LightQNet
This is a demo code of training and testing [LightQNet] using Tensorflow.
Uncertainty Losses:
- IDQ loss
- PCNet loss
Uncertainty Networks:
- MobileNetv3-Small
- Resnet18
Usage
Preprocessing
Download the MS-Celeb-1M dataset from 1 or 2:
- insightface, https://github.com/deepinsight/insightface/wiki/Dataset-Zoo
- face.evoLVe.PyTorch, https://github.com/ZhaoJ9014/face.evoLVe.PyTorch#Data-Zoo)
Decode it using the code: https://github.com/deepinsight/insightface/blob/master/recognition/common/rec2image.py
Training
-
Download the base model ResFace64 from Baidu Drive PW:v800 and unzip the files under
log/resface64
. -
Modify the configuration files under
configfig/
folder. -
Start the training:
python train_idq.py configfig/resface64_msarcface_with_mbv3_small_idq.py
Testing
We use lfw.bin, cfp_fp.bin, etc. from ms1m-retinaface-t1 as the test dataset.
python evaluation/verification_risk_fnmr.py
Freeze and Deploy
Freeze
python freeze_idq.py --model_dir log/resface64_mbv3/20210128-150935
Deployment code
https://github.com/KaenChan/lightqnet
Pre-trained Model
ResFace64
Method | Download |
---|---|
Base Mode | Baidu Drive PW:v800 |
Mobilenetv3-small + IDQ loss + Distillation | Baidu Drive PW:3zgi |
Reference
If you find this repo useful, please consider citing:
@article{chen2021lightqnet,
title={LightQNet: Lightweight Deep Face Quality Assessment for Risk-Controlled Face Recognition},
author={Chen, Kai and Yi, Taihe and Lv, Qi},
journal={IEEE Signal Processing Letters},
volume={28},
pages={1878--1882},
year={2021},
publisher={IEEE}
}