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

[AAAI22] Reliable Propagation-Correction Modulation for Video Object Segmentation

Codes to calculate solar-sensor zenith and azimuth angles directly from hyperspectral images collected by UAV. Works only for UAVs that have high resolution GNSS/IMU unit.

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral)

A minimal solution to hand motion capture from a single color camera at over 100fps. Easy to use, plug to run.

An End-to-End Machine Learning Library to Optimize AUC (AUROC, AUPRC).

My usage of Real-ESRGAN to upscale anime, some test and results in the test_img folder

Code for Greedy Gradient Ensemble for Visual Question Answering （ICCV 2021, Oral）

This repository provides data for the VAW dataset as described in the CVPR 2021 paper titled "Learning to Predict Visual Attributes in the Wild"

This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

Rlmm blender toolkit - A set of tools to streamline level generation in UDK straight from Blender

Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

Official repository for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'21, Oral Presentation)

unet-family: Ultimate version

Model serving at scale

A toolkit for making real world machine learning and data analysis applications in C++

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

A simple, unofficial implementation of MAE using pytorch-lightning

An example to implement a new backbone with OpenMMLab framework.

GraphGT: Machine Learning Datasets for Graph Generation and Transformation