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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# dotenv
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.env
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# virtualenv
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.venv
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venv/
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ENV/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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output
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datasets

‎LICENSE

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‎README.md

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# MobileFaceNet_Tensorflow
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Tensorflow implementation for [MobileFaceNet](https://arxiv.org/abs/1804.07573) which is modified from [MobileFaceNet_TF](https://github.com/xsr-ai/MobileFaceNet_TF)
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## Requirements
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- tensorflow >= r1.2 (support cuda 8.0, original needs tensorflow >= r1.5 and cuda 9.0)
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- opencv-python
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- python 3.x ( if you want to use python 2.x, somewhere in load_data function need to change, see details in comment)
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- mxnet
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- anaconda (recommend)
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## Construction
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```
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├── MobileFaceNet
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│   ├── arch
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│   ├── img
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│   ├── txt
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│ ├── datasets
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│   ├── faces_ms1m_112x112
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│   ├── tfrecords
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│ ├── losses
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│ ├── nets
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│ ├── output
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│   ├── ckpt
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│   ├── ckpt_best
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│   ├── logs
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│   ├── summary
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│ ├── utils
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```
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## Datasets
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1. choose one of The following links to download dataset which is provide by insightface. (Special Recommend MS1M)
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* [Refined-MS1M@BaiduDrive](https://pan.baidu.com/s/1nxmSCch), [Refined-MS1M@GoogleDrive](https://drive.google.com/file/d/1XRdCt3xOw7B3saw0xUSzLRub_HI4Jbk3/view)
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* [VGGFace2@BaiduDrive](https://pan.baidu.com/s/1c3KeLzy), [VGGFace2@GoogleDrive](https://drive.google.com/open?id=1KORwx_DWyIScAjD6vbo4CSRu048APoum)
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2. move dataset to ${MobileFaceNet_TF_ROOT}/datasets.
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3. run ${MobileFaceNet_TF_ROOT}/utils/data_process.py.
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## Training
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### MobileFaceNet
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```
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train_nets.py --max_epoch=10
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--train_batch_size=128
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--model_type=0 # mobilefacenet
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```
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### TinyMobileFaceNet
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```
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train_nets.py --max_epoch=10
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--train_batch_size=128
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--model_type=1 # tinymobilefacenet
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```
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## Inference
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### MobileFaceNet
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```
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python inference.py --pretrained_model='./output/ckpt_best/mobilefacenet_best_ckpt'
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--model_type=0
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```
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### TinyMobileFaceNet
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```
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python inference.py --pretrained_model='./output/ckpt_best/tinymobilefacenet_best_ckpt'
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--model_type=1
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```
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## Performance
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### [Original result](https://github.com/xsr-ai/MobileFaceNet_TF)
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| size | LFW(%) | Val@1e-3(%) | inference@MSM8976(ms) |
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| ------ | ------ | ----------- | --------------------- |
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| 5.7M | 99.25+ | 96.8+ | 260- |
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### My training results
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| Models | LFW | Cfp_FF | Cfp_FP | Agedb_30 | inference@i7-7700 16G 240G (fps) |
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|------------|:---:|:--:|:--:|:--:|:--:|
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|MobileFaceNet(Bad training)|0.983+-0.008|0.980+-0.005|0.827+-0.019|0.878+-0.023|27|
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|Tiny_MobileFaceNet|0.981+-0.008|0.984+-0.006|0.835+-0.019|0.882+-0.023|50|
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## References
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1. [facenet](https://github.com/davidsandberg/facenet)
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2. [InsightFace mxnet](https://github.com/deepinsight/insightface)
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3. [InsightFace_TF](https://github.com/auroua/InsightFace_TF)
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4. [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://arxiv.org/abs/1804.07573)
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5. [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://arxiv.org/abs/1801.09414)
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6. [InsightFace : Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
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7. [tensorflow-triplet-loss](https://github.com/omoindrot/tensorflow-triplet-loss)
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8. [MobileFaceNet_TF](https://github.com/xsr-ai/MobileFaceNet_TF)

‎arch/img/MobileFaceNet.jpg

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