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arcface_torch

README.md

Distributed Arcface Training in Pytorch

The "arcface_torch" repository is the official implementation of the ArcFace algorithm. It supports distributed and sparse training with multiple distributed training examples, including several memory-saving techniques such as mixed precision training and gradient checkpointing. It also supports training for ViT models and datasets including WebFace42M and Glint360K, two of the largest open-source datasets. Additionally, the repository comes with a built-in tool for converting to ONNX format, making it easy to submit to MFR evaluation systems.

PWC
PWC
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Requirements

To avail the latest features of PyTorch, we have upgraded to version 1.12.0.

How to Training

To train a model, execute the train_v2.py script with the path to the configuration files. The sample commands provided below demonstrate the process of conducting distributed training.

1. To run on one GPU:

python train_v2.py configs/ms1mv3_r50_onegpu

Note:
It is not recommended to use a single GPU for training, as this may result in longer training times and suboptimal performance. For best results, we suggest using multiple GPUs or a GPU cluster.

2. To run on a machine with 8 GPUs:

torchrun --nproc_per_node=8 train_v2.py configs/ms1mv3_r50

3. To run on 2 machines with 8 GPUs each:

Node 0:

torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train_v2.py configs/wf42m_pfc02_16gpus_r100

Node 1:

torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train_v2.py configs/wf42m_pfc02_16gpus_r100

4. Run ViT-B on a machine with 24k batchsize:

torchrun --nproc_per_node=8 train_v2.py configs/wf42m_pfc03_40epoch_8gpu_vit_b

Download Datasets or Prepare Datasets

Note: If you want to use DALI for data reading, please use the script 'scripts/shuffle_rec.py' to shuffle the InsightFace style rec before using it.
Example:

python scripts/shuffle_rec.py ms1m-retinaface-t1

You will get the "shuffled_ms1m-retinaface-t1" folder, where the samples in the "train.rec" file are shuffled.

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 IJB-C and 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.

1. Training on Single-Host GPU

Datasets Backbone MFR-ALL IJB-C(1E-4) IJB-C(1E-5) log
MS1MV2 mobilefacenet-0.45G 62.07 93.61 90.28 click me
MS1MV2 r50 75.13 95.97 94.07 click me
MS1MV2 r100 78.12 96.37 94.27 click me
MS1MV3 mobilefacenet-0.45G 63.78 94.23 91.33 click me
MS1MV3 r50 79.14 96.37 94.47 click me
MS1MV3 r100 81.97 96.85 95.02 click me
Glint360K mobilefacenet-0.45G 70.18 95.04 92.62 click me
Glint360K r50 86.34 97.16 95.81 click me
Glint360k r100 89.52 97.55 96.38 click me
WF4M r100 89.87 97.19 95.48 click me
WF12M-PFC-0.2 r100 94.75 97.60 95.90 click me
WF12M-PFC-0.3 r100 94.71 97.64 96.01 click me
WF12M r100 94.69 97.59 95.97 click me
WF42M-PFC-0.2 r100 96.27 97.70 96.31 click me
WF42M-PFC-0.2 ViT-T-1.5G 92.04 97.27 95.68 click me
WF42M-PFC-0.3 ViT-B-11G 97.16 97.91 97.05 click me

2. Training on Multi-Host GPU

Datasets Backbone(bs*gpus) MFR-ALL IJB-C(1E-4) IJB-C(1E-5) Throughout log
WF42M-PFC-0.2 r50(512*8) 93.83 97.53 96.16 ~5900 click me
WF42M-PFC-0.2 r50(512*16) 93.96 97.46 96.12 ~11000 click me
WF42M-PFC-0.2 r50(128*32) 94.04 97.48 95.94 ~17000 click me
WF42M-PFC-0.2 r100(128*16) 96.28 97.80 96.57 ~5200 click me
WF42M-PFC-0.2 r100(256*16) 96.69 97.85 96.63 ~5200 click me
WF42M-PFC-0.0018 r100(512*32) 93.08 97.51 95.88 ~10000 click me
WF42M-PFC-0.2 r100(128*32) 96.57 97.83 96.50 ~9800 click me

r100(128*32) means backbone is r100, batchsize per gpu is 128, the number of gpus is 32.

3. ViT For Face Recognition

Datasets Backbone(bs) FLOPs MFR-ALL IJB-C(1E-4) IJB-C(1E-5) Throughout log
WF42M-PFC-0.3 r18(128*32) 2.6 79.13 95.77 93.36 - click me
WF42M-PFC-0.3 r50(128*32) 6.3 94.03 97.48 95.94 - click me
WF42M-PFC-0.3 r100(128*32) 12.1 96.69 97.82 96.45 - click me
WF42M-PFC-0.3 r200(128*32) 23.5 97.70 97.97 96.93 - click me
WF42M-PFC-0.3 VIT-T(384*64) 1.5 92.24 97.31 95.97 ~35000 click me
WF42M-PFC-0.3 VIT-S(384*64) 5.7 95.87 97.73 96.57 ~25000 click me
WF42M-PFC-0.3 VIT-B(384*64) 11.4 97.42 97.90 97.04 ~13800 click me
WF42M-PFC-0.3 VIT-L(384*64) 25.3 97.85 98.00 97.23 ~9406 click me

WF42M means WebFace42M, PFC-0.3 means negivate class centers sample rate is 0.3.

4. Noisy Datasets

Datasets Backbone MFR-ALL IJB-C(1E-4) IJB-C(1E-5) log
WF12M-Flip(40%) r50 43.87 88.35 80.78 click me
WF12M-Flip(40%)-PFC-0.1* r50 80.20 96.11 93.79 click me
WF12M-Conflict r50 79.93 95.30 91.56 click me
WF12M-Conflict-PFC-0.3* r50 91.68 97.28 95.75 click me

WF12M means WebFace12M, +PFC-0.1* denotes additional abnormal inter-class filtering.

Speed Benchmark

Arcface-Torch is an efficient tool for training large-scale face recognition training sets. When the number of classes in the training sets exceeds one million, the partial FC sampling strategy maintains the same accuracy while providing several times faster training performance and lower GPU memory utilization. The partial FC is a sparse variant of the model parallel architecture for large-scale face recognition, utilizing a sparse softmax that dynamically samples a subset of class centers for each training batch. During each iteration, only a sparse portion of the parameters are updated, leading to a significant reduction in GPU memory requirements and computational demands. With the partial FC approach, it is possible to train sets with up to 29 million identities, the largest to date. Furthermore, the partial FC method supports multi-machine distributed training and mixed precision training.

More details see speed_benchmark.md in docs.

  1. Training Speed of Various Parallel Techniques (Samples per Second) on a Tesla V100 32GB x 8 System (Higher is Optimal)

- 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
  1. GPU Memory Utilization of Various Parallel Techniques (MB per GPU) on a Tesla V100 32GB x 8 System (Lower is Optimal)
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

Citations

@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={CVPR},
  year={2019}
}
@inproceedings{an2022partialfc,
    author={An, Xiang and Deng, Jiankang and Guo, Jia and Feng, Ziyong and Zhu, XuHan and Yang, Jing and Liu, Tongliang},
    title={Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
    booktitle={CVPR},
    year={2022},
}
@inproceedings{zhu2021webface260m,
  title={Webface260m: A benchmark unveiling the power of million-scale deep face recognition},
  author={Zhu, Zheng and Huang, Guan and Deng, Jiankang and Ye, Yun and Huang, Junjie and Chen, Xinze and Zhu, Jiagang and Yang, Tian and Lu, Jiwen and Du, Dalong and Zhou, Jie},
  booktitle={CVPR},
  year={2021}
}

Welcome!