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90 lines
3.9 KiB
Markdown
90 lines
3.9 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 make
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training large scale classification task (eg. 10 or 100 millions identies) fast and easy. It is faster than model parallel
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and can train more identities.
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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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- [Installtion](./docs/installtion.md)
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- [How to run](./docs/how_to_run.md)
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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**.
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Baseline models trained on Glint360k with our proposed training strategy can easily achieve state-of-the-art.
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The released dataset contains 18 million images of 360K individuals. The performance of Glint360k eval on large-scale
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test set IFRT, IJB-C and Megaface are as follows:
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#### Evaluation on IFRT
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**`r`** means the negative class centers sampling rate.
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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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Our backbone is ResNet100, we set feature scale s to 64 and cosine margin m of CosFace at 0.4.
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TAR@FAR=1e-4 is reported on the IJB-C datasets, TAR@FAR=1e-6 is reported on Megaface verification.
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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 Pan**](https://pan.baidu.com/s/1aHC_nJGKzKgwJKoVb2Q_Gg) (code:i1al)
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Google Drive coming soon
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Refer to the following command to unzip.
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```
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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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## Performance
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We remove the influence of IO, all experiments use mixed precision training, 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 Partical-FC or Glint360k useful in your research, please consider to cite the following related papers:
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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},
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year={2020}
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}
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```
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