qidiso / Mobilefacenet V2
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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/src/facencnn/models (reached 99.733 in the cfp-ff、 the 99.68+ in lfw,96.71+ in agedb30)