InsightFace_Pytorch

Pytorch0.4 codes for InsightFace

---

1.Intro

• This repo is a reimplementation of Arcface(paper), or Insightface(github)
• For models, including the pytorch implementation of the backbone modules of Arcface and MobileFacenet
• Codes for transform MXNET data records in Insightface(github) to Image Datafolders are provided
• Pretrained models are posted, include the MobileFacenet and IR-SE50 in the original paper

---

2. Pretrained Models & Performance

[MobileFaceNet@BaiduDrive](Coming Soon), [@GoogleDrive](Coming Soon) (coming soon)

LFW(%)	CFP-FF(%)	CFP-FP(%)	AgeDB-30(%)	calfw(%)	cplfw(%)	vgg2_fp(%)
0.9952	0.9962	0.9504	0.9622	0.9557	0.9107	0.9386

[LResNet50E-IR@BaiduDrive](Coming Soon), [@GoogleDrive](Coming Soon) (coming soon)

LFW(%)	CFP-FF(%)	CFP-FP(%)	AgeDB-30(%)	calfw(%)	cplfw(%)	vgg2_fp(%)
?	?	?	?	?	?	?

3.How to use

• clone

  git clone https://github.com/TropComplique/mtcnn-pytorch.git
  

3.1Data Preparation

3.1.1 Prepare Facebank (For testing over camera or video)

Provide the face images your want to detect in the data/face_bank folder, and guarantee it have a structure like following:

data/facebank/
        ---> id1/
            ---> id1_1.jpg
        ---> id2/
            ---> id2_1.jpg
        ---> id3/
            ---> id3_1.jpg
           ---> id3_2.jpg

3.1.2 download the pretrained model to work_space/model

If more than 1 image appears in one folder, an average embedding will be calculated

3.2.2 Prepare Dataset ( For training)

download the refined dataset from original post: (emore recommended)

• Refined-MS1M@BaiduDrive, Refined-MS1M@GoogleDrive

• VGGFace2@BaiduDrive, VGGFace2@GoogleDrive

• emore dataset @ BaiduDrive, emore dataset @ OneDrive

• after unzip the files to 'data' path, run :

  python prepare_data.py
  

after the execution, you should find following structure:

faces_emore/
            ---> agedb_30
            ---> calfw
            ---> cfp_ff
            --->  cfp_fp
            ---> cfp_fp
            ---> cplfw
            --->imgs
            ---> lfw
            ---> vgg2_fp

---

detect over camera:

• 2 download the desired weights [Mobilefacenet] , [IR-SE50]to model folder [Mobilefacenet]:https://pan.baidu.com/s/1PwHjtGLAmAoG5LJkQk5LSQ [IR-SE50]:https://pan.baidu.com/s/1PwHjtGLAmAoG5LJkQk5LSQ

• 3 to take a picture, run

  python take_pic.py -n name
  

  press q to take a picture, it will only capture 1 highest possibility face if more than 1 person appear in the camera

• 4 or you can put any preexisting photo into the facebank directory, the file structure is following:

• facebank/ name1/ photo1.jpg photo2.jpg ... name2/ photo1.jpg photo2.jpg ... ..... if more than 1 image appears in the directory, average embedding will be calculated

• 5 to start

  python face_verify.py 
  

---

detect over video:

```
python infer_on_video.py -f [video file name] -s [save file name]
```

the video file should be inside the data/face_bank folder

• Video Detection Demo @Youtube

Training:

```
python train.py -b [batch_size] -lr [learning rate] -e [epochs]
```