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92 lines
4.2 KiB
Markdown
92 lines
4.2 KiB
Markdown
## Partial-FC
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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.
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## Contents
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[Partial FC](https://arxiv.org/abs/2010.05222)
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- [Largest Face Recognition Dataset: **Glint360k**](#Glint360k)
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- [Distributed Training Performance](#Performance)
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- [Citation](#Citation)
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## Glint360K
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We clean, merge, and release the **largest** and **cleanest** face recognition dataset **Glint360K**, which contains 18 million images of 360K individuals.
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By employing the Patial FC training strategy, baseline models trained on Glint360K can easily achieve state-of-the-art performance.
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Detailed evaluation results on the large-scale test set (e.g. IFRT, IJB-C and Megaface) are as follows:
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#### Evaluation on IFRT
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**`r`** denotes the sampling rate of negative class centers.
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| Backbone | Dataset | African | Caucasian | Indian | Asian | ALL |
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| ------------ | ----------- | ----- | ----- | ------ | ----- | ----- |
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| R50 | MS1M-V3 | 76.24 | 86.21 | 84.44 | 37.43 | 71.02 |
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| R124 | MS1M-V3 | 81.08 | 89.06 | 87.53 | 38.40 | 74.76 |
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| R100 | **Glint360k**(r=1.0) | 89.50 | 94.23 | 93.54 | **65.07** | **88.67** |
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| R100 | **Glint360k**(r=0.1) | **90.45** | **94.60** | **93.96** | 63.91 | 88.23 |
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#### Evaluation on IJB-C and Megaface
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We employ ResNet100 as the backbone and CosFace (m=0.4) as the loss function.
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TAR@FAR=1e-4 is reported on the IJB-C datasets, and TAR@FAR=1e-6 is reported on the Megaface dataset.
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|Test Dataset | IJB-C | Megaface_Id | Megaface_Ver |
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| :--- | :---: | :---: | :---: |
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| MS1MV2 | 96.4 | 98.3 | 98.6 |
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|**Glint360k** | **97.3** | **99.1** | **99.1** |
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#### Download
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[**Baidu Drive**](https://pan.baidu.com/s/1aHC_nJGKzKgwJKoVb2Q_Gg) (code:i1al)
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Refer to the following command to unzip.
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```
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8b469dabd959b51f5ae63f1113fa9a7e glint360k00
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25449911e03766bf773b12abcc5789cd glint360k01
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bbd236acb4b561def6071f94b423d85e glint360k02
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bd392b1506dddc5cead1b9de1e7b6e52 glint360k03
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163aec95c3e258a79df7f1cb563fd5ef glint360k04
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41bbf8314e24f98405ecbad8e9c89dfd glint360k05
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dcc2f021aa2a9463ff1b2a8021ef87b6 glint360k06
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cat glint360k* > glint360k.tar
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tar -xvf glint360k.tar
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# md5sum:
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# train.rec 2a74c71c4d20e770273f103eda97e878
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# train.idx f7a3e98d3533ac481bdf3dc03a5416e8
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```
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Use [unpack_glint360k.py](./unpack_glint360k.py) to unpack.
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## Performance
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We neglect the influence of IO. All experiments use mixed-precision training, and the backbone is ResNet50.
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#### 1 Million Identities On 8 RTX2080Ti
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|Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W |
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| :--- | :---: | :---: | :---: | :---: | :---: |
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| Model Parallel | 8 | 1024 | 10408 | 2390 | GPU |
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| **Partial FC(Ours)** | **8** | **1024** | **8100** | **2780** | GPU |
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#### 10 Million Identities On 64 RTX2080Ti
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|Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W |
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| :--- | :---: | :---: | :---: | :---: | :---: |
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| Model Parallel | 64 | 2048 | 9684 | 4483 | GPU |
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| **Partial FC(Ours)** | **64** | **4096** | **6722** | **12600** | GPU |
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## Citation
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If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper:
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[Partial FC](https://arxiv.org/abs/2010.05222)
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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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