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All Projects → Xiaoccer → Mobilefacenet_pytorch

Xiaoccer / Mobilefacenet_pytorch

MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

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python
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Labels

pytorchface-recognition

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MobileFaceNet

Introduction

Requirements

  • Python 3.5
  • pytorch 0.4+
  • GPU menory

Usage

Part 1: Preprocessing

Part 2: Train

  1. Change the CAISIA_DATA_DIR and LFW_DATA_DAR in config.py to your data path.

  2. Train the mobilefacenet model.

    Note: The default settings set the batch size of 512, use 2 gpus and train the model on 70 epochs. You can change the settings in config.py

    python3 train.py

Part 3: Test

  1. Test the model on LFW.

    Note: I have tested lfw_eval.py on the caffe model at SphereFace, it gets the same result.

    python3 lfw_eval.py --resume --feature_save_dir
    • --resume: path of saved model
    • --feature_save_dir: path to save the extracted features (must be .mat file)

Results

  • You can just run the lfw_eval.py to get the result, the accuracy on LFW like this:
Fold 1 2 3 4 5 6 7 8 9 10 AVE(ours) Paper(112x96)
ACC 99.00 99.00 99.00 98.67 99.33 99.67 99.17 99.50 100.00 99.67 99.30 99.18

Reference resources

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