XinWangg/InsightFace-PyTorch

PyTorch implementation of Additive Angular Margin Loss for Deep Face Recognition.

InsightFace

PyTorch implementation of Additive Angular Margin Loss for Deep Face Recognition. paper.

@article{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
journal={arXiv:1801.07698},
year={2018}
}

Performance

• sgd with momentum
• margin-m = 0.6
• margin-s = 64.0
• batch size = 256
• input image is normalized with mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]

Models	MegaFace	LFW	Download
SE-LResNet101E-IR	98.06%	99.80%	Link

Dataset

Function	Dataset
Train	MS-Celeb-1M
Test	MegaFace

Introduction

MS-Celeb-1M dataset for training, 3,804,846 faces over 85,164 identities.

Dependencies

• Python 3.6.8
• PyTorch 1.3.0

Usage

Data wrangling

Extract images, scan them, to get bounding boxes and landmarks:

$ python extract.py
$ python pre_process.py

Image alignment:

1. Face detection(Retinaface mobilenet0.25).
2. Face alignment(similar transformation).
3. Central face selection.
4. Resize -> 112x112.

Original	Aligned & Resized	Original	Aligned & Resized

Train

$ python train.py

To visualize the training process：

$ tensorboard --logdir=runs

Performance evaluation

MegaFace

Introduction

MegaFace dataset includes 1,027,060 faces, 690,572 identities.

Challenge 1 is taken to test our model with 1 million distractors.

Download

1. Download MegaFace and FaceScrub Images
2. Download FaceScrub annotation files:
   • facescrub_actors.txt
   • facescrub_actresses.txt
3. Download Linux DevKit from MagaFace WebSite then extract to megaface folder:

$ tar -vxf linux-devkit.tar.gz

Face Alignment

1. Align Megaface images:

$ python3 align_megaface.py

2. Align FaceScrub images with annotations:

$ python3 align_facescrub.py

Evaluation

$ python3 megaface_eval.py

It does following things:

1. Generate features for FaceScrub and MegaFace.
2. Remove noises.
   Note: we used the noises list proposed by InsightFace, at https://github.com/deepinsight/insightface.
3. Start MegaFace evaluation through devkit.

Results

Curves

Draw curves with matlab script @ megaface/draw_curve.m.

CMC	ROC

Textual results

Done matching! Score matrix size: 3359 966804
Saving to results/otherFiles/facescrub_megaface_0_1000000_1.bin
Loaded 3359 probes spanning 80 classes
Loading from results/otherFiles/facescrub_facescrub_0.bin
Probe score matrix size: 3359 3359
distractor score matrix size: 3359 966804
Done loading. Time to compute some stats!
Finding top distractors!
Done sorting distractor scores
Making gallery!
Done Making Gallery!
Allocating ranks (966884)

Rank 1: 0.980616

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