GuideWsp / cavaface.pytorch

:beers: :rocket: :city_sunset: face recognition project(pytorch)

cavaface.pytorch: A Pytorch Training Framework for Deep Face Recognition

By Yaobin Li

Introduction

This repo provides a high-performance distribute parallel training framework for face recognition with pytorch, including various backbones (e.g., ResNet, IR, IR-SE, ResNeXt, AttentionNet-IR-SE, ResNeSt, HRNet, etc.), various losses (e.g., Softmax, Focal, SphereFace, CosFace, AmSoftmax, ArcFace, ArcNegFace, CurricularFace, Li-Arcface, QAMFace, etc.), various data augmentation(e.g., RandomErasing, Mixup, RandAugment, Cutout, CutMix, etc.) and bags of tricks for improving performance (e.g., FP16 training(apex), Label smooth, LR warmup, etc)

Main requirements

• torch == 1.4.0
• torchvision == 0.5.0
• tensorboardX == 1.7
• bcolz == 1.2.1
• Python 3
• Apex == 0.1

Features

• Backone
  • ResNet(IR-SE)
  • ResNeXt
  • DenseNet
  • MobileFaceNet
  • MobileNetV3
  • EfficientNet
  • ProxylessNas
  • GhostNet
  • AttentionNet-IRSE
  • EfficientPolyFace
  • ResNeSt
• Attention Module
  • SE
  • CBAM
  • ECA
  • ACNet
• Loss
  • Softmax
  • SphereFace
  • Am_Softmax
  • CosFace
  • ArcFace
  • Combined Loss
  • AdaCos
  • SV-X-Softmax
  • CurricularFace
  • ArcNegFace
  • Li-Arcface
  • QAMFace
• Parallel Training
  • Data Parallel
  • Model Parallel
• Automatic Mixed Precision
  • Apex
• Optimizer
  • LR_Scheduler(faireq,rwightman)
  • Optim(SGD,Adam,RAdam,LookAhead,Ranger)
• Data Augmentation
  • RandomErasing(官方版torchvision.transforms.RandomErasing)
  • Mixup
  • RandAugment
  • Cutout
  • CutMix
• Distillation
  • KnowledgeDistillation
  • Multi Feature KD
• Bag of Tricks
  • Label smooth
  • LR warmup
  • Zero gamma

Quick start

Installation

1. Install pytorch==1.4.0 and torchvision==0.5.0 following official instruction.
2. Clone this repo, and we'll call the directory that you cloned as ${WORK_ROOT}.
3. Install dependencies:

   pip install -r requirements.txt
   

Data preparation

For training data, please download the ms1m-retinaface in https://github.com/deepinsight/insightface/tree/master/iccv19-challenge.

For test data, please download the megaface and ijbc in https://github.com/deepinsight/insightface/tree/master/Evaluation.

Training and Testing

Training on ms1m-retinaface

You can change the experimental setting by simply modifying the parameter in the config.py
bash train.sh
# To evaluate the model on validation set:
(1)please first download the val data in https://github.com/ZhaoJ9014/face.evoLVe.PyTorch.
(2)set the checkpoint dir in config.py
bash evaluate.sh

Testing on Megaface and IJBC

1. Put the test data and image list into proper directory.
2. Start evaluation service.

nohup python evaluate_service.py > logs/log.service &

3. Start extracting features and evaluating.

nohup bash run.sh > logs/log &

Benchmark

Backbone	Head	Loss	Flops	Megaface(Id/ver@1e-6)	IJBC(tar@far=1e-4)
MobileFaceNet	Arcface	Softmax	440M	92.8694/93.6329	92.80
GhostNet	Arcface	Softmax	270M	93.3914/94.3359	93.50
GhostNet_x1.3	Arcface	Softmax	440M	95.3005/95.7757	94.27
MobileNetV3	Arcface	Softmax	430M	93.9805/95.7314	93.57
ProxylessNAS_mobile	Arcface	Softmax	630M	93.2886/95.2094	93.74
ProxylessNAS_cpu	Arcface	Softmax	860M	95.4242/95.79	94.22
EfficientNet_b0	Arcface	Softmax	770M	96.3589/97.1946	94.84
EfficientNet_b1	Arcface	Softmax	1.14G	97.095/97.4003	95.38
AttentionNet-IRSE-92	MV-AM	Softmax	17.63G	99.1356/99.3999	96.56
IR-SE-100	Arcface	Softmax	24.18G	99.0881/99.4259	96.69
IR-SE-100	ArcNegface	Softmax	24.18G	99.1304/98.7099	96.81
IR-SE-100	Curricularface	Softmax	24.18G	99.0497/98.6162	97.00
IR-SE-100	Combined	Softmax	24.18G	99.0718/99.4493	96.83
IR-SE-100	CircleLoss	Softplus	24.18G	98.5732/98.4834	96.52
ResNeSt-101	Arcface	Softmax	18.45G	98.8746/98.5615	96.63
DenseNet-201	Arcface	Softmax	8.52G	98.3649/98.4294	96.03

Acknowledgement

• This repo is modified and adapted on these great repositories face.evoLVe.PyTorch, CurricularFace, insightface and imgclsmob
• The evaluation tools is developed by Charrin

Contact

[e-mail]