Which embedding model is best?

Four candidates went into the bake-off: three ArcFace variants (w600k-R50, R18, w600k-MBF) and LVFace-B (Glint360K), a Vision-Transformer embedder that's a drop-in replacement for ArcFace's [N,3,112,112] input / 512-d output. The open question: is LVFace (455MB) actually better, or just the biggest?

First signal: calibration curves

Each gallery carries a fitted Platt sigmoid P(match | cosine similarity) = σ(a·sim + b), embedded directly in the gallery's HDF5 file (src/gallery/gallery_calibration.hpp). This is a property of the embedding space alone — computed from intra/inter-actor reference-image pairs, no tracking or scene logic involved — so it's a clean first read on discriminative power before running a single benchmark.

model	a (steepness)	boundary at P=0.5
LVFace-B Glint360K	17.7	sim 0.228
ArcFace w600k-MBF	16.2	sim 0.267
ArcFace w600k-R50	15.4	sim 0.301
ArcFace R18	15.3	sim 0.309

LVFace has both the steepest transition and the lowest decision boundary — it separates same-actor from different-actor reference pairs more confidently, at a lower similarity threshold, than any ArcFace variant. That's a genuine head start before the tracking/scoring pipeline is even involved.

Second signal: F1 on the actual benchmark

Best full-gallery (no cast-restriction) result per model, from the 16-combo rep4 matrix (rep4-optimizer-results.md):

model	F1	P	R	misID
LVFace-B Glint360K	75.3%	89.7%	65.4%	232
ArcFace w600k-MBF	74.2%	87.4%	64.4%	57
ArcFace R18	69.1%	87.6%	57.7%	242
ArcFace w600k-R50	68.5%	94.0%	54.1%	150

LVFace wins outright, with the highest recall of any full-mode combo. This reverses an earlier conclusion from a prior (superseded) benchmarking pass using a scene-union metric, which found the three models statistically indistinguishable (~85% each) and concluded LVFace wasn't worth its size — that metric hid out-of-cast false positives behind a gallery∩cast recall mask (see optimizer-experiments.md); the per-second metric used here does not.

Held-out validation (5 films never seen by the optimizer) confirms LVFace's lead holds up out of sample — see the deep-dive page for the full breakdown, including where it fails.

Caveat: model choice is an operational change

Switching the default embedder isn't just flipping a config value — the gallery itself is model-specific (embeddings from different models aren't comparable), so any existing gallery built against ArcFace w600k-R50 needs to be rebuilt from source images against LVFace before the new default takes effect. scripts/optimizer/reembed_gallery.py does this from a reference gallery's cached source images without re-downloading anything.