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.

	True: 0-14	True: 15-22	True: 22+
Pred: 0-14	0.91	0.07	0.01
Pred: 15-22	0.08	0.74	0.16
Pred: 22+	0.01	0.19	0.82

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).