Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/qidiso/mobilefacenet-v2

🔥improve the accuracy of mobilefacenet(insight face) reached 99.733 in the cfp-ff、 the 99.68+ in lfw,96.71+ in agedb30.🔥
https://github.com/qidiso/mobilefacenet-v2

face fast insightface mobile mobilenet

Last synced: 3 days ago
JSON representation

🔥improve the accuracy of mobilefacenet(insight face) reached 99.733 in the cfp-ff、 the 99.68+ in lfw,96.71+ in agedb30.🔥

Host: GitHub
URL: https://github.com/qidiso/mobilefacenet-v2
Owner: qidiso
Created: 2018-06-04T16:00:42.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2019-08-08T09:40:08.000Z (over 5 years ago)
Last Synced: 2024-12-23T15:15:26.704Z (10 days ago)
Topics: face, fast, insightface, mobile, mobilenet
Language: Python
Homepage:
Size: 470 KB
Stars: 393
Watchers: 71
Forks: 147
Open Issues: 19
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # 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)