FastFace

FastFace is a CPU-efficient face age and gender model family. Phase 1 freezes three variants:

Variant	Purpose	Input	Primary artifact
fastface-large-128	Recommended accuracy/throughput student	aligned RGB face crop, 128x128	models/fastface-large-128/model_fp32.onnx
fastface-small-112	Highest-throughput student	aligned RGB face crop, 112x112	models/fastface-small-112/model_fp32.onnx
fastface-teacher-v2s-128	Audit/teacher model, not CPU default	aligned RGB face crop, 128x128	models/fastface-teacher-v2s-128/model_fp32.onnx

The released task is gender classification plus numeric age estimation. Race prediction is intentionally out of scope.

Training Data

Training used aligned face crops and manifests built from public/research datasets available in the local training workspace:

• FairFace train/validation labels.
• UTKFace aligned face images.
• IMDB-clean derived from the IMDB-WIKI family after quality filtering.
• Lagenda-hosted face-age data was explored but is not part of the frozen phase-1 student release.

See data_provenance.md and technical_report.md in this repository for the exact source policy, manifest contract, and limitations.

Metrics

Validation gender balanced accuracy on the mixed public validation set:

Model	Mixed GBA	FairFace GBA	IMDB-clean GBA	UTKFace GBA
fastface-teacher-v2s-128	0.98605	0.94386	0.99138	0.95424
fastface-large-128	0.97929	0.92877	0.98548	0.95017
fastface-small-112	0.96800	0.90562	0.97542	0.94101

In a 24,333-sample comparison set, gender balanced accuracy was:

• fastface-teacher-v2s-128: 0.96638
• fastface-large-128: 0.95618
• public fairface-onnx: 0.94658
• fastface-small-112: 0.94059

Public FairFace-ONNX vs fastface-large-128 disagreed on 1,301 samples. Against the available public labels, fastface-large-128 was correct on 762 of those and public FairFace-ONNX was correct on 539.

Intended Use

• High-throughput CPU inference on already-detected/aligned face crops.
• Gender and age signals for product analytics or moderation-assist workflows where uncertainty and bias are handled upstream/downstream.
• Teacher-assisted auditing and future distillation.

Limitations

• The model does not detect faces; it expects aligned face crops.
• Age labels are noisy across public face-age datasets, so age should be treated as an estimate.
• Gender labels follow dataset annotations and can encode social and labeling bias.
• Race/ethnicity classification is not provided.
• Metrics reflect the assembled validation manifests, not universal real-world performance.