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recognition/partial_fc/README.md
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## Partial-FC
Partial FC is a distributed deep learning training framework for face recognition. The goal of Partial FC is to make
training large scale classification task (eg. 10 or 100 millions identies) fast and easy. It is faster than model parallel
and can train more identities.
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.
![Image text](https://github.com/nttstar/insightface-resources/blob/master/images/partial_speed1.png)
@@ -15,14 +13,13 @@ and can train more identities.
- [Citation](#Citation)
## Glint360k
We clean, merge, and release the **largest** and **cleanest** face recognition dataset **Glint360k**.
Baseline models trained on Glint360k with our proposed training strategy can easily achieve state-of-the-art.
The released dataset contains 18 million images of 360K individuals. The performance of Glint360k eval on large-scale
test set IFRT, IJB-C and Megaface are as follows:
## 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`** means the negative class centers sampling rate.
**`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 |
@@ -31,17 +28,16 @@ test set IFRT, IJB-C and Megaface are as follows:
| R100 | **Glint360k**(r=0.1) | **90.45** | **94.60** | **93.96** | 63.91 | 88.23 |
#### Evaluation on IJB-C and Megaface
Our backbone is ResNet100, we set feature scale s to 64 and cosine margin m of CosFace at 0.4.
TAR@FAR=1e-4 is reported on the IJB-C datasets, TAR@FAR=1e-6 is reported on Megaface verification.
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 Pan**](https://pan.baidu.com/s/1aHC_nJGKzKgwJKoVb2Q_Gg) (code:i1al)
Google Drive coming soon
[**Baidu Drive**](https://pan.baidu.com/s/1aHC_nJGKzKgwJKoVb2Q_Gg) (code:i1al)
[**Dropbox**](https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0)
Refer to the following command to unzip.
```
@@ -54,7 +50,7 @@ tar -xvf glint360k.tar
## Performance
We remove the influence of IO, all experiments use mixed precision training, backbone is ResNet50.
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 |
@@ -71,7 +67,7 @@ We remove the influence of IO, all experiments use mixed precision training, bac
## Citation
If you find Partical-FC or Glint360k useful in your research, please consider to cite the following related papers:
If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper:
[Partial FC](https://arxiv.org/abs/2010.05222)
```
@@ -79,7 +75,7 @@ If you find Partical-FC or Glint360k useful in your research, please consider to
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},
booktitle={Arxiv 2010.05222},
year={2020}
}
```