technical_report.md

FastFace Phase-1 Technical Report

Status: phase-1 frozen candidate set.

Date: 2026-07-31.

Scope

FastFace trains face-attribute models for:

• Gender classification.
• Numeric age prediction.

Race prediction is deliberately excluded. Race labels in public datasets are not used as a product output because the category is culturally unstable, difficult for humans to assign consistently, and creates avoidable product and compliance risk.

The phase-1 production target is CPU inference throughput, not maximum GPU accuracy. Heavy models are used as teachers and evaluators; deployment candidates are MobileNetV3 students exported to ONNX.

Hardware And Environment

Training was run on <remote-gpu-host>:

• Hostname: <gpu-hostname>.
• GPUs: 8 x NVIDIA GPU accelerator.
• Conda root: <conda-root>.
• Conda env: faceattr.
• Project path: <repo-root>.
• Data path: ${FASTFACE_WORK_ROOT}.

The environment, package versions, and setup commands are recorded in GPU_ENVIRONMENT.md.

Data

Phase-1 manifests on the GPU host:

Dataset	Rows	Role	Label Notes
FairFace	97,698	gender training, age-range auxiliary supervision, fairness/public validation	gender exact; age range weak supervision
UTKFace	23,705	exact-age and gender training/validation	gender exact; age exact from filename
IMDB-clean	285,946	exact-age/gender data expansion and teacher/student training	gender exact; age exact; MiVOLO face boxes
Lagenda-HF	12,409	staged but not promoted in phase 1	exact labels, requires bbox_face crop

See DATA_PROVENANCE.md for source URLs, staging paths, download caveats, and license/access cautions.

Manifest Contract

Training consumes JSONL manifests. Required fields include:

• sample_id
• dataset
• split
• image_path
• gender, with 0=female, 1=male
• age, age_min, age_max
• age_label_type, either exact or range
• age_loss_weight

Optional fields include:

• bbox_face
• bbox_person
• upstream label fields such as gender_original

When bbox_face exists, training/evaluation can crop the face box with a margin before resizing. Phase-1 IMDB and Lagenda experiments use this path.

Model Architecture

All FastFace models use two heads:

• Gender head: binary logits.
• Age head: a 0..100 age distribution with numeric expectation output.

The age-distribution head keeps the product output simple while making training more stable than direct scalar regression.

Implemented backbones:

• MobileNetV3 Small/Large.
• EfficientNet-B0.
• EfficientNetV2-S.
• ResNet18.
• ConvNeXt-Tiny.
• Swin-T.

The deployment candidates are MobileNetV3 students. EfficientNetV2-S is the strongest phase-1 teacher/challenger.

Training Objective

Primary selection target: gender balanced accuracy.

Secondary target: numeric age quality.

Representative gender-priority loss settings:

loss:
  gender_weight: 4.0
  age_weight: 0.5

For teacher/student runs, distillation was used only where it improved the student tradeoff. The final IMDB source-balanced student distillation uses:

distillation:
  enabled: true
  temperature: 2.0
  gender_weight: 0.2
  age_weight: 0.0

This keeps teacher supervision low-weight and gender-focused.

Source Balancing

Natural IMDB mixing caused the validation aggregate to be dominated by IMDB-clean. Source-balanced follow-up runs cap IMDB-clean rows during training and checkpoint-selection validation:

data:
  train_sample_limits:
    imdb-clean: 90000
  val_sample_limits:
    imdb-clean: 10954

Final evaluation still reports full source-sliced metrics so aggregate gains are not mistaken for robust public-domain gains.

Main Runs

Run	Backbone	Purpose	Mixed Gender Balanced Acc	FairFace	IMDB-clean	UTKFace	Tuned FP32 Batch-128
efficientnet_v2_s_128_imdb_source_balanced_gender_priority_real_fairface_utkface	EfficientNetV2-S	IMDB-inclusive teacher/challenger	0.98605	0.94386	0.99138	0.95424	default FP32 1,044.1 img/s
mobilenetv3_large128_imdb_source_balanced_distill_gender_priority_efficientnet_v2_s_imdb_fairface_utkface	MobileNetV3-Large	accuracy-oriented CPU candidate	0.97929	0.92877	0.98548	0.95017	4,477.7 img/s
mobilenetv3_small112_imdb_source_balanced_distill_gender_priority_efficientnet_v2_s_imdb_fairface_utkface	MobileNetV3-Small	throughput-oriented CPU candidate	0.96800	0.90562	0.97542	0.94101	10,837.8 img/s

The full historical run matrix, including negative challengers and CPU benchmarks, is in model-runs.md.

Public Baseline Comparison

A fixed comparison was run against public FairFace-ONNX and MiVOLO on:

• FairFace validation.
• UTKFace validation.
• A seed-stable IMDB-clean validation sample capped to FairFace size.

Selected rows: 24,333.

Model	Gender Balanced Acc	Gender Acc
teacher_v2s_imdb	0.96638	0.96638
our_large128_imdb_distill	0.95618	0.95640
public_fairface_onnx	0.94658	0.94723
mivolo_imdb_face	0.94461	0.94530
our_small112_imdb_distill	0.94059	0.94103

For rows where our_large128_imdb_distill and public FairFace-ONNX disagree:

• Count: 1,301.
• FastFace Large correct / public wrong by public labels: 762.
• Public correct / FastFace Large wrong by public labels: 539.

This is not treated as final truth because public labels include ambiguous or noisy samples. The current manual review workbook is built only from this public-vs-FastFace disagreement set.

Frozen Phase-1 Decision

Freeze these variants:

• fastface-large-128: default accuracy-oriented CPU candidate.
• fastface-small-112: throughput-oriented CPU candidate.
• fastface-teacher-v2s-128: teacher/evaluation artifact, not a CPU default.

Do not continue broad architecture search for phase 1. Next work should use the manual disagreement review to decide whether data relabeling, filtering, or targeted fine-tuning is justified.

Known Limitations

• Public datasets contain noisy labels, especially in hard gender cases.
• FairFace age labels are ranges; FairFace age MAE is directional only.
• IMDB-clean is celebrity-heavy and can inflate mixed aggregate metrics.
• Lagenda raw images are full-scene images and require face-box cropping.
• INT8 did not beat FP32 for sustained MobileNetV3 high-batch throughput in the measured ONNX Runtime environment.
• MiVOLO is now included through the face-only IMDB-clean checkpoint. The GPU host still cannot reach Google Drive directly, so the checkpoint was downloaded locally and staged under third_party/mivolo.

Reproducibility Entry Points

bash scripts/build-training-manifests.sh
bash scripts/train-and-finalize.sh
bash scripts/compare-gender-models.sh
bash scripts/build-manual-gender-review.sh

Every completed run should keep:

• config.resolved.yaml
• metrics.jsonl
• best.pt
• evaluation_val.json
• model_fp32.onnx
• model_int8_static.onnx
• CPU benchmark JSON files
• model_card.md