mirror of
https://github.com/deepinsight/insightface.git
synced 2026-05-16 13:46:15 +00:00
108 lines
6.0 KiB
Markdown
108 lines
6.0 KiB
Markdown
# Arcface Pytorch (Distributed Version of ArcFace)
|
|
|
|
## Contents
|
|
|
|
## Set Up
|
|
```shell
|
|
torch >= 1.6.0
|
|
```
|
|
More details see [eval.md](docs/install.md) in docs.
|
|
|
|
## Training
|
|
### 1. Single node, 1 GPUs:
|
|
```shell
|
|
python -m torch.distributed.launch --nproc_per_node=1 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py
|
|
```
|
|
### 2. Single node, 8 GPUs:
|
|
```shell
|
|
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py
|
|
```
|
|
### 3. Multiple nodes, each node 8 GPUs:
|
|
Node 0:
|
|
```shell
|
|
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py
|
|
```
|
|
Node 1:
|
|
```shell
|
|
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py
|
|
```
|
|
|
|
|
|
## Evaluation IJBC
|
|
More details see [eval.md](docs/eval.md) in docs.
|
|
|
|
## Speed Benchmark
|
|
|
|
|
|
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. Training speed of different parallel methods (samples/second), Tesla V100 32GB * 8. (Larger is better)
|
|
|
|
| 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. GPU memory cost of different parallel methods (GB per GPU), Tesla V100 32GB * 8. (Smaller is better)
|
|
|
|
| Method | Bs128-R100-2 Million Identities | Bs128-R50-4 Million Identities | Bs64-R50-8 Million Identities |
|
|
| :--- | :--- | :--- | :--- |
|
|
| Data Parallel | OOM | OOM | OOM |
|
|
| Model Parallel | 27.3 | 30.3 | 32.1 |
|
|
| Fp16 + Model Parallel | 20.3 | 26.6 | 32.1 |
|
|
| Fp16 + Partial Fc 0.1 | 11.9 | 10.8 | 11.1 |
|
|
|
|
|
|
## Model Zoo
|
|
|
|
The models are available for non-commercial research purposes only.
|
|
|
|
All Model Can be found in here.
|
|
[Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw
|
|
|
|
### MS1MV3
|
|
| Datasets | log | backbone | IJBC(1e-05) | IJBC(1e-04) |agedb30|cfp_fp|lfw |
|
|
| :---: | :--- | :--- | :--- | :--- |:--- |:--- |:--- |
|
|
| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18_fp16/training.log) | r18-fp16 | 92.07 | 94.66 | 97.77 | 97.73 | 99.77 |
|
|
| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34_fp16/training.log) | r34-fp16 | 94.10 | 95.90 | 98.10 | 98.67 | 99.80 |
|
|
| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log) | r50-fp16 | 94.79 | 96.46 | 98.35 | 98.96 | 99.83 |
|
|
| MS1MV3-Arcface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100_fp16/training.log) | r100-fp16 | 95.31 | 96.81 | 98.48 | 99.06 | 99.85 |
|
|
|
|
### Glint360k
|
|
| Datasets | log |backbone | IJBC(1e-05) | IJBC(1e-04) |agedb30|cfp_fp|lfw |
|
|
| :---: | :--- |:--- | :--- | :--- |:--- |:--- |:--- |
|
|
| Glint360k-Cosface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r18_fp16_0.1/training.log) |r18-fp16-0.1 | 93.16 | 95.33 | 97.72 | 97.73 | 99.77 |
|
|
| Glint360k-Cosface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r34_fp16_0.1/training.log) |r34-fp16-0.1 | 95.16 | 96.56 | 98.33 | 98.78 | 99.82 |
|
|
| Glint360k-Cosface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r50_fp16_0.1/training.log) |r50-fp16-0.1 | 95.61 | 96.97 | 98.38 | 99.20 | 99.83 |
|
|
| Glint360k-Cosface |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|r100-fp16-0.1 | 95.88 | 97.32 | 98.48 | 99.29 | 99.82 |
|
|
|
|
0.1 means sample rate is 0.1.
|
|
|
|
More details see [eval.md](docs/modelzoo.md) in docs.
|
|
|
|
|
|
## Test
|
|
We test on PyTorch versions 1.6.0, 1.7.1, and 1.8.0. Please create an issue if you are having trouble.
|
|
|
|
## 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}
|
|
}
|
|
```
|