/MobileFaceNet_Tensorflow

Tensorflow implementation for [MobileFaceNet]

Primary LanguagePythonApache License 2.0Apache-2.0

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

Training

MobileFaceNet

train_nets.py --max_epoch=10
              --train_batch_size=128
              --model_type=0  # mobilefacenet

TinyMobileFaceNet

train_nets.py --max_epoch=10
              --train_batch_size=128
              --model_type=1  # tinymobilefacenet

Inference

MobileFaceNet

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

TinyMobileFaceNet

python inference.py --pretrained_model='./output/ckpt_best/tinymobilefacenet_best_ckpt'
                    --model_type=1

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

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