{"url":"/dataset/vggface2-1","name":"VGGFace2","full_name":"Vggface2: A dataset for recognising faces  across pose and age","description_markdown":"VGGFace2 is a large-scale face recognition dataset. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession. VGGFace2 contains images from identities spanning a wide range of different ethnicities, accents, professions and ages. All face images are captured \"in the wild\", with pose and emotion variations and different lighting and occlusion conditions. Face distribution for different identities is varied, from 87 to 843, with an average of 362 images for each subject.","description_withheld":null,"homepage":"https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/","introduced_date":"2017-10-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/vggface2-a-dataset-for-recognising-faces","title":"VGGFace2: A dataset for recognising faces across pose and age","first_author":"Qiong Cao","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Facial Inpainting","url":"/task/facial-inpainting","datasets_with_task":"/datasets/task/facial-inpainting"},{"name":"Image Attribution","url":"/task/image-attribution","datasets_with_task":"/datasets/task/image-attribution"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["VggFace2","VGGFace2 (2.3M)","VggFace2 - 8x upscaling","VGGFace2"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/vgg_face2","frameworks":["tf","jax"]}],"num_papers_in_archive":539,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-attribution-on-vggface2","task":"Image Attribution","dataset_variant":"VGGFace2","rows":8,"metrics":["Insertion AUC score (ArcFace ResNet-101)","Deletion AUC score (ArcFace ResNet-101)"],"first_row_in_archive_order":{"model":"SMDL-Attribution (ICLR version)","paper":"/paper/less-is-more-fewer-interpretable-region-via","metrics":{"Deletion AUC score (ArcFace ResNet-101)":"0.1304","Insertion AUC score (ArcFace ResNet-101)":"0.6705"},"code_links":[{"title":"ruoyuchen10/smdl-attribution","url":"https://github.com/ruoyuchen10/smdl-attribution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-vggface2-8x","task":"Image Super-Resolution","dataset_variant":"VggFace2 - 8x upscaling","rows":7,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Full-GWAInet","paper":"/paper/exemplar-guided-face-image-super-resolution","metrics":{"PSNR":"25.57"},"code_links":[{"title":"berkdogan2/GWAInet","url":"https://github.com/berkdogan2/GWAInet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-inpainting-on-vggface2","task":"Facial Inpainting","dataset_variant":"VggFace2","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"SymmFCNet (Full)","paper":"/paper/learning-symmetry-consistent-deep-cnns-for","metrics":{"PSNR":"27.81"},"code_links":[{"title":"csxmli2016/SymmFCNet","url":"https://github.com/csxmli2016/SymmFCNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/less-is-more-fewer-interpretable-region-via","title":"Less is More: Fewer Interpretable Region via Submodular Subset Selection","date":"2024-02-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/making-sense-of-dependence-efficient-black","title":"Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure","date":"2022-06-13","rows_on_this_dataset":1,"code_links":1,"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/exemplar-guided-face-image-super-resolution","title":"Exemplar Guided Face Image Super-Resolution without Facial Landmarks","date":"2019-06-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-symmetry-consistent-deep-cnns-for","title":"Learning Symmetry Consistent Deep CNNs for Face Completion","date":"2018-12-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rise-randomized-input-sampling-for","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","date":"2018-06-19","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":34,"samples_ran":1,"samples_unverified":33,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-warped-guidance-for-blind-face","title":"Learning Warped Guidance for Blind Face Restoration","date":"2018-04-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wavelet-srnet-a-wavelet-based-cnn-for-multi","title":"Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution","date":"2017-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hallucinating-very-low-resolution-unaligned","title":"Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders","date":"2017-07-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-unified-approach-to-interpreting-model","title":"A Unified Approach to Interpreting Model Predictions","date":"2017-05-22","rows_on_this_dataset":1,"code_links":17,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","rows_on_this_dataset":1,"code_links":40,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":56,"samples_ran":36,"samples_unverified":20,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/grad-cam-visual-explanations-from-deep","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","date":"2016-10-07","rows_on_this_dataset":1,"code_links":126,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":141,"samples_ran":79,"samples_unverified":62,"pointer_only_for_licence":68,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/photo-realistic-single-image-super-resolution","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","date":"2016-09-15","rows_on_this_dataset":1,"code_links":140,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":72,"samples_ran":16,"samples_unverified":56,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-cascaded-bi-network-for-face","title":"Deep Cascaded Bi-Network for Face Hallucination","date":"2016-07-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/why-should-i-trust-you-explaining-the","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","date":"2016-02-16","rows_on_this_dataset":1,"code_links":27,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":5,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/accurate-image-super-resolution-using-very","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","date":"2015-11-14","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-inside-convolutional-networks","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","date":"2013-12-20","rows_on_this_dataset":1,"code_links":23,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":10,"samples_harvested":344,"samples_ran":145,"samples_unverified":199,"pointer_only_for_licence":117,"papers_with_no_sample_that_ran":1,"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."}