K-FACE Analysis Project on Pytorch

Related tags

Deep Learningmixface
Overview

Installation

Setup with Conda

# create a new environment
conda create --name insightKface python=3.7 # or over
conda activate insightKface

#install the appropriate cuda version of pytorch(https://pytorch.org/)
#example:
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

# install requirements
pip install -r requirements.txt

Data prepration

K-FACE Database

K-FACE AI-hub.

Detail configuration about K-FACE is provided in the paper below.

K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments

K-FACE sample images

title

Structure of the K-FACE database

title

Configuration of K-FACE

Configuration_of_KFACE

Detection & Alignment on K-FACE

"""
    ###################################################################

    K-Face : Korean Facial Image AI Dataset
    url    : http://www.aihub.or.kr/aidata/73

    Directory structure : High-ID-Accessories-Lux-Emotion
    ID example          : '19062421' ... '19101513' len 400
    Accessories example : 'S001', 'S002' .. 'S006'  len 6
    Lux example         : 'L1', 'L2' .. 'L30'       len 30
    Emotion example     : 'E01', 'E02', 'E03'       len 3
    
    ###################################################################
"""

# example
cd detection

python align_kfaces.py --ori_data_path '/data/FACE/KFACE/High' --detected_data_path 'kface_retina_align_112x112'

Training and test datasets on K-FACE

Train ID Accessories Lux Expression Pose #Image Variance
T1 A1 1000 E1 C4-10 2,590 Very Low
T2 A1-2 400-1000 E1 C4-10 46,620 Low
T3 A1-A4 200-1000 E1-2 C4-13 654,160 Middle
T4 A1-A6 40-1000 E1-3 C1-20 3,862,800 High
Test ID Accessories Lux Expression Pose #Pairs Variance
Q1 A1 1000 E1 C4-10 1,000 Very Low
Q2 A1-2 400-1000 E1 C4-10 100,000 Low
Q3 A1-4 200-1000 E1-2 C4-13 100,000 Middle
Q4 A1-6 40-1000 E1-3 C1-20 100,000 High

MS1M-RetinaFace (MS1M-R)

MS1M-RetinaFace download link:

  1. The Lightweight Face Recognition Challenge & Workshop.

  2. https://github.com/deepinsight/insightface/wiki/Dataset-Zoo

#Preprocess 'train.rec' and 'train.idx' to 'jpg'

# example
cd detection

python rec2image.py --include '/data/FACE/ms1m-retinaface-t1/' --output 'MS1M-RetinaFace'

Inference

After downloading the pretrained model, run test.py.

Pretrained Model

For all experiments, ResNet-34 was chosen as the baseline backbone.

The model was trained on KFACE

Head&Loss Q1 Q2 Q3 Q4
ArcFace (s=16, m=0.25) 98.30 94.77 87.87 85.41
SN-pair (s=64) 99.20 95.01 91.84 89.74
MixFace (e=1e-22, m=0.25) 100 96.37 92.36 89.80

Note:

  • For ArcFace, We tested (s,m)={(16,0.5), (32,0.25), (64,0.25), (32,0.5), (64,0.5)}, but the model was not trained properly So, we apply (s,m)=(16,0.25).
cd recognition

# example
python test.py --weights 'kface.mixface.1e-22m0.25.best.pt' --dataset 'kface' --data_cfg 'data/KFACE/kface.T4.yaml'

The model was trained on MS1M-R

Head&Loss Q2 Q3 Q4 LFW CFP-FP AgeDB-30
ArcFace (s=64, m=0.5) 98.71 86.60 82.03 99.80 98.41 98.80
SN-pair (s=64) 92.85 76.36 70.08 99.55 96.20 95.46
MixFace (e=1e-22, m=0.5) 97.36 82.89 76.95 99.68 97.74 97.25 
cd recognition

# example
python test.py --weights 'face.mixface.1e-22m0.5.best.pt' --dataset 'face' --data_cfg 'data/face.all.yaml'

The model was trained on MS1M-R+T4

Head&Loss Q2 Q3 Q4 LFW CFP-FP AgeDB-30
ArcFace (s=8, m=0.25) 76.58 73.13 71.38 99.46 96.75 93.83
SN-pair (s=64) 98.37 94.98 93.33 99.45 94.90 93.45
MixFace (e=1e-22, m=0.5) 99.27 96.85 94.79 99.53 96.32 95.56

Note:

  • For ArcFace, we tested (s,m)={(8, 0.5), (16, 0.25), (16,0.5), (32,0.25), (64,0.25), (32,0.5), (64,0.5)}, but the model was not trained properly So, we apply (s,m)=(8,0.25).
cd recognition

# example
python test.py --weights 'merge.mixface.1e-22m0.5.best.pt' --dataset 'merge' --data_cfg 'data/merge.yaml'

Training

Multi-GPU DataParallel Mode

Example script for training on KFACE

cd recognition

# example 
python train.py --dataset 'kface' --head 'mixface' --data_cfg 'data/KFACE/kface.T4.yaml' --hyp 'data/face.hyp.yaml' --head_cfg 'models/head.kface.cfg.yaml' --name 'example' --device 0,1

Multi-GPU DistributedDataParallel Mode

Example script for training on KFACE

cd recognition

# example
python -m torch.distributed.launch --nproc_per_node 2 train.py --dataset 'kface' --head 'mixface' --data_cfg 'data/KFACE/kface.T4.yaml' --hyp 'data/face.hyp.yaml' --head_cfg 'models/head.kface.cfg.yaml' --name 'example' --device 0,1

Note:

  • For MS1M-R, change args --dataset face, --data_cfg data/face.all.yaml, and --head_cfg model/head.face.cfg.yaml.
  • For MS1M-R+T4, change args --dataset merge, --data_cfg data/merge.yaml, and --head_cfg model/head.merge.cfg.yaml.
  • The args --nodrop should be used if you train with the metric loss(e.g., SN-pair, N-pair, etc.) on MS1M-R or MS1M-R+T4.
  • The args --double should be used if you train with the metric loss(e.g., SN-pair, N-pair, etc.) or MixFace on MS1M-R or MS1M-R+T4.
  • DistributedDataParallel is only available to classification loss(e.g., arcface, cosface, etc.)

Reference code

Thanks for these source codes porviding me with knowledges to complete this repository.

  1. https://github.com/biubug6/Pytorch_Retinaface.
  2. https://github.com/deepinsight/insightface.
  3. https://github.com/ultralytics/yolov5
Owner
Jung Jun Uk
Jung Jun Uk
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