@@ -9,6 +9,10 @@ torch >= 1.6.0
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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
@@ -42,39 +46,26 @@ CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
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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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-
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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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+
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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.
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