InsightFace_Pytorch
Pytorch0.4.1 codes for InsightFace
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
IR-SE50 @ BaiduNetdisk, IR-SE50 @ Onedrive
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 |
Mobilefacenet @ BaiduNetDisk, Mobilefacenet @ OneDrive
LFW(%) | CFP-FF(%) | CFP-FP(%) | AgeDB-30(%) | calfw(%) | cplfw(%) | vgg2_fp(%) |
---|---|---|---|---|---|---|
0.9918 | 0.9891 | 0.8986 | 0.9347 | 0.9402 | 0.866 | 0.9100 |
clone
git clone https://github.com/TropComplique/mtcnn-pytorch.git
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
If more than 1 image appears in one folder, an average embedding will be calculated
download the refined dataset: (emore recommended)
More Dataset please refer to the original post
Note: If you use the refined MS1M dataset and the cropped VGG2 dataset, please cite the original papers.
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
download the desired weights to model folder:
2 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
3 or you can put any preexisting photo into the facebank directory, the file structure is as 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
4 to start
python face_verify.py
``` 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
``` python train.py -b [batch_size] -lr [learning rate] -e [epochs] # python train.py -net mobilefacenet -b 200 -w 4 ```
This repo is mainly inspired by deepinsight/insightface and InsightFace_TF
PRs are welcome, in case that I don't have the resource to train some large models like the 100 and 151 layers model
Email : treb1en@qq.com
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