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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

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

My training results

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