K-FACE Analysis Project on Pytorch

Last update: Nov 10, 2022

Related tags

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

GNN4Traffic - This is the repository for the collection of Graph Neural Network for Traffic Forecasting

Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

Skipgram Negative Sampling in PyTorch

Implementation of MA-Trace - a general-purpose multi-agent RL algorithm for cooperative environments.

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

Object DGCNN and DETR3D, Our implementations are built on top of MMdetection3D.

[CVPR 2021] 'Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator'

LineBoard - Python+React+MySQL-白板即時系統改善人群行為

Temporal Knowledge Graph Reasoning Triggered by Memories

ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

Unifying Global-Local Representations in Salient Object Detection with Transformer

An efficient PyTorch implementation of the evaluation metrics in recommender systems.

AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

Training Very Deep Neural Networks Without Skip-Connections

Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

Transfer Learning Remote Sensing

Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.

Pytorch implementation of the paper Time-series Generative Adversarial Networks

AntiFuzz: Impeding Fuzzing Audits of Binary Executables

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks