MultiFace_Pytorch
- This repo is a implementation of MultiFace
- Pretrained models are posted, include the MobileFacenet and IR-SE100 in the original paper
| Loss | AgeDB-30(%) | CFP-FP(%) | calfw(%) | cplfw(%) |
|---|---|---|---|---|
| Multi-Arcface(N=2) | 98.20 | 98.30 | 96.02 | 93.17 |
| Multi-Cosface(N=2) | 98.20 | 98.40 | 96.07 | 93.06 |
| Loss | LFW(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) |
|---|---|---|---|---|---|
| Softmax | 99.22 | 92.84 | 94.00 | 93.80 | 88.30 |
| Multi-Softmax(N=4) | 99.40 | 95.46 | 95.25 | 95.15 | 90.22 |
| Loss | LFW(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) |
|---|---|---|---|---|---|
| Arcface | 99.45 | 92.27 | 96.03 | 95.12 | 87.75 |
| Multi-Arcface(N=2) | 99.52 | 93.41 | 96.35 | 95.28 | 88.65 |
| Cosface | 99.43 | 92.83 | 95.77 | 94.97 | 88.88 |
| Multi-Cosface(N=2) | 99.50 | 93.58 | 96.17 | 95.20 | 89.03 |
| Multi-Cosface(N=4) | 99.60 | 94.11 | 96.13 | 95.18 | 89.47 |
download the refined dataset: (emore recommended, our method is suitable for larger dataset )
-
emore dataset @ BaiduDrive, emore dataset @ Dropbox
Note: If you use the refined MS1M dataset, please cite the original papers.
-
after unzip the files to 'data' path, run :
python prepare_data.pyafter the execution, you should find following structure:
faces_emore/
---> agedb_30
---> calfw
---> cfp_ff
---> cfp_fp
---> cfp_fp
---> cplfw
--->imgs
---> lfw
---> vgg2_fp
'''
# mobilefacenet loss:softmax num_sphere:2
python train.py --num_sphere 2 --work_path [save log and model information]
# mobilefacenet loss:arcface num_sphere:2
python train.py --num_sphere 2 --arcface_loss --work_path [save log and model information]
# mobilefacenet loss:cosface num_sphere:2
python train.py --num_sphere 2 --am_softmax_loss --work_path [save log and model information]
# ir-se100 loss:arcface num_sphere:2
python train.py --num_sphere 2 --arcface_loss --net ir_se -depth 100 --work_path [save log and model information]
# ir-se100 loss:cosface num_sphere:2
python train.py --num_sphere 2 --am_softmax_loss --net ir_se -depth 100 --work_path [save log and model information]
Evaluating the model on LFW, Age-DB, CFP-FP, CALFW, CPLFW
#pretrained mobilefacenet
python train.py --pretrain --pretrained_model_path [mobilefacenet_pretrained_model_path]
#pretrained ir-se 100
python train.py --pretrain -net ir_se -depth 100 --work_path [resnet_pretrained_model_path]
To evaluate on Megaface, please refer to megaface-evaluation.
- This code is based on the implementations of TreB1eN/InsightFace_Pytorch and deepinsight/insightface
- Email :[email protected]
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