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Update pretrain model.
1. Add training log and pretrain
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recognition/arcface_torch/README.md

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```
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## Train on a single node
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If you want to use 8 GPU to train, you should set `--nproc_per_node=8` and set `CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 `
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If you want to use 4 GPU to train, you should set `--nproc_per_node=4` and set `CUDA_VISIBLE_DEVICES=0,1,2,3`
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If you want to use 1 GPU to train, you should set `--nproc_per_node=1` ...
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```shell
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export OMP_NUM_THREADS=4
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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More details see [eval.md](docs/eval.md) in docs.
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## Model Zoo
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### MS1MV3
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| Datasets | backbone | IJBC(1e-05) | IJBC(1e-04) |agedb30|cfp_fp|lfw |
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| :---: | :--- | :--- | :--- |:--- |:--- |:--- |
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| MS1MV3-Arcface | r18 | 92.08 | 94.68 |97.65 |97.63 |99.73|
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| MS1MV3-Arcface | r34 | | | | | |
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| MS1MV3-Arcface | r50 | 94.79 | 96.43 |98.28 |98.89 |99.85|
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| MS1MV3-Arcface | r50-amp | 94.72 | 96.41 |98.30 |99.06 |99.85|
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| MS1MV3-Arcface | r100 | 95.22 | 96.87 |98.45 |99.19 |99.85|
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### Glint360k
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| Datasets | backbone | IJBC(1e-05) | IJBC(1e-04) |agedb30|cfp_fp|lfw |
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| :---: | :--- | :--- | :--- |:--- |:--- |:--- |
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| Glint360k-Cosface | r100 | 96.19 | 97.39 |98.52 |99.26 |99.83|
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## Model Zoo
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More details see [eval.md](docs/modelzoo.md) in docs.
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The models are available for non-commercial research purposes only.
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All Model 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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### MS1MV3
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| Datasets | log | backbone | IJBC(1e-05) | IJBC(1e-04) |agedb30|cfp_fp|lfw |
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| :---: | :--- | :--- | :--- | :--- |:--- |:--- |:--- |
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| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18/training.log) | r18 | 92.08 | 94.68 | 97.65 | 97.63 | 99.73 |
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| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34/training.log) | r34 | 94.13 | 95.98 | 98.05 | 98.60 | 99.80 |
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| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50/training.log) | r50 | 94.79 | 96.43 | 98.28 | 98.89 | 99.85 |
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| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log) | r50-fp16 | 94.72 | 96.41 | 98.30 | 99.06 | 99.85 |
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| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100/training.log) | r100 | 95.22 | 96.87 | 98.45 | 99.19 | 99.85 |
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### Glint360k
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| Datasets | log |backbone | IJBC(1e-05) | IJBC(1e-04) |agedb30|cfp_fp|lfw |
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| :---: | :--- |:--- | :--- | :--- |:--- |:--- |:--- |
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| Glint360k-Cosface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100/training.log) |r100 | 96.19 | 97.39 | 98.52 | 99.26 | 99.83 |
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| Glint360k-Cosface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|r100-fp16-sample-0.1 | 95.95 | 97.35 | 98.57 | 99.30 | 99.85 |
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## Citation
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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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More details see [eval.md](docs/modelzoo.md) in docs.

recognition/arcface_torch/backbones/iresnet.py

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class IBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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groups=1, base_width=64, dilation=1):
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super(IBasicBlock, self).__init__()

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