now we get more higher accuray:
[lfw][12000]Accuracy-Flip: 0.996233+-0.00358
[agedb_30][12000]Accuracy-Flip: 0.96383+-0.00893 use my modified mobilenet network.
improve the accuracy of mobilefacenet in paper mobilefacenet论文(https://arxiv.org/abs/1804.07573)
now i modifed to train:
batch-epoch: 0.001 11999 0 testing verification.. (12000, 512) infer time 12.695148 [lfw][12000]XNorm: 22.189873 [lfw][12000]Accuracy-Flip: 0.99617+-0.00358 testing verification.. (14000, 512) infer time 14.926009 [cfp_fp][12000]XNorm: 18.588222 [cfp_fp][12000]Accuracy-Flip: 0.88857+-0.01704 testing verification.. (12000, 512) infer time 12.718514 [agedb_30][12000]XNorm: 22.106283 [agedb_30][12000]Accuracy-Flip: 0.96017+-0.00893 saving 6 INFO:root:Saved checkpoint to "../models/MF/model-y1-arcfaceredodododo-0006.params" [12000]Accuracy-Highest: 0.96033
or
lr-batch-epoch: 0.001 1317 1 testing verification.. (12000, 512) infer time 12.44981 [lfw][14000]XNorm: 22.190438 [lfw][14000]Accuracy-Flip: 0.99600+-0.00359 testing verification.. (14000, 512) infer time 14.993647 [cfp_fp][14000]XNorm: 18.583859 [cfp_fp][14000]Accuracy-Flip: 0.88814+-0.01752 testing verification.. (12000, 512) infer time 12.716178 [agedb_30][14000]XNorm: 22.115754 [agedb_30][14000]Accuracy-Flip: 0.96100+-0.00790 saving 7 INFO:root:Saved checkpoint to "../models/MF/model-y1-arcfaceredodododo-0007.params" [14000]Accuracy-Highest: 0.96100
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:
| 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