# Utkface Age Estimation Eval

> This evaluation protocol assesses the accuracy of a lightweight neural network for predicting a person's age from a single facial image. It focuses on regression-based age estimation to determine how well compact models generalize to held-out test data while maintaining deployment efficiency. Use when the user wants to benchmark on UTKFace, or asks about evaluating this task. Reports MAE.

- Skill: `qhjqhj00/utkface-age-estimation-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/utkface-age-estimation-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/utkface-age-estimation-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/utkface-age-estimation-eval

---


# utkface-age-estimation-eval

> MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment — Arun Kumar et al. (2026) (arXiv:2604.17007, 2026)

## What this evaluates

This evaluation protocol assesses the accuracy of a lightweight neural network for predicting a person's age from a single facial image. It focuses on regression-based age estimation to determine how well compact models generalize to held-out test data while maintaining deployment efficiency.

## Datasets

- **UTKFace** — total ?; splits: train (-1), val (-1), test (-1); repo https://github.com/ABrain-One/NN-Dataset

## Metrics

- `MAE` **(primary)** — range: other
  - Mean Absolute Error: the average of the absolute differences between predicted ages and ground-truth ages across all test samples.

## Input / output format

**Input**: Single RGB facial image (preprocessed with constrained augmentations like Resize, ColorJit, Flip, Blur).

**Output**: A single continuous floating-point value representing the predicted age in years.

## Scoring recipe

```python
def compute_mae(predictions, ground_truth):
    absolute_errors = [abs(p - g) for p, g in zip(predictions, ground_truth)]
    mae = sum(absolute_errors) / len(absolute_errors)
    return mae
```

## Common pitfalls

- Tuning hyperparameters on the same data used for final reporting, which inflates performance estimates.
- Applying aggressive or inappropriate data augmentations that distort age-relevant facial features.
- Evaluating on validation checkpoints instead of a strictly held-out test partition.

## Evidence (verbatim from paper)

> The primary evaluation metric is mean absolute error (MAE), reported in years. MAE is widely used in facial age estimation because it measures the average absolute deviation between predicted and ground-truth ages in a directly interpretable form. It is also well aligned with the regression-based formulation of MobileAgeNet, which predicts a single continuous age value.

## Citation

```bibtex
@misc{kumar2026mobileagenet,
  title={MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment},
  author={Arun Kumar et al. (2026)},
  year={2026},
  note={arXiv:2604.17007}
}
```

- arXiv: 2604.17007