---
task_categories:
- image-feature-extraction
- image-classification
tags:
- face_recognition
pretty_name: Glint360K
size_categories:
- 10M<n<100M
---
# Dataset Card for Glint360K

Citiation by [InsightFace Repository](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc)

> We clean, merge, and release the largest and cleanest face recognition dataset Glint360K, which 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:

## Dataset Details

### Dataset Description


- **Curated by:** [InsightFace](https://insightface.ai/)
- **Shared by:** [Academic Torrents](https://academictorrents.com/details/e5f46ee502b9e76da8cc3a0e4f7c17e4000c7b1e)
- **License:** The license isn't clearly stated, but InsightFace says it's for "available for non-commercial research purposes only."

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [deepinsight/insightface](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc)
- **Paper:** [https://arxiv.org/abs/2010.05222](https://arxiv.org/abs/2010.05222)


## Uses
It is used for training face recognition models such as RetinaFace, FaceNet, etc.

## Dataset Structure

It adopts the WebDataset format, with images and metadata (class: cls) stored in tar files split every 16GB.

## Dataset Creation

### Source Data
Get Data from torrent and concatenate divided tar files.
Next, extract the tar file.
Finally, the directory structure is as follows:
```sh
.\glint360k\
├── agedb_30.bin
├── calfw.bin
├── cfp_ff.bin
├── cfp_fp.bin
├── cplfw.bin
├── lfw.bin
├── train.idx
├── train.rec
└── vgg2_fp.bin
```
#### Data Collection and Processing

use train.rec and train.idx.
Save the following script and run it with `uv run script.py`.

```py
# /// script
# dependencies = [
#     "mxnet",
#     "numpy=<1.24",
#     "Pillow",
#     "tqdm",
# ]
# requires-python = "==3.10.*"
# ///
import mxnet
import os
from PIL import Image
from tqdm import tqdm

glint360k_root = "/path/to/glint360k"
idx_path = os.path.join(glint360k_root, "train.idx")
rec_path = os.path.join(glint360k_root, "train.rec")
export_path = "/path/to/glint360k_export"

imgrec = mxnet.recordio.MXIndexedRecordIO(idx_path, rec_path, 'r')

print(f"Total records to process: {imgrec.keys.__len__()}")

for i in tqdm(imgrec.keys):
    header, content = mxnet.recordio.unpack(imgrec.read_idx(i))
    label = int(header.label if isinstance(header.label, (int, float)) else header.label[0])
    label_dir = os.path.join(export_path, str(label))
    if not os.path.exists(label_dir):
        os.makedirs(label_dir, exist_ok=True)
    
    img = mxnet.image.imdecode(content).asnumpy()
    img = Image.fromarray(img.astype('uint8'))
    img_save_path = os.path.join(label_dir, f'{i}.jpg')
    img.save(img_save_path, quality=93)

print("Export complete.")
```
This converts Glint360K to PyTorch ImageFolder format. To convert it to WebDataset format, follow the steps below.

```py
# /// script
# dependencies = [
#   "webdataset",
#   "torchvision",
#   "tqdm",
#   "torch",
# ]
# requires-python = ">=3.9"
# ///
import os
import webdataset
from torchvision import datasets
from tqdm import tqdm

imagefolder_path = "/path/to/glint360k_export"

output_prefix = "/path/to/glint360k_WebDataset/glint360k_train"

dataset = datasets.ImageFolder(
    root=imagefolder_path,
    transform=None
)

with webdataset.ShardWriter(f"{output_prefix}-%02d.tar", maxsize=1.1e+10, maxcount=float('inf')) as writer:
    for image_path, label in tqdm(dataset.imgs, desc="Converting to WebDataset"):
        with open(image_path, "rb") as image_file:
            image_bytes = image_file.read()

        basename = os.path.splitext(os.path.basename(image_path))[0]

        sample = {
            "__key__": basename,
            "jpg": image_bytes,
            "cls": str(label)
        }

        writer.write(sample)

print("Conversion to WebDataset format complete.")
```

#### Personal and Sensitive Information

This dataset collects human faces and contains personally identifiable information. Please handle it with care.

## Bias, Risks, and Limitations
The dataset may not consider the diversity of race, gender, age distribution, and shooting environments in the included facial images. This may lead to biases against specific groups.