CPLFW HF-ready

This folder packages the local CPLFW (Cross-Pose LFW) aligned images as a Hugging Face imagefolder dataset with a verification-pairs CSV.

Layout

cplfw/
├── README.md
├── pairs.csv
└── train/
    ├── images/<shard>/<file>.jpg
    └── metadata.csv

metadata.csv columns

• file_name: relative image path used by ImageFolder, e.g. images/000/AJ_Cook_1.jpg.
• label: numeric identity label.
• label_name / identity: identity name.
• image_num: per-identity image index from the original filename.
• source_filename: original CPLFW filename.

pairs.csv columns

pairs.csv mirrors pairs_CPLFW.txt (6000 verification pairs; 3000 same-identity cross-pose positives and 3000 negatives, split into 10 folds of 600 pairs each following the LFW convention).

• pair_id (0..5999), fold_id (1..10), fold_position (0..299).
• is_same: 1 for positive pairs, 0 for negatives.
• image_a, image_b: bare filenames as in the upstream pairs file.
• image_a_path, image_b_path: paths under the train split.

Local Stats

• Images: 11648
• Unique identities: 3929
• Identities with one image: 21
• Verification pairs: 6000 (3000 positive / 3000 negative)
• Folds: 10 x 600 pairs

Skipped upstream files

These four upstream files have malformed names (typos like _3jpg.jpg, .jip.jpg, or - instead of _). They are not referenced by any verification pair, so the benchmark is unaffected.

• Landon_Donovan_3jpg.jpg
• Leni_Bjorklund-2.jpg
• Leni_Bjorklund-3.jpg
• Mike_Montgomery_3.jip.jpg

Loading

from datasets import load_dataset

ds = load_dataset("imagefolder", data_dir="data/evaluation/huggingface/cplfw")
train = ds["train"]

import pandas as pd

pairs = pd.read_csv("data/evaluation/huggingface/cplfw/pairs.csv")

Notes

CPLFW (Cross-Pose LFW) is described by its authors as a verification benchmark emphasizing pose variation between the two faces in each positive pair. Check the original dataset terms before publishing or redistributing it.