insightface/recognition/partial_fc/README.md

Partial-FC

Partial FC is a distributed deep learning training framework for face recognition. The goal of Partial FC is to facilitate large-scale classification task (e.g. 10 or 100 million identities). It is much faster than the model parallel solution and there is no performance drop.

Contents

Partial FC

• Largest Face Recognition Dataset: Glint360k
• Installtion
• How to run
• Distributed Training Performance
• Citation

Glint360K

We clean, merge, and release the largest and cleanest face recognition dataset Glint360K, which contains 18 million images of 360K individuals. By employing the Patial FC training strategy, baseline models trained on Glint360K can easily achieve state-of-the-art performance. Detailed evaluation results on the large-scale test set (e.g. IFRT, IJB-C and Megaface) are as follows:

Evaluation on IFRT

r denotes the sampling rate of negative class centers.

Backbone	Dataset	African	Caucasian	Indian	Asian	ALL
R50	MS1M-V3	76.24	86.21	84.44	37.43	71.02
R124	MS1M-V3	81.08	89.06	87.53	38.40	74.76
R100	Glint360k(r=1.0)	89.50	94.23	93.54	65.07	88.67
R100	Glint360k(r=0.1)	90.45	94.60	93.96	63.91	88.23

Evaluation on IJB-C and Megaface

We employ ResNet100 as the backbone and CosFace (m=0.4) as the loss function. TAR@FAR=1e-4 is reported on the IJB-C datasets, and TAR@FAR=1e-6 is reported on the Megaface dataset.

Test Dataset	IJB-C	Megaface_Id	Megaface_Ver
MS1MV2	96.4	98.3	98.6
Glint360k	97.3	99.1	99.1

Download

Baidu Drive (code:i1al)

Refer to the following command to unzip.

cat glint360k* > glint360k.tar
tar -xvf glint360k.tar
# md5sum:
# train.rec 2a74c71c4d20e770273f103eda97e878
# train.idx f7a3e98d3533ac481bdf3dc03a5416e8

Use unpack_glint360k.py to unpack.

Train

Requirements

python==3.6
cuda==10.1
cudnn==765
mxnet-cu101==1.6.0.post0
pip install easydict mxboard opencv-python tqdm
nccl
openmpi==4.0.0
horovod==0.19.2

Failures due to SSH issues

The host where horovodrun is executed must be able to SSH to all other hosts without any prompts.

Run with horovodrun

Typically one GPU will be allocated per process, so if a server has 8 GPUs, you will run 8 processes. In horovodrun, the number of processes is specified with the -np flag.

To run on a machine with 8 GPUs:

horovodrun -np 8 -H localhost:8 bash config.sh

To run on two machine with 16 GPUs:

horovodrun -np 16 -H ip1:8,ip2:8 bash config.sh

Run with mpi

bash run.sh

Performance

We neglect the influence of IO. All experiments use mixed-precision training, and the backbone is ResNet50.

1 Million Identities On 8 RTX2080Ti

Method	GPUs	BatchSize	Memory/M	Throughput img/sec	W
Model Parallel	8	1024	10408	2390	GPU
Partial FC(Ours)	8	1024	8100	2780	GPU

10 Million Identities On 64 RTX2080Ti

Method	GPUs	BatchSize	Memory/M	Throughput img/sec	W
Model Parallel	64	2048	9684	4483	GPU
Partial FC(Ours)	64	4096	6722	12600	GPU

Citation

If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper:

Partial FC

@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}
}