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End of preview. Expand in Data Studio

Paired ArcFace Embeddings (CASIA-WebFace)

This dataset contains randomly paired face records from CASIA-WebFace embedded with ArcFace 512 (512-dimensional embeddings).

Introduced by Yi et al. in Learning Face Representation from Scratch The CASIA-WebFace dataset is used for face verification and face identification tasks.

The purpose of this dataset was to enable fast training of models learning from paired embeddings.

Record fields

  • image1_jpeg, image2_jpeg: JPEG bytes for each image in the pair
  • image1_metadata, image2_metadata: per-image metadata payloads
  • image1_embedding0, image2_embedding0: ArcFace embedding vectors

Arc2Face naming note

We picked ArcFace 512 for embeddings because Arc2Face diffusion can be used to generate debug face visualizations from embeddings.

See https://huggingface.co/FoivosPar/Arc2Face

Splits

  • train: 9 parquet files, 3.50 GB
  • validation: 20 parquet files, 345.90 MB

Example

from datasets import load_dataset

ds = load_dataset("scaleinvariant/paired-arcface-embeddings-casia-webface", split="train", streaming=True)
print(next(iter(ds)).keys())
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