文件列表:

fmobilefacenet.py (4281, 2018-07-30)
model-y1-arcface-symbol.json (107947, 2018-07-30)
model-y1-softmax12-symbol-512.json (107947, 2018-07-30)
petrained-models (70, 2018-07-30)
train.md (2112142, 2018-07-30)
trainarc.sh (3463, 2018-07-30)
wd1e-6-0.01.log (648757, 2018-07-30)

# 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: 23.02***81 [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: 23.56***53 [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 *** --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 *** --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 *** --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 |0.941700| | age_db30 |0.966700|

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