dtourolle
4925443e56
Splits the rep4 write-up's key findings into their own linkable pages: - best-model.md: calibration curves first (discriminative power, independent of any threshold), then F1 on the benchmark — LVFace-B Glint360K wins both. - gallery-scope.md: whole vs. cast-restricted gallery, isolated from model and expansion choice — restriction wins on every axis, but isn't a shipped runtime feature yet. - pose-expansion.md: the training-set expand_gallery effect, and the held-out replication attempt that found it doesn't reproduce (5 films, 2 models, after catching and fixing a replay-timeout truncation bug and a bbox first-match-instead-of-best-match bug in the comparison harness itself). An honest null result, with the methodology errors documented since they're exactly the kind that manufacture a false "it works!" finding. - lvface-deep-dive.md: the winning model's held-out generalization gap, its two failure modes (frozen-bbox ghost tracks), and a verified case (cross- checked against Jellyfin's independent cast metadata) where LVFace correctly identified an actor that X-Ray's ground truth failed to credit. Adds a "report-highlights" artifact-registry package (scripts/artifacts/ push_artifacts.sh, pull_artifacts.sh) for hand-picked illustrative frames that aren't reproducible via the automated best/worst montage selection, and wires pulling it into scripts/docs/build_site.sh.
62 lines
2.9 KiB
Markdown
62 lines
2.9 KiB
Markdown
# Which embedding model is best?
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Four candidates went into the bake-off: three ArcFace variants (w600k-R50,
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R18, w600k-MBF) and LVFace-B (Glint360K), a Vision-Transformer embedder that's
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a drop-in replacement for ArcFace's `[N,3,112,112]` input / 512-d output. The
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open question: is LVFace (455MB) actually better, or just the biggest?
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## First signal: calibration curves
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Each gallery carries a fitted Platt sigmoid `P(match | cosine similarity) =
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σ(a·sim + b)`, embedded directly in the gallery's HDF5 file
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(`src/gallery/gallery_calibration.hpp`). This is a property of the embedding
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space alone — computed from intra/inter-actor reference-image pairs, no
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tracking or scene logic involved — so it's a clean first read on discriminative
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power before running a single benchmark.
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| model | `a` (steepness) | boundary at P=0.5 |
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|---|---|---|
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| **LVFace-B Glint360K** | **17.7** | **sim 0.228** |
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| ArcFace w600k-MBF | 16.2 | sim 0.267 |
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| ArcFace w600k-R50 | 15.4 | sim 0.301 |
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| ArcFace R18 | 15.3 | sim 0.309 |
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LVFace has both the steepest transition and the lowest decision boundary — it
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separates same-actor from different-actor reference pairs more confidently, at
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a *lower* similarity threshold, than any ArcFace variant. That's a genuine
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head start before the tracking/scoring pipeline is even involved.
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## Second signal: F1 on the actual benchmark
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Best full-gallery (no cast-restriction) result per model, from the 16-combo
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rep4 matrix (`rep4-optimizer-results.md`):
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| model | F1 | P | R | misID |
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|---|---|---|---|---|
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| **LVFace-B Glint360K** | **75.3%** | 89.7% | **65.4%** | 232 |
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| ArcFace w600k-MBF | 74.2% | 87.4% | 64.4% | 57 |
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| ArcFace R18 | 69.1% | 87.6% | 57.7% | 242 |
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| ArcFace w600k-R50 | 68.5% | 94.0% | 54.1% | 150 |
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LVFace wins outright, with the highest recall of any full-mode combo. This
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reverses an earlier conclusion from a prior (superseded) benchmarking pass
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using a scene-union metric, which found the three models statistically
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indistinguishable (~85% each) and concluded LVFace wasn't worth its size — that
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metric hid out-of-cast false positives behind a gallery∩cast recall mask (see
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`optimizer-experiments.md`); the per-second metric used here does not.
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Held-out validation (5 films never seen by the optimizer) confirms LVFace's
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lead holds up out of sample — see the deep-dive page for the full breakdown,
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including where it fails.
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## Caveat: model choice is an operational change
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Switching the default embedder isn't just flipping a config value — the
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gallery itself is model-specific (embeddings from different models aren't
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comparable), so any existing gallery built against ArcFace w600k-R50 needs to
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be rebuilt from source images against LVFace before the new default takes
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effect. `scripts/optimizer/reembed_gallery.py` does this from a reference
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gallery's cached source images without re-downloading anything.
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