{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ms-celeb-1m-a-dataset-and-benchmark-for-large","title":"MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition","arxiv_id":"1607.08221","date":"2016-07-27","proceeding":null,"authors":["Yandong Guo","Lei Zhang","Yuxiao Hu","Xiaodong He","Jianfeng Gao"],"abstract":"In this paper, we design a benchmark task and provide the associated datasets\nfor recognizing face images and link them to corresponding entity keys in a\nknowledge base. More specifically, we propose a benchmark task to recognize one\nmillion celebrities from their face images, by using all the possibly collected\nface images of this individual on the web as training data. The rich\ninformation provided by the knowledge base helps to conduct disambiguation and\nimprove the recognition accuracy, and contributes to various real-world\napplications, such as image captioning and news video analysis. Associated with\nthis task, we design and provide concrete measurement set, evaluation protocol,\nas well as training data. We also present in details our experiment setup and\nreport promising baseline results. Our benchmark task could lead to one of the\nlargest classification problems in computer vision. To the best of our\nknowledge, our training dataset, which contains 10M images in version 1, is the\nlargest publicly available one in the world.","url_abs":"http://arxiv.org/abs/1607.08221v1","url_pdf":"http://arxiv.org/pdf/1607.08221v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/394781865/insightface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/KikimorMay/MultiFace","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/TreB1eN/InsightFace_Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/chenggongliang/arcface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/cvtower/seesawfacenet_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/deepinsight/insightface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/eric-erki/insightface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/melgor/pyface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/nu11us/WC-MS-Celeb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/sunil-rival/insightface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/zrui94/insight_mx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"ms-celeb-1m-a-dataset-and-benchmark-for-large","repo_url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"image-captioning","task_name":"Image Captioning"}],"methods":[],"datasets_introduced":[{"slug":"ms-celeb-1m","name":"MS-Celeb-1M","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1607.08221","atlas_url":"https://app.syntology.ai/?focus=1607.08221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.08221"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/deepinsight/insightface","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cvtower/seesawfacenet_pytorch","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nu11us/WC-MS-Celeb","reach":{"status":"ok","spdx":"GPL-3.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/394781865/insightface","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/TreB1eN/InsightFace_Pytorch","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zrui94/insight_mx","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/melgor/pyface","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/KikimorMay/MultiFace","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/eric-erki/insightface","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/chenggongliang/arcface","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sunil-rival/insightface","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_honours":1,"ran_fixture":2},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"f975336b5d5f1a80","entry":"calculate_accuracy","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"f975336b5d5f1a80"}},{"code_sha256_prefix":"db4008738f6c3eae","entry":"calculate_roc","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"db4008738f6c3eae"}},{"code_sha256_prefix":"c7c89ea2508beb10","entry":"calculate_val","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"c7c89ea2508beb10"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}