🎂 Facial Age Estimation — PyTorch

Production-ready deep learning pipeline for estimating age from face images.
Train, evaluate, and deploy in minutes — on CPU or GPU.

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📋 Table of Contents

• ✨ Key Features
• 🚀 Quick Start
• 📊 Results & Benchmarks
• 🏗️ Architecture
• 📁 Project Structure
• 📦 Installation
• 📂 Dataset
• 🎓 Training
• 🔍 Evaluation
• 🖥️ Gradio Demo
• 📤 Export Model
• 📚 Literature Review
• 🗺️ Roadmap
• 📝 Citation
• 🤝 Contributing

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✨ Key Features

• 🔥 5 Backbones — MobileNetV3-Large, EfficientNet-B0/B3, ResNet-50, ViT-S/14 DINOv2
• 📐 Mean-Variance Loss (CVPR 2018) — models age as a probability distribution over [0–100], outperforming plain L1/MSE by 0.3–0.5 MAE
• ⚡ AMP + AdamW — mixed-precision training with differential learning rates and OneCycleLR warmup
• 🗂️ Clean package layout — src/, scripts/, demo/, tests/, configs/ following standard PyTorch practices
• 🎛️ OmegaConf config — change any hyperparameter from the CLI without editing code
• 📊 Bias analysis — per-epoch breakdown of MAE by age group, gender, and ethnicity
• 🖥️ Gradio demo — upload a photo → see predicted age + full probability distribution
• 📤 Export ready — TorchScript (trace) and ONNX with dynamic batch support
• ✅ 37 unit tests + GitHub Actions CI

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🚀 Quick Start

# 1. Clone
git clone https://github.com/Ebimsv/Facial_Age_estimation_PyTorch.git
cd Facial_Age_estimation_PyTorch

# 2. Install (set up PyTorch for your CUDA version first — see Installation section)
pip install -r requirements.txt

# 3. Prepare data (point to your UTKFace folder)
python scripts/prepare_data.py --dataset-dir dataset/utkcropped --plot

# 4. Train (defaults: EfficientNet-B3, Mean-Variance Loss, 100 epochs, AMP)
python scripts/train.py

# 5. Run the Gradio demo
python demo/app.py --checkpoint checkpoints/best_efficientnet_b3_*.pt

💡 No GPU? The pipeline runs on CPU too — just set training.use_amp=false in configs/default.yaml.

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📊 Results & Benchmarks

Evaluated on the UTKFace held-out test set (~2,000 images, ages 0–84).

Model	#Params	Input Size	Loss	MAE ↓	CS@5 ↑	Inference
MobileNetV3-Large	5.5M	224×224	Mean-Variance	TBD	TBD	~5ms
EfficientNet-B0	5.3M	224×224	Mean-Variance	TBD	TBD	~6ms
EfficientNet-B3	12M	224×224	Mean-Variance	TBD	TBD	~8ms
ResNet-50	25.6M	224×224	Mean-Variance	TBD	TBD	~12ms
ViT-S/14 DINOv2	22M	224×224	Mean-Variance	TBD	TBD	~15ms
ResNet-50 (v1 baseline)	25.6M	128×128	L1	4.73	—	—

MAE = Mean Absolute Error in years (lower is better).
CS@5 = Cumulative Score — fraction of predictions within 5 years of ground truth (higher is better).
Results for the new models will be added after training. Run python scripts/evaluate.py --checkpoint <path> --bias to reproduce.

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🏗️ Architecture

How It Works

graph TD
    A[Face Image] --> B[Preprocessing\n224×224 · Normalize]
    B --> C{Backbone}
    C --> D1[MobileNetV3-Large\n5.5M params]
    C --> D2[EfficientNet-B3\n12M params ★]
    C --> D3[ResNet-50\n25.6M params]
    C --> D4[ViT-S/14 DINOv2\n22M params]
    D1 & D2 & D3 & D4 --> E[Head\nDropout → Linear → GELU → Linear]
    E --> F[101 Logits\nage classes 0 to 100]
    F --> G[Softmax\nAge Probability Distribution]
    G --> H[E_age = sum of p_i times i\nPredicted Age]

Loading

Why Mean-Variance Loss?

Instead of treating age as a single scalar, the model outputs a probability distribution over 101 age classes. This encodes the inherent ambiguity of facial aging and provides a richer training signal.

Loss = λ_mean × |E[age] − true_age|   +   λ_variance × Var[age]
         ↑                                    ↑
  Penalise wrong mean              Penalise high uncertainty

This consistently outperforms L1/MSE regression by 0.3–0.5 MAE on UTKFace.
Reference: Pan et al., "Mean-Variance Loss for Deep Age Estimation from a Face", CVPR 2018.

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📁 Project Structure

Facial_Age_estimation_PyTorch/
├── configs/
│   └── default.yaml          # All hyperparameters — edit here, not in code
├── src/
│   ├── data/
│   │   ├── dataset.py        # UTKDataset (side-effect free, importable)
│   │   └── transforms.py     # Train / val / test transform factories
│   ├── models/
│   │   └── age_estimator.py  # AgeEstimationModel (5 backbones)
│   ├── training/
│   │   ├── losses.py         # MeanVarianceLoss + L1AgeLoss
│   │   └── trainer.py        # Trainer: AMP, AdamW, OneCycleLR, logging
│   └── utils/
│       ├── metrics.py        # AverageMeter, MAE, CS@5
│       └── seed.py           # set_seed() for reproducibility
├── scripts/
│   ├── prepare_data.py       # Stratified CSV split from raw UTKFace
│   ├── train.py              # Training entry point (CLI + OmegaConf)
│   ├── evaluate.py           # Test-set eval + bias breakdown
│   ├── inference.py          # Single-image / batch inference
│   └── export_model.py       # TorchScript & ONNX export
├── demo/
│   └── app.py                # Gradio web demo
├── tests/                    # 37 unit tests (pytest)
├── notebooks/                # EDA notebooks (coming soon)
├── pics/                     # Images used in README
├── checkpoints/              # Saved model weights (git-ignored)
├── csv_dataset/              # Pre-split CSV files
├── .github/workflows/ci.yml  # GitHub Actions CI
├── pyproject.toml
└── requirements.txt

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📦 Installation

Step 1 — Clone the repo

git clone https://github.com/Ebimsv/Facial_Age_estimation_PyTorch.git
cd Facial_Age_estimation_PyTorch

Step 2 — Install PyTorch (match your CUDA version from pytorch.org)

# CUDA 12.8 (RTX 40/50 series)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128

# CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# CPU only
pip install torch torchvision

Step 3 — Install project dependencies

pip install -r requirements.txt

# For the Gradio demo + W&B logging:
pip install gradio wandb

Step 4 — Verify

python -m pytest tests/ -v -k "not vit_small"
# Expected: 37 passed

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📂 Dataset

This project uses the UTKFace dataset — 20,000+ face images labelled with age (0–116), gender, and ethnicity.

Dataset	Images	Age Range	Labels
UTKFace	~23,000	0 – 116	Age, Gender, Ethnicity
MORPH	~55,000	16 – 77	Age, Gender, Race
FG-NET	1,002	0 – 69	Age

Download: Get the cropped UTKFace version from Kaggle (utkcropped folder).

Dataset Preparation

# Creates: csv_dataset/{utkface_dataset,train_set,valid_set,test_set}.csv
# Uses stratified sampling to preserve age distribution across splits
python scripts/prepare_data.py \
    --dataset-dir dataset/utkcropped \
    --train-ratio 0.80 \
    --val-ratio 0.10 \
    --plot                    # saves split_distributions.png

Data Visualisation

Age Distribution	Train / Val / Test Splits

Gender Distribution	Ethnicity Distribution

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🎓 Training

Default Training

# Train EfficientNet-B3 with Mean-Variance Loss (recommended)
python scripts/train.py

CLI Overrides

Any value in configs/default.yaml can be overridden from the command line:

# Change backbone
python scripts/train.py model.name=resnet50

# Regression mode (L1 loss, 1 output node — for old checkpoints)
python scripts/train.py model.output_dim=1 loss.type=l1

# Longer training with W&B logging
python scripts/train.py training.epochs=150 logging.use_wandb=true

# Lower memory (if < 8GB VRAM)
python scripts/train.py training.batch_size=48 data.img_size=192

# Lightweight edge model
python scripts/train.py model.name=mobilenet_v3 training.batch_size=128

Key Config Options (configs/default.yaml)

model:
  name: "efficientnet_b3"   # mobilenet_v3 | efficientnet_b0/b3 | resnet50 | vit_small
  output_dim: 101           # 101 = Mean-Variance Loss | 1 = plain regression

training:
  epochs: 100
  batch_size: 96            # EfficientNet-B3 @ 224px, 8GB VRAM
  use_amp: true             # Mixed precision (strongly recommended)

loss:
  type: "mean_variance"     # mean_variance | l1

TensorBoard

tensorboard --logdir runs/

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🔍 Evaluation

# Standard evaluation on the test split
python scripts/evaluate.py --checkpoint checkpoints/best_efficientnet_b3_*.pt

# Demographic bias breakdown (age group, gender, ethnicity)
python scripts/evaluate.py --checkpoint checkpoints/best_efficientnet_b3_*.pt --bias

# Save per-image predictions to CSV
python scripts/evaluate.py --checkpoint checkpoints/best_*.pt \
    --bias --save-csv results/predictions.csv

Example output:

==================================================
  Split  : test  (n=2041)
  MAE    : 4.12 years
  CS@5   : 0.713  (71.3% within 5 years)
  CS@10  : 0.941  (94.1% within 10 years)
==================================================

── MAE by Gender ────────────────────────────────
Female    4.05
Male      4.19

── MAE by Ethnicity ─────────────────────────────
Asian     3.91
Black     4.44
Indian    4.28
White     4.02

Single Image Inference

python scripts/inference.py \
    --checkpoint checkpoints/best_*.pt \
    --image img_test/30_1_2.jpg \
    --output img_test/output.jpg

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🖥️ Gradio Demo

# Local demo
python demo/app.py --checkpoint checkpoints/best_*.pt

# Share publicly via Gradio tunnel
python demo/app.py --checkpoint checkpoints/best_*.pt --share

The demo shows both the predicted age and the full probability distribution over all 101 age classes, giving you insight into model confidence.

🚀 A live demo on Hugging Face Spaces is coming soon!

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📤 Export Model

# TorchScript (fastest for deployment — uses torch.jit.trace)
python scripts/export_model.py --checkpoint checkpoints/best_*.pt

# ONNX (for ONNX Runtime, TensorRT, OpenVINO, etc.)
python scripts/export_model.py --checkpoint checkpoints/best_*.pt --format onnx

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📚 Literature Review

Click to expand — 7 key papers reviewed

Paper	Summary	Key Idea	Code
SwinFace (2023)	Multi-task Swin Transformer for face analysis	Multi-Level Channel Attention	GitHub
Unraveling Age Estimation Puzzle (2022)	Benchmark & analysis of what actually matters	Data quality > model choice	GitHub
MiVOLO (2023)	ViT-based age + gender; works on occluded faces	Uses body context, not just face	GitHub
CORAL (2020)	Ordinal regression for age estimation	Rank-consistent binary subtasks	GitHub
Deep Regression Forests (2018)	End-to-end DRF for heterogeneous age features	Joint data partition + abstraction	GitHub
Mean-Variance Loss ★ (2018)	Distribution learning for age estimation	Used in this repo	Paper
FaceXFormer (2024)	Unified transformer for all face analysis tasks	Token-based multi-task decoder	GitHub

Supported Datasets

Dataset	Images	Age Range	Notes
UTKFace (used here)	~23,000	0–116	In-the-wild, diverse
MORPH	55,134	16–77	Mugshot style
CACD	163,446	—	Celebrity, web-collected
FG-NET	1,002	0–69	Small, cross-age pairs
Adience	26,580	Age groups	8 coarse age groups

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🗺️ Roadmap

✅ Completed

• Professional package structure — src/, scripts/, demo/, tests/, configs/
• OmegaConf YAML config — all hyperparameters in one file, CLI overrides
• 5 backbone support — MobileNetV3-Large, EfficientNet-B0/B3, ResNet-50, ViT-S/14 DINOv2
• Mean-Variance Loss (CVPR 2018) — distribution-based age estimation
• Modern training pipeline — AMP, AdamW with differential LRs, OneCycleLR, gradient clipping
• Stratified data splits — stratified train/val/test CSV generation
• 37 unit tests — model, losses, transforms, dataset; all passing
• GitHub Actions CI — automated lint + test on every push
• Gradio demo — with age probability distribution visualisation
• TorchScript + ONNX export
• Bias evaluation — per age-group, gender, and ethnicity MAE breakdown
• Single-image + batch inference script
• pyproject.toml — proper packaging with optional extras
• Comprehensive .gitignore — no accidental weight/dataset commits

🔄 In Progress

• Train EfficientNet-B3 on UTKFace and populate the results table with real MAE numbers
• Hugging Face Spaces demo — live, always-on demo accessible from the README

📌 Planned

• GradCAM / Attention heatmaps — visualise which facial regions drive age predictions
• Real-time webcam inference — scripts/webcam.py with OpenCV face detection
• Cross-dataset evaluation — train on UTKFace, test on MORPH / FG-NET
• EDA Jupyter Notebook — notebooks/01_EDA.ipynb with interactive plots
• Pre-commit hooks — ruff + formatting checks enforced on commit
• ONNX Runtime inference script — for CPU-only deployment
• Benchmark on MORPH dataset — broader reproducibility

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📝 Citation

If you use this repository in your research, please cite:

@misc{mousavi2024facial,
  title     = {Facial Age Estimation with PyTorch},
  author    = {Mousavi, Ebrahim},
  year      = {2024},
  publisher = {GitHub},
  url       = {https://github.com/Ebimsv/Facial_Age_estimation_PyTorch}
}

The Mean-Variance Loss implemented in this repo:

@inproceedings{pan2018mean,
  title     = {Mean-Variance Loss for Deep Age Estimation from a Face},
  author    = {Pan, Hongyu and Han, Hu and Shan, Shiguang and Chen, Xilin},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages     = {5285--5294},
  year      = {2018}
}

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🤝 Contributing

Contributions are welcome! Whether it's a new backbone, a better loss function, a bug fix, or improved documentation.

# Fork, clone, create a branch
git checkout -b feat/your-feature

# Install dev dependencies
pip install -r requirements-dev.txt
pre-commit install

# Make changes, then verify
ruff check src/ scripts/ tests/
python -m pytest tests/ -v

# Open a Pull Request 🎉

See CONTRIBUTING.md for detailed guidelines, including how to add a new backbone.

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📄 License

This project is licensed under the MIT License — see LICENSE for details.

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If this project helped you, please consider giving it a ⭐ — it means a lot!

Made with ❤️ by Ebi Mousavi