MobileFaceNet_Tensorflow

Tensorflow implementation for MobileFaceNet which is modified from MobileFaceNet_TF

Requirements

• tensorflow >= r1.2 (support cuda 8.0, original needs tensorflow >= r1.5 and cuda 9.0)
• opencv-python
• python 3.x ( if you want to use python 2.x, somewhere in load_data function need to change, see details in comment)
• mxnet
• anaconda (recommend)

Construction

├── MobileFaceNet
│   ├── arch
│       ├── img
│       ├── txt
│   ├── datasets
│       ├── faces_ms1m_112x112
│       ├── tfrecords
│   ├── losses
│   ├── nets
│   ├── output
│       ├── ckpt
│       ├── ckpt_best
│       ├── logs
│       ├── summary
│   ├── utils

Datasets

1. choose one of The following links to download dataset which is provide by insightface. (Special Recommend MS1M)

• Refined-MS1M@BaiduDrive, Refined-MS1M@GoogleDrive
• VGGFace2@BaiduDrive, VGGFace2@GoogleDrive

2. move dataset to ${MobileFaceNet_TF_ROOT}/datasets.
3. run ${MobileFaceNet_TF_ROOT}/utils/data_process.py.

Training

train_nets.py --max_epoch=10
              --train_batch_size=128
              --model_type=0  # 0-mobilefacenet 1-tinymobilefacenet 2-squeezefacenet 3-shufflefacenet 4-shufflefacenetV2
              --gpu=0

Inference

MobileFaceNet

python inference.py --pretrained_model='./output/ckpt_best/mobilefacenet_best_ckpt'
                    --model_type=0
                    --gpu=0

Performance

Original result

size	LFW(%)	Val@1e-3(%)	inference@MSM8976(ms)
5.7M	99.25+	96.8+	260-

My training results

Models	LFW	Cfp_FF	Cfp_FP	Agedb_30	inference@i7-7700 16G 240G (fps)
MobileFaceNet(Bad training)	0.983+-0.008	0.980+-0.005	0.827+-0.019	0.878+-0.023	27
Tiny_MobileFaceNet	0.981+-0.008	0.984+-0.006	0.835+-0.019	0.882+-0.023	50
SqueezeFaceNet	0.972+-0.008	0.962+-0.006	0.785+-0.019	0.837+-0.023	83
ShuffleFaceNet v1	0.962+-0.008	0.941+-0.006	0.763+-0.019	0.747+-0.023	33
ShuffleFaceNet v2	0.980+-0.008	0.971+-0.006	0.820+-0.019	0.823+-0.023	50

References

1. facenet
2. InsightFace mxnet
3. InsightFace_TF
4. MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
5. CosFace: Large Margin Cosine Loss for Deep Face Recognition
6. InsightFace : Additive Angular Margin Loss for Deep Face Recognition
7. tensorflow-triplet-loss
8. MobileFaceNet_TF
9. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
10. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices