksoman/GIMP-ML"},{"title":"yueruchen/EnlightenGAN","url":"https://github.com/yueruchen/EnlightenGAN"},{"title":"VITA-Group/EnlightenGAN","url":"https://github.com/VITA-Group/EnlightenGAN"},{"title":"arsenyinfo/EnlightenGAN-inference","url":"https://github.com/arsenyinfo/EnlightenGAN-inference"},{"title":"wkhademi/ImageEnhancement","url":"https://github.com/wkhademi/ImageEnhancement"},{"title":"bchao1/awesome-image-enhancement","url":"https://github.com/bchao1/awesome-image-enhancement"},{"title":"yaegasikk/howtoengan","url":"https://github.com/yaegasikk/howtoengan"},{"title":"del1and/openSW_Detector","url":"https://github.com/del1and/openSW_Detector"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fiducial-focus-augmentation-for-facial","title":"Fiducial Focus Augmentation for Facial Landmark Detection","date":"2024-02-23","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/synergy-between-3dmm-and-3d-landmarks-for","title":"Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry","date":"2021-10-19","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/multi-task-head-pose-estimation-in-the-wild-1","title":"Multi-task head pose estimation in-the-wild","date":"2020-12-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-fast-accurate-and-stable-3d-dense-1","title":"Towards Fast, Accurate and Stable 3D Dense Face Alignment","date":"2020-09-21","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; 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