From fdcfe67dd326aab7740e399fe2888bf1b9a39ab0 Mon Sep 17 00:00:00 2001 From: JiankangDeng Date: Sun, 14 Apr 2019 13:41:49 +0100 Subject: [PATCH] Update README.md --- README.md | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index e959ddb..a051951 100644 --- a/README.md +++ b/README.md @@ -15,17 +15,18 @@ Please click the image to watch the Youtube video. For Bilibili users, click [he ## Recent Update +**`2019.04.14`**: We will launch a Light-weight Face Recognition challenge/workshop on ICCV 2019. + +**`2019.04.04`**: Arcface achieved state-of-the-art performance (5/109) on the NIST Face Recognition Vendor Test (FRVT) (1:1 verification) +[report](https://www.nist.gov/sites/default/files/documents/2019/04/04/frvt_report_2019_04_04.pdf) (name: Imperial-000). Our solution is based on [MS1MV2+DeepGlintAsian, ResNet100, ArcFace loss]. + **`2019.02.08`**: Please check [https://github.com/deepinsight/insightface/tree/master/recognition](https://github.com/deepinsight/insightface/tree/master/recognition) for our parallel training code which can easily and efficiently support one million identities on a single machine (8* 1080ti). -**`2018.12.13`**: [TVM-Benchmark](https://github.com/deepinsight/insightface/wiki/TVM-Benchmark) +**`2018.12.13`**: Inference acceleration [TVM-Benchmark](https://github.com/deepinsight/insightface/wiki/TVM-Benchmark). -**`2018.10.28`**: [Gender-Age](https://github.com/deepinsight/insightface/tree/master/gender-age) created with a lightweight model. About 1MB size, 10ms on single CPU core. Gender accuracy 96% on validation set and 4.1 age MAE. +**`2018.10.28`**: Light-weight attribute model [Gender-Age](https://github.com/deepinsight/insightface/tree/master/gender-age). About 1MB, 10ms on single CPU core. Gender accuracy 96% on validation set and 4.1 age MAE. -**`2018.10.16`**: We got rank 1st on [IQIYI_VID](http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e)(IQIYI video person identification) competition which in conjunction with PRCV2018, see [detail](https://github.com/deepinsight/insightface/issues/439). - -**`2018.06.14`**: There's a large scale Asian training dataset provided by Glint, see this [discussion](https://github.com/deepinsight/insightface/issues/256) for detail. - -**`2018.02.13`**: We achieved state-of-the-art performance on [MegaFace-Challenge](http://megaface.cs.washington.edu/results/facescrub.html). Please check our paper and code for implementation details. +**`2018.10.16`**: We achieved state-of-the-art performance on [Trillionpairs](http://trillionpairs.deepglint.com/results) (name: nttstar) and [IQIYI_VID](http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e) (name: WitcheR). ## Contents [Deep Face Recognition](#deep-face-recognition) @@ -222,6 +223,12 @@ Todo If you find *InsightFace* useful in your research, please consider to cite the following related papers: ``` +@inproceedings{guo2018stacked, + title={Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment}, + author={Guo, Jia and Deng, Jiankang and Xue, Niannan and Zafeiriou, Stefanos}, + booktitle={BMVC}, + year={2018} +} @inproceedings{deng2018arcface, title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition}, author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},