轻量级人脸年龄估计模型，手机端实时预测误差仅4.65年。
MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment
- 基于MobileNetV3-Large构建轻量回归头，支持移动端高效推理。
- 在UTKFace测试集上MAE达4.65年，参数仅323万，延迟14.4毫秒。
- 全流程可复现，从训练到TensorFlow Lite部署无性能损失。
面向移动端的人脸年龄估计需兼顾精度、低延迟与小体积。本文提出MobileAgeNet，一种轻量级年龄回归框架，在UTKFace保留测试集上实现4.65年平均绝对误差（MAE），平均延迟为14.4毫秒（使用AI Benchmark应用测量）。模型基于预训练的MobileNetV3-Large主干网络，搭配紧凑回归头，支持移动设备实时预测。训练与评估流程集成于NN LEMUR数据集框架，支持可复现实验、结构化超参数优化与一致评估。采用有界年龄回归与两阶段微调策略，提升训练稳定性和泛化能力。实验表明，MobileAgeNet在仅323万参数下达到竞争力精度，且从PyTorch训练经ONNX导出至TensorFlow Lite转换的部署流程，在实际设备条件下无明显性能下降。整体提供了一套面向移动端的可直接部署的面部年龄估计基准方案。
原文摘要 · Abstract (English)
Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight age-regression framework that achieves an MAE of 4.65 years on the UTKFace held-out test set while maintaining efficient on-device inference with an average latency of 14.4 ms measured using the AI Benchmark application. The model is built on a pretrained MobileNetV3-Large backbone combined with a compact regression head, enabling real-time prediction on mobile devices. The training and evaluation pipeline is integrated into the NN LEMUR Dataset framework, supporting reproducible experimentation, structured hyperparameter optimization, and consistent evaluation. We employ bounded age regression together with a two-stage fine-tuning strategy to improve training stability and generalization. Experimental results show that MobileAgeNet achieves competitive accuracy with 3.23M parameters, and that the deployment pipeline from PyTorch training through ONNX export to TensorFlow Lite conversion - preserves predictive behavior without measurable degradation under practical on-device conditions. Overall, this work provides a practical, deployment-ready baseline for mobile-oriented facial age estimation.
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