* This repository is the pytorch implement of the paper: [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://arxiv.org/pdf/1804.07573.pdf) and I almost follow the implement details of the paper.
* I train the model on CASIA-WebFace dataset, and evaluate on LFW dataset.
## Requirements
* Python 3.5
* pytorch 0.4+
* GPU memory
## Usage
### Part 1: Preprocessing
* All images of dataset are preprocessed following the [SphereFace](https://github.com/wy1iu/sphereface) and you can download the aligned images at [Align-CASIA-WebFace@BaiduDrive](https://pan.baidu.com/s/1k3Cel2wSHQxHO9NkNi3rkg) and [Align-LFW@BaiduDrive](https://pan.baidu.com/s/1r6BQxzlFza8FM8Z8C_OCBg).
### 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](https://github.com/wy1iu/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:
