AdamCodd / yolo11n-face-age

YOLO11 Nano - Age Classification

This is a lightweight face detection and age classification model based on the YOLO11 Nano architecture. It was fine-tuned for 300 epochs on a heavily curated dataset of approximately 75,000 face images to classify individuals into three broad age brackets: 0-14 , 15-22 , and 22+ .

The training data was sourced and filtered from IMDB-Wiki, Adience, UTKFace, and FairFace. Consequently, this model is released under a CC-BY-NC-4.0 license and is intended strictly for non-commercial research .

Performance

The model achieves high precision despite its small size, making it suitable for edge devices or local privacy-preserving inference.

• mAP@50: ~89.5%
• mAP@50-95: ~86.5%
• Recall: ~87%
• Precision: ~83%

Confusion Matrix

The table below shows the normalized prediction results. The columns represent the actual age (True Labels), and the rows represent the model's predictions.

The model is highly reliable at identifying children , with 91% accuracy and minimal confusion with adults (only 1% of children were predicted as 22+).

Limitations & Legal

Since this model was trained exclusively on real photos, performance may degrade on AI-generated faces, cartoons, or 3D renders. However, personal tests suggest it maintains reasonable detection capabilities on realistic AI generated content.

• Accuracy: This model is not 100% accurate. It uses a "buffer" class (15-22) to handle the ambiguity of young adulthood, but obviously it's not perfect.
• Critical Use: It should not be used for critical age verification or legal identity checks.
• Biometric Privacy: This model processes biometric features (faces). It is provided for research and local-inference contexts only and should not be deployed for mass surveillance or without considering local regulations (such as the GDPR guidelines in Europe or CCPA in the US).