Accuracy benchmarks#
Tracks per-release accuracy of py-feat detectors against held-out labeled datasets. Each entry is a single run produced by python scripts/bench_regression.py --markdown. Throughput benchmarks live in throughput.md.
Latest#
Methodology#
DISFA P3 fold, ArcFace-aligned crops, AU intensity binarized at >=2 for F1; ICC(3,1) on continuous intensity vs. py-feat probability.
AffectNet validation set, classes 0..6 mapped to the 7 py-feat emotion columns; top-1 emotion accuracy and macro F1.
CALFW / CPLFW 6000 pairs, LFW 10-fold CV protocol, InsightFace 5-landmark template alignment before ArcFace embedding.
TinyFace closed-set + open-set rank-K identification with the Gallery_Distractor set (153k images) when not disabled.
History#
date |
run |
|---|---|
2026-05-15-67cd7d9 |