K-FACE Analysis Project on Pytorch

Last update: Nov 10, 2022

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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

Structure of the K-FACE database

Configuration of K-FACE

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:

#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.

K-FACE Analysis Project on Pytorch

Related tags

Overview

Installation

Setup with Conda

Data prepration

K-FACE Database

Detection & Alignment on K-FACE

Training and test datasets on K-FACE

MS1M-RetinaFace (MS1M-R)

Inference

Pretrained Model

The model was trained on KFACE

The model was trained on MS1M-R

The model was trained on MS1M-R+T4

Training

Multi-GPU DataParallel Mode

Example script for training on KFACE

Multi-GPU DistributedDataParallel Mode

Example script for training on KFACE

Reference code

Owner

Jung Jun Uk

Official PyTorch implementation of PS-KD

Code accompanying "Dynamic Neural Relational Inference" from CVPR 2020

A full pipeline AutoML tool for tabular data

We will release the code of "ConTNet: Why not use convolution and transformer at the same time?" in this repo

Long Expressive Memory (LEM)

This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".

Hands-On Machine Learning for Algorithmic Trading, published by Packt

Official page of Patchwork (RA-L'21 w/ IROS'21)

Code for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

Learning Energy-Based Models by Diffusion Recovery Likelihood

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

[AAAI-2022] Official implementations of MCL: Mutual Contrastive Learning for Visual Representation Learning

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

"Projelerle Yapay Zeka Ve Bilgisayarlı Görü" Kitabımın projeleri

code from "Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity"

DAN: Unfolding the Alternating Optimization for Blind Super Resolution

Markov Attention Models

Official repository of the AAAI'2022 paper "Contrast and Generation Make BART a Good Dialogue Emotion Recognizer"

Text-to-Image generation

The undersampled DWI image using Slice-Interleaved Diffusion Encoding (SIDE) method can be reconstructed by the UNet network.