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Face Detection Dataset
Small grayscale face crops + a large pool of natural-image negatives, packaged for classical face detectors (Viola-Jones, Haar cascades, sliding-window classifiers).
At a glance
| Split | Rows | Faces / Non-faces |
|---|---|---|
train |
106,977 | 102,429 / 4,548 |
test |
24,045 | 472 / 23,573 (CBCL benchmark) |
negatives |
29,879 | — / 29,879 (Caltech-256) |
Same schema across all splits: image, label (0/1), source, category.
Quick start
from datasets import load_dataset
ds = load_dataset("salvacarrion/face-detection")
# All faces for training
faces = ds["train"].filter(lambda x: x["label"] == 1)
# CBCL benchmark for evaluation
test = ds["test"]
# Hard-negative pool (raw color JPGs at native resolution)
negs = ds["negatives"]
Face sources (train split)
CelebA ships in two paired variants at the same index — same person, two crop styles:
celeba— loose portrait framing (hair and jaw visible).celeba_aligned— tight Viola-Jones crop (eyes pinned to fixed pixel positions, face fills the 48×48 frame).
CBCL is already canonically aligned, so it has a single variant.
source |
Label | Count | Notes |
|---|---|---|---|
celeba |
1 | 50,000 | Loose portrait. Filtered for frontal pose using the manual CelebA landmarks. |
celeba_aligned |
1 | 50,000 | Same 50,000 faces tightly warped to CBCL-matched geometry. |
cbcl |
1 | 2,429 | MIT CBCL #1, upsampled from native 19×19. Already canonically aligned. |
cbcl |
0 | 4,548 | CBCL non-faces — matched-domain seed for stage 1 of cascade training. |
Picking source == "cbcl" from train returns both faces and non-faces —
filter by label to pick.
Test split
The classic CBCL Viola-Jones benchmark, untouched. Faces and non-faces live together in one split so accuracy is one pass.
Negatives split
Caltech-256 source JPGs (native color, varying resolution). Categories
containing face, people, or human are excluded. Sample patches at
whatever resolution your detector needs.
Aligned geometry (the *_aligned variants)
Two-point similarity transform — face shape preserved, no stretching:
- Left eye → (12, 10) in a 48×48 frame
- Right eye → (36, 10)
The canonical positions were measured empirically from the mean of CBCL
training faces, so models trained on celeba_aligned generalize cleanly to
the CBCL test benchmark.
License
Research / non-commercial use only. Cite the original sources:
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