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Update Speed Benchmark.
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anxiangsir committed Mar 12, 2021
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Expand Up @@ -45,6 +45,31 @@ CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
```
More details see [eval.md](docs/eval.md) in docs.

## Speed Benchmark
![Image text](https://github.com/nttstar/insightface-resources/blob/master/images/partial_fc_speed.png)

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.

1. Different Parallel Methods Training Speed

| Method | Bs128-r100-2 Million Identities | Bs128-r50-4 Million Identities | Bs64-r50-8 Million Identities |
| :--- | :--- | :--- | :--- |
| Data Parallel | 1 | 1 | 1 |
| Model Parallel | 1362 | 1600 | 482 |
| Fp16 + Model Parallel | 2006 | 2165 | 767 |
| Fp16 + Partial Fc 0.1 | 3247 | 4385 | 3001 |

2. Different Parallel Methods GPU Memory

| Method | Bs128-r100-2 Million Identities | Bs128-r50-4 Million Identities | Bs64-r50-8 Million Identities |
| :--- | :--- | :--- | :--- |
| Data Parallel | OOM | OOM | OOM |
| Model Parallel | 27382 | 30322 | 32182 |
| Fp16 + Model Parallel | 20310 | 26622 | 32182 |
| Fp16 + Partial Fc 0.1 | 11987 | 10890 | 11108 |


## Model Zoo

Expand All @@ -68,4 +93,24 @@ All Model Can be found in here.
| 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 |
| 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 |

More details see [eval.md](docs/modelzoo.md) in docs.
More details see [eval.md](docs/modelzoo.md) in docs.



## 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}
}
```

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