Glint360K face recognition dataset

glint360k (7 files)
glint360k_05 20.40GB
glint360k_06 6.18GB
glint360k_03 20.40GB
glint360k_04 20.40GB
glint360k_01 20.40GB
glint360k_02 20.40GB
glint360k_00 20.40GB
Type: Dataset
Tags:

Bibtex:
@article{,
title= {Glint360K face recognition dataset},
journal= {},
author= {},
year= {},
url= {https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc},
abstract= {Glint360K contains **`17091657`** images of **`360232`** 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:

# 1. 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 |

### 2. 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**     |

# 3. License 

The Glint360K dataset (and the models trained with this dataset) are available for non-commercial research purposes only.

Refer to the following command to unzip.
```
cat glint360k_* | tar -xzvf -

# Don't forget the last '-'!

# cf7433cbb915ac422230ba33176f4625  glint360k_00
# 589a5ea3ab59f283d2b5dd3242bc027a  glint360k_01
# 8d54fdd5b1e4cd55e1b9a714d76d1075  glint360k_02
# cd7f008579dbed9c5af4d1275915d95e  glint360k_03
# 64666b324911b47334cc824f5f836d4c  glint360k_04
# a318e4d32493dd5be6b94dd48f9943ac  glint360k_05
# c3ae1dcbecea360d2ec2a43a7b6f1d94  glint360k_06
# md5sum:
# 5d9cd9f262ec87a5ca2eac5e703f7cdf train.idx
# 8483be5af6f9906e19f85dee49132f8e train.rec
```
Use unpack_glint360k.py to unpack.


## Citation
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/2203.15565)
```
@inproceedings{an2022pfc,
  title={Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
  author={An, Xiang and Deng, Jiangkang and Guo, Jia and Feng, Ziyong and Zhu, Xuhan and Jing, Yang and Tongliang, Liu},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

```
},
keywords= {},
terms= {},
license= {},
superseded= {}
}

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