README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: class_label
  splits:
    - name: train
      num_bytes: 29320000000
      num_examples: 5179510
  download_size: 26157852001
  dataset_size: 29320000000
license: cc-by-4.0
task_categories:
  - image-classification
tags:
  - face-recognition
  - ms1mv3
  - prosopo
pretty_name: Prosopo Large Dataset (MS1MV3)
size_categories:
  - 1M<n<10M

🧪 Prosopo Large Dataset (MS1MV3)

5.1 Million high-quality aligned face images for state-of-the-art model training.

This dataset serves as the core training engine for the Prosopo face recognition system. It contains the MS1MV3 (MS-Celeb-1M Cleaned) dataset, pre-aligned and packed into the high-performance MXNet RecordIO format.

📊 Dataset Statistics

Metric	Value
Identities	93,431
Total Images	5,179,510
Image Size	112 x 112 px
Alignment	RetinaFace (5-point landmark)
Format	MXNet RecordIO (Packed Binary)
Total Size	~28 GB (Unpacked)

📁 Content Structure

The dataset is provided as a ZIP archive containing the following RecordIO files:

• train.rec: The Data (27.3 GB) - All images packed
• train.idx: The Index (97 MB) - Offsets for random access
• train.lst: The Metadata (411 MB) - Path/Label/Index map

It also includes standard validation benchmarks:

• lfw.bin, agedb_30.bin, cfp_fp.bin

🚀 Usage

You can download the zip file and extract it to your training environment.

from huggingface_hub import hf_hub_download
import zipfile

# Download
zip_path = hf_hub_download(repo_id="inanxr/prosopo-large-dataset", filename="prosopo-large-dataset.zip", repo_type="dataset")

# Extract
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
    zip_ref.extractall("./ms1m_dataset")

📜 Acknowledgements

Original Data Source: MS-Celeb-1M (Cleaned by InsightFace/DeepGlint)

InanXR/Prosopo re-hosting for reproducibility. Guo, Yandong, et al. "Ms-celeb-1m: A dataset and benchmark for large-scale face recognition." ECCV 2016.