chenxuanhong
137 lines
9.6 KiB
Markdown
137 lines
9.6 KiB
Markdown
# Distributed Arcface Training in Pytorch
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This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions
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identity on a single server.
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## Requirements
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- Install [PyTorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md).
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- (Optional) Install [DALI](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), our doc for [install_dali.md](docs/install_dali.md).
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- `pip install -r requirement.txt`.
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## How to Training
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To train a model, run `train.py` with the path to the configs.
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The example commands below show how to run
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distributed training.
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### 1. To run on a machine with 8 GPUs:
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```shell
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python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=12581 train.py configs/ms1mv3_r50_lr02
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```
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### 2. To run on 2 machines with 8 GPUs each:
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Node 0:
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```shell
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python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train.py configs/webface42m_r100_lr01_pfc02_bs4k_16gpus
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```
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Node 1:
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```shell
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python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train.py configs/webface42m_r100_lr01_pfc02_bs4k_16gpus
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```
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## Download Datasets or Prepare Datasets
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- [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-retinaface) (93k IDs, 5.2M images)
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- [Glint360K](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download) (360k IDs, 17.1M images)
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- [WebFace42M](docs/prepare_webface42m.md) (2M IDs, 42.5M images)
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## Model Zoo
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- The models are available for non-commercial research purposes only.
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- All models can be found in here.
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- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw
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- [OneDrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
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### Performance on IJB-C and [**ICCV2021-MFR**](https://github.com/deepinsight/insightface/blob/master/challenges/mfr/README.md)
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ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face
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recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities.
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As the result, we can evaluate the FAIR performance for different algorithms.
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For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The
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globalised multi-racial testset contains 242,143 identities and 1,624,305 images.
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| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Training Throughout | log |
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|:-------------------------|:-----------|:------------|:------------|:------------|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| MS1MV3 | mobileface | 65.76 | 94.44 | 91.85 | ~13000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_mobileface_lr02/training.log)\|[config](configs/ms1mv3_mobileface_lr02.py) |
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| Glint360K | mobileface | 69.83 | 95.17 | 92.58 | -11000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_mobileface_lr02_bs4k/training.log)\|[config](configs/glint360k_mobileface_lr02_bs4k.py) |
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| WebFace42M-PartialFC-0.2 | mobileface | 73.80 | 95.40 | 92.64 | (16GPUs)~18583 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_mobilefacenet_pfc02_bs8k_16gpus/training.log)\|[config](configs/webface42m_mobilefacenet_pfc02_bs8k_16gpus.py) |
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| MS1MV3 | r100 | 83.23 | 96.88 | 95.31 | ~3400 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r100_lr02/training.log)\|[config](configs/ms1mv3_r100_lr02.py) |
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| Glint360K | r100 | 90.86 | 97.53 | 96.43 | ~5000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r100_lr02_bs4k_16gpus/training.log)\|[config](configs/glint360k_r100_lr02_bs4k_16gpus.py) |
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| WebFace42M-PartialFC-0.2 | r50(bs4k) | 93.83 | 97.53 | 96.16 | (8 GPUs)~5900 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_bs4k_pfc02/training.log)\|[config](configs/webface42m_r50_lr01_pfc02_bs4k_8gpus.py) |
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| WebFace42M-PartialFC-0.2 | r50(bs8k) | 93.96 | 97.46 | 96.12 | (16GPUs)~11000 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_lr01_pfc02_bs8k_16gpus/training.log)\|[config](configs/webface42m_r50_lr01_pfc02_bs8k_16gpus.py) |
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| WebFace42M-PartialFC-0.2 | r50(bs4k) | 94.04 | 97.48 | 95.94 | (32GPUs)~17000 | log\|[config](configs/webface42m_r50_lr01_pfc02_bs4k_32gpus.py) |
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| WebFace42M-PartialFC-0.2 | r100(bs4k) | 96.69 | 97.85 | 96.63 | (16GPUs)~5200 | [log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r100_bs4k_pfc02/training.log)\|[config](configs/webface42m_r100_lr01_pfc02_bs4k_16gpus.py) |
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| WebFace42M-PartialFC-0.2 | r200 | - | - | - | - | log\|config |
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`PartialFC-0.2` means negivate class centers sample rate is 0.2.
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## Speed Benchmark
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`arcface_torch` can train large-scale face recognition training set efficiently and quickly. When the number of
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classes in training sets is greater than 1 Million, partial fc sampling strategy will get same
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accuracy with several times faster training performance and smaller GPU memory.
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Partial FC is a sparse variant of the model parallel architecture for large sacle face recognition. Partial FC use a
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sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a
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sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC,
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we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed
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training and mixed precision training.
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More details see
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[speed_benchmark.md](docs/speed_benchmark.md) in docs.
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### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better)
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`-` means training failed because of gpu memory limitations.
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| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
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|:--------------------------------|:--------------|:---------------|:---------------|
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| 125000 | 4681 | 4824 | 5004 |
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| 1400000 | **1672** | 3043 | 4738 |
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| 5500000 | **-** | **1389** | 3975 |
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| 8000000 | **-** | **-** | 3565 |
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| 16000000 | **-** | **-** | 2679 |
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| 29000000 | **-** | **-** | **1855** |
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### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better)
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| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
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|:--------------------------------|:--------------|:---------------|:---------------|
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| 125000 | 7358 | 5306 | 4868 |
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| 1400000 | 32252 | 11178 | 6056 |
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| 5500000 | **-** | 32188 | 9854 |
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| 8000000 | **-** | **-** | 12310 |
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| 16000000 | **-** | **-** | 19950 |
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| 29000000 | **-** | **-** | 32324 |
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## Citations
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```
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@inproceedings{deng2019arcface,
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title={Arcface: Additive angular margin loss for deep face recognition},
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author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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pages={4690--4699},
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year={2019}
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}
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@inproceedings{an2020partical_fc,
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title={Partial FC: Training 10 Million Identities on a Single Machine},
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author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
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Zhang, Debing and Fu Ying},
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booktitle={Arxiv 2010.05222},
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year={2020}
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}
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```
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