Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟳𝟳 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 WebFace260M: A Benchmark for Million-Scale Deep Face Recognition by Tsinghua University, Beijing, China Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published #CVPR 2022. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 👉 Face benchmarks empower the research community to train and evaluate high-performance face recognition systems. 👉 In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. 👉 Firstly, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. 👉 To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry. 👉 Referring to practical deployments, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Besides, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. 👉 For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively. Equipped with this benchmark, we delve into million-scale face recognition problems. 👉 A distributed framework is developed to train face recognition models efficiently without tampering with the performance. 👉 Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank 3rd among 430 entries on NIST-FRVT. 👉 Even 10% data (WebFace4M) shows superior performance compared with the public training sets. 👉 Furthermore, comprehensive baselines are established under the FRUITS-100/500/1000 milliseconds protocols. 👉 The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios. #computervision #artificialintelligence #deeplearning #machinelearning #tensorflow #pytorch #facerecognition #india #datascience #data #Analytics #technology #innovation #dataanalytics