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.
2019.02.08: 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: TVM-Benchmark
2018.10.28: Gender-Age created with a lightweight model. About 1MB size, 10ms on single CPU core. Gender accuracy 96% on validation set and 4.1 age MAE.
2018.10.16: We got rank 1st on IQIYI_VID(IQIYI video person identification) competition which in conjunction with PRCV2018, see detail.
2018.06.14: There's a large scale Asian training dataset provided by Glint, see this discussion for detail.
2018.02.13: We achieved state-of-the-art performance on MegaFace-Challenge. Please check our paper and code for implementation details.
- 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 full 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{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)