SoloFace2: A Large-Scale Single-Face Dataset with Landmarks

SoloFace2 is a large-scale face detection and landmark regression dataset optimized for resource-constrained deployment (TinyML, edge AI, microcontrollers). Every image contains exactly one visible human face or none at all, making it ideal for training lightweight single-face detectors.

Key Features

• 5 facial landmarks per face (eyes, nose, mouth corners)
• Single-face only — no multi-face ambiguity
• Stratified splits — 80/10/10 train/val/test, stratified by face presence
• 5× augmentation (WIDER FACE, SoloFace train) — geometric + color + crop
• 224×224 JPEGs — ready-to-train, quality 95
• Multiple sources for diversity: studio portraits, natural scenes, crowds, backgrounds

Dataset Statistics

Split	Face (p=1)	No-face (p=0)	Total
Train	46,689	67,147	113,836
Val	5,792	8,397	14,189
Test	5,909	8,387	14,296
Total	58,390	83,931	142,321

Face:No-face = 1:1.44 overall (41% face). Training uses 50/50 batch sampling.

Source Composition

Source	Generator	Face	No-face	Description
SoloFace train	RetinaFace	27,498	28,862	COCO-derived, 5× augmented
SoloFace test	YuNet	1,669	180	COCO-derived, raw images
SoloFace val	YuNet	196	21	COCO-derived, raw images
WIDER FACE train	RetinaFace	22,255	0	Natural scenes, 5× augmented
WIDER FACE val	RetinaFace	5,175	0	Natural scenes, 5× augmented
COCO no-person	YuNet	1,597	54,868	Person-free scenes
Total		58,390	83,931

Label Format

Each image has a corresponding row in labels.csv:

Column	Type	Description
image	str	JPEG filename
p	int	1 = face present, 0 = no face
x	int	Bbox left edge (224×224 pixels)
y	int	Bbox top edge
w	int	Bbox width
h	int	Bbox height
right_eye_x, right_eye_y	int	Right eye center
left_eye_x, left_eye_y	int	Left eye center
nose_x, nose_y	int	Nose tip
right_mouth_x, right_mouth_y	int	Right mouth corner
left_mouth_x, left_mouth_y	int	Left mouth corner
score	float	Detection confidence (0.0 when p=0)

Coordinate space: All integers are in 224×224 pixel coordinates. Divide by 224 to normalize to [0, 1].

Landmark order (subject's perspective): 0. Right eye | 1. Left eye | 2. Nose tip | 3. Right mouth | 4. Left mouth

Face Detection Models

Labels were generated by two complementary face detectors:

Model	Usage	Output
RetinaFace (insightface)	WIDER FACE, SoloFace train	Bbox + 5 landmarks
YuNet (OpenCV)	SoloFace test/val, COCO	Bbox + 5 landmarks

Both models target the same 5 anatomical points. The column order is normalized to YuNet convention in the CSV.

Data Augmentation (5×)

Applied to WIDER FACE and SoloFace training images (matching original SoloFace):

Augmentation	Parameters
Rotation	±15° random
Scaling	±20% random
Horizontal flip	50% probability
Brightness	±30% random
Contrast	±30% random
Random crop	Up to 10% from edges

All augmentations preserve bounding box and landmark consistency. Augmented variants are named *_aug0.jpg through *_aug4.jpg.

Splits

Train/val/test splits are 80/10/10, stratified by the p column. The splits.csv file maps each image to its split.

split_dataset() using sklearn.model_selection.train_test_split
  stratify on column p
  random_state = 42
  train : val : test = 80 : 10 : 10

Download & Extract

Images are split across 5 tar.gz archives (~640 MB each, 3.2 GB total). VGGFace2 images are excluded:

images_part_01.tar.gz   images_part_03.tar.gz   images_part_05.tar.gz
images_part_02.tar.gz   images_part_04.tar.gz

Option 1: Extract script (recommended)

# Download all images_part_*.tar.gz files, then:
python extract_images.py

Option 2: Manual extraction

# Linux/Mac
for f in images_part_*.tar.gz; do tar -xzf "$f"; done

# Windows (PowerShell)
Get-ChildItem images_part_*.tar.gz | ForEach-Object { tar -xzf $_ }

File Structure

soloface2/
├── README.md              ← This dataset card
├── images/                ← 142,321 JPEG images (224×224, quality 95)
├── images_part_01.tar.gz  ← Image archive chunk 1
├── ...
├── images_part_05.tar.gz  ← Image archive chunk 5
├── extract_images.py      ← Archive extraction script
├── labels.csv             ← All labels (image, p, bbox, landmarks, score)
├── splits.csv             ← Train/val/test split assignments
└── generate_splits.py     ← Split generation script (reproducibility)

Intended Use

1. Training lightweight face detection models for edge deployment
2. Facial landmark regression on resource-constrained hardware
3. Single-face detection benchmarking
4. TinyML model compression and quantization research
5. Privacy-preserving on-device face detection

Usage

import pandas as pd
from pathlib import Path
from PIL import Image

# Load labels and splits
labels = pd.read_csv("labels.csv")
splits = pd.read_csv("splits.csv")
df = labels.merge(splits, on="image")

# Training subset
train = df[df["split"] == "train"]
row = train.iloc[0]
img = Image.open(f"images/{row['image']}")  # 224×224 RGB

# Normalize coordinates to [0, 1]
DATA_SIZE = 224
bbox = [row["x"] / DATA_SIZE, row["y"] / DATA_SIZE,
        row["w"] / DATA_SIZE, row["h"] / DATA_SIZE]
kps = [
    (row["right_eye_x"] / DATA_SIZE, row["right_eye_y"] / DATA_SIZE),
    (row["left_eye_x"] / DATA_SIZE, row["left_eye_y"] / DATA_SIZE),
    (row["nose_x"] / DATA_SIZE, row["nose_y"] / DATA_SIZE),
    (row["right_mouth_x"] / DATA_SIZE, row["right_mouth_y"] / DATA_SIZE),
    (row["left_mouth_x"] / DATA_SIZE, row["left_mouth_y"] / DATA_SIZE),
]

License

Source	License
VGGFace2	Research use only
SoloFace	CC BY 4.0
WIDER FACE	Research use
COCO 2017	CC BY 4.0

This dataset is for research purposes only. Ensure compliance with VGGFace2 and WIDER FACE terms before commercial use.

Citation

@dataset{soloface2,
  title     = {SoloFace2: A Large-Scale Single-Face Dataset with Landmarks},
  author    = {Saha, Bidyut and Samanta, Riya},
  year      = {2026},
  publisher = {Hugging Face},
  note      = {Extended from SoloFace (10.5281/zenodo.14474899) with
               WIDER FACE and RetinaFace landmarks}
}

Contact

• Bidyut Saha: sahabidyut999@gmail.com
• Riya Samanta: study.riya1792@gmail.com