From 19c8118e01e67edfaa7a548a0db51bdf500177b0 Mon Sep 17 00:00:00 2001 From: JiankangDeng Date: Tue, 13 Oct 2020 23:26:42 +0100 Subject: [PATCH] Update README.md --- recognition/partial_fc/README.md | 30 +++++++++++++----------------- 1 file changed, 13 insertions(+), 17 deletions(-) diff --git a/recognition/partial_fc/README.md b/recognition/partial_fc/README.md index cf90961..c1c89a1 100644 --- a/recognition/partial_fc/README.md +++ b/recognition/partial_fc/README.md @@ -1,7 +1,5 @@ ## Partial-FC -Partial FC is a distributed deep learning training framework for face recognition. The goal of Partial FC is to make -training large scale classification task (eg. 10 or 100 millions identies) fast and easy. It is faster than model parallel -and can train more identities. +Partial FC is a distributed deep learning training framework for face recognition. The goal of Partial FC is to facilitate large-scale classification task (e.g. 10 or 100 million identities). It is much faster than the model parallel solution and there is no performance drop. ![Image text](https://github.com/nttstar/insightface-resources/blob/master/images/partial_speed1.png) @@ -15,14 +13,13 @@ and can train more identities. - [Citation](#Citation) -## Glint360k -We clean, merge, and release the **largest** and **cleanest** face recognition dataset **Glint360k**. -Baseline models trained on Glint360k with our proposed training strategy can easily achieve state-of-the-art. -The released dataset contains 18 million images of 360K individuals. The performance of Glint360k eval on large-scale -test set IFRT, IJB-C and Megaface are as follows: +## Glint360K +We clean, merge, and release the **largest** and **cleanest** face recognition dataset **Glint360K**, which contains 18 million images of 360K individuals. +By employing the Patial FC training strategy, baseline models trained on Glint360K can easily achieve state-of-the-art performance. +Detailed evaluation results on the large-scale test set (e.g. IFRT, IJB-C and Megaface) are as follows: #### Evaluation on IFRT -**`r`** means the negative class centers sampling rate. +**`r`** denotes the sampling rate of negative class centers. | Backbone | Dataset | African | Caucasian | Indian | Asian | ALL | | ------------ | ----------- | ----- | ----- | ------ | ----- | ----- | | R50 | MS1M-V3 | 76.24 | 86.21 | 84.44 | 37.43 | 71.02 | @@ -31,17 +28,16 @@ test set IFRT, IJB-C and Megaface are as follows: | R100 | **Glint360k**(r=0.1) | **90.45** | **94.60** | **93.96** | 63.91 | 88.23 | #### Evaluation on IJB-C and Megaface - -Our backbone is ResNet100, we set feature scale s to 64 and cosine margin m of CosFace at 0.4. -TAR@FAR=1e-4 is reported on the IJB-C datasets, TAR@FAR=1e-6 is reported on Megaface verification. +We employ ResNet100 as the backbone and CosFace (m=0.4) as the loss function. +TAR@FAR=1e-4 is reported on the IJB-C datasets, and TAR@FAR=1e-6 is reported on the Megaface dataset. |Test Dataset | IJB-C | Megaface_Id | Megaface_Ver | | :--- | :---: | :---: | :---: | | MS1MV2 | 96.4 | 98.3 | 98.6 | |**Glint360k** | **97.3** | **99.1** | **99.1** | #### Download -[**Baidu Pan**](https://pan.baidu.com/s/1aHC_nJGKzKgwJKoVb2Q_Gg) (code:i1al) -Google Drive coming soon +[**Baidu Drive**](https://pan.baidu.com/s/1aHC_nJGKzKgwJKoVb2Q_Gg) (code:i1al) +[**Dropbox**](https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0) Refer to the following command to unzip. ``` @@ -54,7 +50,7 @@ tar -xvf glint360k.tar ## Performance -We remove the influence of IO, all experiments use mixed precision training, backbone is ResNet50. +We neglect the influence of IO. All experiments use mixed-precision training, and the backbone is ResNet50. #### 1 Million Identities On 8 RTX2080Ti |Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W | @@ -71,7 +67,7 @@ We remove the influence of IO, all experiments use mixed precision training, bac ## Citation -If you find Partical-FC or Glint360k useful in your research, please consider to cite the following related papers: +If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper: [Partial FC](https://arxiv.org/abs/2010.05222) ``` @@ -79,7 +75,7 @@ If you find Partical-FC or Glint360k useful in your research, please consider to title={Partial FC: Training 10 Million Identities on a Single Machine}, author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and Zhang, Debing and Fu Ying}, - booktitle={Arxiv}, + booktitle={Arxiv 2010.05222}, year={2020} } ```