--- license: other task_categories: - image-classification - zero-shot-image-classification tags: - face-recognition - face-verification - cross-age - calfw - imagefolder pretty_name: CALFW HF-ready size_categories: - 10K/.jpg │ └── metadata.csv └── raw/ ├── images//.jpg └── metadata.csv ``` ## metadata.csv columns - `file_name`: relative image path used by `ImageFolder`, e.g. `images/000/AJ_Cook_0001.jpg`. - `label`: numeric identity label, shared across the two splits. - `label_name` / `identity`: identity name corresponding to `label`. - `image_num`: per-identity image index from the original filename (e.g. `0001`). - `variant`: `aligned` or `raw`. - `source_filename`: original CALFW filename. ## pairs.csv columns `pairs.csv` mirrors `txts/pairs_CALFW.txt` (6000 verification pairs; 3000 same-identity cross-age positives and 3000 negatives, split into 10 folds of 600 pairs each). - `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_aligned_path`, `image_b_aligned_path`: path under the `aligned` split. - `image_a_raw_path`, `image_b_raw_path`: path under the `raw` split. ## Local Stats - Images per split: 12174 - Unique identities: 4025 - Identities with one image: 1 - Verification pairs: 6000 (3000 positive / 3000 negative) - Folds: 10 x 600 pairs ## Loading ```python from datasets import load_dataset ds = load_dataset("imagefolder", data_dir="data/evaluation/huggingface/calfw") ds["aligned"], ds["raw"] ``` ```python import pandas as pd pairs = pd.read_csv("data/evaluation/huggingface/calfw/pairs.csv") ``` ## Notes CALFW (Cross-Age LFW) is described by its authors as a verification benchmark that emphasizes age variation between the two faces in each positive pair. Check the original dataset terms before publishing or redistributing it.