arcface

arcface

The mxnet implementation is from deepinsight/insightface.

The pretrained model is LResNet50E-IR,ArcFace@ms1m-refine-v1.

The two input images used in this project are joey0.ppm and joey1.ppm, download them from Google Drive.. The input image is 112x112, and generated from get_input() in insightface/deploy/face_model.py, which is cropped and aligned face image.

Config

• FP16/FP32 can be selected by the macro USE_FP16 in arcface-r50.cpp
• GPU id can be selected by the macro DEVICE in arcface-r50.cpp

Run

1. generate arcface-r50.wts from mxnet implementation with LResNet50E-IR,ArcFace@ms1m-refine-v1 pretrained model

git clone https://github.com/deepinsight/insightface
cd insightface
git checkout 3866cd77a6896c934b51ed39e9651b791d78bb57
cd deploy
// copy tensorrtx/arcface/gen_wts.py to here(insightface/deploy)
// download model-r50-am-lfw.zip and unzip here(insightface/deploy)
python gen_wts.py
// a file 'arcface-r50.wts' will be generated.
// the master branch of insightface should work, if not, you can checkout 94ad870abb3203d6f31b049b70dd080dc8f33fca

2. put arcface-r50.wts into tensorrtx/arcface, build and run

cd tensorrtx/arcface
// download joey0.ppm and joey1.ppm, and put here(tensorrtx/arcface)
mkdir build
cd build
cmake ..
make
sudo ./arcface-r50 -s    // serialize model to plan file i.e. 'arcface-r50.engine'
sudo ./arcface-r50 -d    // deserialize plan file and run inference

3. check the output log, latency and similarity score.

More Information

See the readme in home page.