mobilefacenet-V2

now we get more higher accuray:

[lfw][12000]Accuracy-Flip: 0.99667+-0.00358
[agedb_30][12000]Accuracy-Flip: 0.96667+-0.00167 use my modified mobilenet network.

lr-batch-epoch: 0.01 11738 1 testing verification.. (12000, 512) infer time 39.129495 [lfw][36000]XNorm: 22.729305 [lfw][36000]Accuracy-Flip: 0.99667+-0.00358

improve the accuracy of mobilefacenet in paper mobilefacenet论文(https://arxiv.org/abs/1804.07573)

First step training (use softmax to pretrain): train softmax(facenet):

[lfw][62000]XNorm: 23.029881 [lfw][62000]Accuracy-Flip: 0.99383+-0.00308 testing verification.. (14000, 512) infer time 20.121058 [cfp_fp][62000]XNorm: 24.043967 [cfp_fp][62000]Accuracy-Flip: 0.89343+-0.01705 testing verification.. (12000, 512) infer time 16.860138 [agedb_30][62000]XNorm: 23.566453 [agedb_30][62000]Accuracy-Flip: 0.93883+-0.01675 saving 31 INFO:root:Saved checkpoint to "../models/MF/model-y1-softmax12-0031.params"

pretrained models: https://pan.baidu.com/s/1xBq9FoL79z7K892aFWkmFw

Second step: CUDA_VISIBLE_DEVICES='0,1' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --margin-s [128] --lr-steps 120000,180000,210000,230000 --emb-size [512] --per-batch-size 150 --data-dir ../data/faces_ms1m_112x112 --pretrained ../models/MobileFaceNet/model-y1-softmax,20 --prefix ../models/MF/model-y1-arcface

Third step: CUDA_VISIBLE_DEVICES='0,1' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --lr 0.001 --lr-steps 40000,60000,70000 --wd 0.00004 --fc7-wd-mult 10 --emb-size 512 --per-batch-size 150 --margin-s 64 --data-dir ../data/faces_ms1m_112x112 --pretrained ../models/MF/model-y1-arcface,46 --prefix ../models/MF/model-y1-arcface

Update wd=0.00001 , --fc7-wd-mult 10 --emb-size 512 i get new Accuracy:

Accuracy

dbname	accuracy
lfw	0.996233
cfp_fp	0.94300
age_db30	0.96383

##########first #CUDA_VISIBLE_DEVICES='0' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --lr 0.1 --emb-size 512 --per-batch-size 240 --margin-s 64 --wd 0.00001 --fc7-wd-mult 10 --data-dir /Users/sunyimac/faces_emore --pretrained ../models/MobileFaceNet/model-y1-arcfaced,18 --prefix ../models/MobileFaceNet/model-y1-arcface

#CUDA_VISIBLE_DEVICES='0' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --lr 0.01 --emb-size 512 --per-batch-size 240 --margin-s 64 --wd 0.00001 --fc7-wd-mult 10 --data-dir /Users/sunyimac/faces_emore --pretrained ../models/MobileFaceNet/model-y1-arcface,62 --prefix ../models/MobileFaceNet/model-y1-arcfaced

CUDA_VISIBLE_DEVICES='0' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --lr 0.00001 --emb-size 512 --per-batch-size 240 --wd 0.00001 --fc7-wd-mult 10 --data-dir /Users/sunyimac/faces_emore --pretrained ../models/MobileFaceNet/model-y1-arcface,75 --prefix ../models/MobileFaceNet/model-y1-arcfaced

Update wd=0.000001 trainning is not end. now is the new Accuracy: i get new higher Accuracy:

Accuracy

dbname	accuracy
lfw	0.99667
cfp_fp	0.94300
age_db30	0.96700

Update wd=0.0000001 trainning is not end. now is the new Accuracy: i get new higher Accuracy:

Accuracy🔥

dbname	accuracy
lfw	0.99683
cfp_ff	0.99733
cfp_fp	0.94500
age_db30	0.96717
you can visit my log file:
https://github.com/qidiso/mobilefacenet-V2/blob/master/retrain0.001.log

Now Release the models:

[models:]https://github.com/aidlearning/AidLearning-FrameWork/tree/master/examples/facencnn (reached 99.733 in the cfp-ff、 the 99.68+ in lfw,96.71+ in agedb30)