Distributed Arcface Training in Pytorch

This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions
identity on a single server.

Requirements

How to Training

To train a model, run train.py with the path to the configs:

1. Single node, 8 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50

2. Multiple nodes, each node 8 GPUs:

Node 0:

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50

Node 1:

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50

3.Training resnet2060 with 8 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r2060.py

Model Zoo

  • The models are available for non-commercial research purposes only.
  • All models can be found in here.
  • Baidu Yun Pan: e8pw
  • onedrive

Performance on ICCV2021-MFR

ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face
recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities.
As the result, we can evaluate the FAIR performance for different algorithms.

For ICCV2021-MFR-ALL set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The
globalised multi-racial testset contains 242,143 identities and 1,624,305 images.

For ICCV2021-MFR-MASK set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4).
Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images.
There are totally 13,928 positive pairs and 96,983,824 negative pairs.

Datasets backbone Training throughout Size / MB ICCV2021-MFR-MASK ICCV2021-MFR-ALL
MS1MV3 r18 – 91 47.85 68.33
Glint360k r18 8536 91 53.32 72.07
MS1MV3 r34 – 130 58.72 77.36
Glint360k r34 6344 130 65.10 83.02
MS1MV3 r50 5500 166 63.85 80.53
Glint360k r50 5136 166 70.23 87.08
MS1MV3 r100 – 248 69.09 84.31
Glint360k r100 3332 248 75.57 90.66
MS1MV3 mobilefacenet 12185 7.8 41.52 65.26
Glint360k mobilefacenet 11197 7.8 44.52 66.48

Performance on IJB-C and Verification Datasets

Datasets backbone IJBC(1e-05) IJBC(1e-04) agedb30 cfp_fp lfw log
MS1MV3 r18 92.07 94.66 97.77 97.73 99.77 log
MS1MV3 r34 94.10 95.90 98.10 98.67 99.80 log
MS1MV3 r50 94.79 96.46 98.35 98.96 99.83 log
MS1MV3 r100 95.31 96.81 98.48 99.06 99.85 log
MS1MV3 r2060 95.34 97.11 98.67 99.24 99.87 log
Glint360k r18-0.1 93.16 95.33 97.72 97.73 99.77 log
Glint360k r34-0.1 95.16 96.56 98.33 98.78 99.82 log
Glint360k r50-0.1 95.61 96.97 98.38 99.20 99.83 log
Glint360k r100-0.1 95.88 97.32 98.48 99.29 99.82 log

Speed Benchmark

Arcface Torch can train large-scale face recognition training set efficiently and quickly. When the number of
classes in training sets is greater than 300K and the training is sufficient, partial fc sampling strategy will get same
accuracy with several times faster training performance and smaller GPU memory.
Partial FC is a sparse variant of the model parallel architecture for large sacle face recognition. Partial FC use a
sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a
sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC,
we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed
training and mixed precision training.

Image text

More details see
speed_benchmark.md in docs.

1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better)

- means training failed because of gpu memory limitations.

Number of Identities in Dataset Data Parallel Model Parallel Partial FC 0.1
125000 4681 4824 5004
1400000 1672 3043 4738
5500000 – 1389 3975
8000000 – – 3565
16000000 – – 2679
29000000 – – 1855

2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better)

Number of Identities in Dataset Data Parallel Model Parallel Partial FC 0.1
125000 7358 5306 4868
1400000 32252 11178 6056
5500000 – 32188 9854
8000000 – – 12310
16000000 – – 19950
29000000 – – 32324

Evaluation ICCV2021-MFR and IJB-C

More details see eval.md in docs.

Test

We tested many versions of PyTorch. Please create an issue if you are having trouble.

  • torch 1.6.0
  • torch 1.7.1
  • torch 1.8.0
  • torch 1.9.0

Citation

@inproceedings{deng2019arcface,
  title={Arcface: Additive angular margin loss for deep face recognition},
  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4690--4699},
  year={2019}
}
@inproceedings{an2020partical_fc,
  title={Partial FC: Training 10 Million Identities on a Single Machine},
  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
  Zhang, Debing and Fu Ying},
  booktitle={Arxiv 2010.05222},
  year={2020}
}

GitHub

View Github