By Jia Guo and Jiankang Deng
The code of InsightFace is released under the MIT License.
Please click the image to watch the Youtube video. For Bilibili users, click here.
2019.04.14: We will launch a Light-weight Face Recognition challenge/workshop on ICCV 2019.
2019.04.04: Arcface achieved state-of-the-art performance (5/109) on the NIST Face Recognition Vendor Test (FRVT) (1:1 verification)
report (name: Imperial-000). Our solution is based on [MS1MV2+DeepGlintAsian, ResNet100, ArcFace loss].
2019.02.08: Please check https://github.com/deepinsight/insightface/tree/master/recognition for our parallel training code which can easily and efficiently support one million identities on a single machine (8* 1080ti).
2018.12.13: Inference acceleration TVM-Benchmark.
2018.10.28: Light-weight attribute model Gender-Age. About 1MB, 10ms on single CPU core. Gender accuracy 96% on validation set and 4.1 age MAE.
2018.10.16: We achieved state-of-the-art performance on Trillionpairs (name: nttstar) and IQIYI_VID (name: WitcheR).
- Introduction
- Training Data
- Train
- Pretrained Models
- Verification Results On Combined Margin
- Test on MegaFace
- 512-D Feature Embedding
- Third-party Re-implementation
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 ful 8000 l pose alignment methods recently.
- Install
MXNetwith GPU support (Python 2.7).
pip install mxnet-cu90
- Clone the InsightFace repository. We call the directory insightface as
INSIGHTFACE_ROOT.
git clone --recursive https://github.com/deepinsight/insightface.git
- Download the training set (
MS1M-Arcface) and place it in$INSIGHTFACE_ROOT/datasets/. Each training dataset includes at least following 6 files:
faces_emore/
train.idx
train.rec
property
lfw.bin
cfp_fp.bin
agedb_30.binThe first three files are the training dataset while the last three files are verification sets.
- Train deep face recognition models.
In this part, we assume you are in the directory
$INSIGHTFACE_ROOT/recognition/.
export MXNET_CPU_WORKER_NTHREADS=24
export MXNET_ENGINE_TYPE=ThreadedEnginePerDevicePlace and edit config file:
cp sample_config.py config.py
vim config.py # edit dataset path etc..We give some examples below. Our experiments were conducted on the Tesla P40 GPU.
(1). Train ArcFace with LResNet100E-IR.
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r100 --loss arcface --dataset emoreIt will output verification results of LFW, CFP-FP and AgeDB-30 every 2000 batches. You can check all options in config.py. This model can achieve LFW 99.80+ and MegaFace 98.3%+.
(2). Train CosineFace with LResNet50E-IR.
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network r50 --loss cosface --dataset emore(3). Train Softmax with LMobileNet-GAP.
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network m1 --loss softmax --dataset emore(4). Fine-turn the above Softmax model with Triplet loss.
CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --network m1 --loss triplet --lr 0.005 --pretrained ./models/m1-softmax-emore,1- Verification results.
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 |
Please check $INSIGHTFACE_ROOT/Evaluation/megaface/ to evaluate the model accuracy on Megaface. All aligned images were already provided.
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).
- TensorFlow: InsightFace_TF
- TensorFlow: tf-insightface
- PyTorch: InsightFace_Pytorch
- PyTorch: arcface-pytorch
- Caffe: arcface-caffe
- Caffe: CombinedMargin-caffe
- Tensorflow: InsightFace-tensorflow
Todo
Todo
If you find InsightFace useful in your research, please consider to cite the following related papers:
@inproceedings{guo2018stacked,
title={Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment},
author={Guo, Jia and Deng, Jiankang and Xue, Niannan and Zafeiriou, Stefanos},
booktitle={BMVC},
year={2018}
}
@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}
}
[Jia Guo](guojia[at]gmail.com)
[Jiankang Deng](jiankangdeng[at]gmail.com)