By Jia Guo and Jiankang Deng
The code of InsightFace is released under the MIT License.
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In this repository, we provide training data, network settings and loss designs for deep face recognition. The training data includes the normalised MS1M, VGG2 and CASIA-Webface datasets, which were already packed in MXNet binary format. The network backbones include ResNet, MobilefaceNet, MobileNet, InceptionResNet_v2, DenseNet, DPN. The loss functions include Softmax, SphereFace, CosineFace, ArcFace and Triplet (Euclidean/Angular) Loss.
Our method, ArcFace, was initially described in an arXiv technical report. By using this repository, you can simply achieve LFW 99.80%+ and Megaface 98%+ by a single model. This repository can help researcher/engineer to develop deep face recognition algorithms quickly by only two steps: download the binary dataset and run the training script.
All face images are aligned by MTCNN and cropped to 112x112:
Please check Dataset-Zoo for detail information and dataset downloading.
- Please check src/data/face2rec2.py on how to build a binary face dataset. Any public available MTCNN can be used to align the faces, and the performance should not change. We will improve the face normalisation step by full pose alignment methods recently.
LResNet100E-IR network trained on MS1M-Arcface dataset with ArcFace loss:
| Method | LFW(%) | CFP-FP(%) | AgeDB-30(%) |
|---|---|---|---|
| Ours | 99.80+ | 98.0+ | 98.20+ |
You can use $INSIGHTFACE/src/eval/verification.py to test all the pre-trained models.
Please check Model-Zoo for more pretrained models.
A combined margin method was proposed as a function of target logits value and original θ:
COM(θ) = cos(m_1*θ+m_2) - m_3
For training with m1=1.0, m2=0.3, m3=0.2, run following command:
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train_softmax.py --network r100 --loss combined --dataset emore
Results by using MS1M-IBUG(MS1M-V1)
| Method | m1 | m2 | m3 | LFW | CFP-FP | AgeDB-30 |
|---|---|---|---|---|---|---|
| W&F Norm Softmax | 1 | 0 | 0 | 99.28 | 88.50 | 95.13 |
| SphereFace | 1.5 | 0 | 0 | 99.76 | 94.17 | 97.30 |
| CosineFace | 1 | 0 | 0.35 | 99.80 | 94.4 | 97.91 |
| ArcFace | 1 | 0.5 | 0 | 99.83 | 94.04 | 98.08 |
| Combined Margin | 1.2 | 0.4 | 0 | 99.80 | 94.08 | 98.05 |
| Combined Margin | 1.1 | 0 | 0.35 | 99.81 | 94.50 | 98.08 |
| Combined Margin | 1 | 0.3 | 0.2 | 99.83 | 94.51 | 98.13 |
| Combined Margin | 0.9 | 0.4 | 0.15 | 99.83 | 94.20 | 98.16 |
In this part, we assume you are in the directory $INSIGHTFACE_ROOT/deploy/. The input face image should be generally centre cropped. We use RNet+ONet of MTCNN to further align the image before sending it to the feature embedding network.
- Prepare a pre-trained model.
- Put the model under
$INSIGHTFACE_ROOT/models/. For example,$INSIGHTFACE_ROOT/models/model-r100-ii. - Run the test script
$INSIGHTFACE_ROOT/deploy/test.py.
For single cropped face image(112x112), total inference time is only 17ms on our testing server(Intel E5-2660 @ 2.00GHz, Tesla M40, LResNet34E-IR).
Todo
Todo
If you find InsightFace useful in your research, please consider to cite the following related papers:
@inproceedings{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
booktitle={CVPR},
year={2019}
}