dtourolle
b1efefac6f
- experiment_charts.py generates 4 figures from experiments/ artifacts: held-out per-film F1, 16-combo ranking, DE search landscape, and the Downton detector-vs-tracker ghost timeline (replaces the blank title-card screenshot) - new frames: 19-correct wedding shot (success case), Many Saints ghost-vs-unknown frame (three error classes in one image) - rename rep4-optimizer-results.md -> model-bakeoff.md; rep4 kept only as the on-disk artifact prefix, explained once - repo file references are now links via https://REPOLINK/<path> placeholders; build_site.sh pins them to the HEAD commit's raw URLs and fails the build if a linked path doesn't exist at HEAD - drop references to removed scripts (scene_score.py, score_config.py) and to session-memory names; mark artifact-registry paths with their pull commands - commit readme_example.jpg + pipeline_topology.svg so README renders on the plain Gitea repo view - deploy_pages.sh: push built site/ to the gitea-pages branch
71 lines
3.4 KiB
Markdown
71 lines
3.4 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`](https://REPOLINK/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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| **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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bake-off matrix ([full experiment log](model-bakeoff.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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The full 16-combo picture makes the model ordering visible at a glance — LVFace
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(yellow) tops both the restricted and full columns, and R18 (green) props up
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the bottom of the full-gallery ranking:
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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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[the prior optimizer round](optimizer-experiments.md)); the per-second metric
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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
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[LVFace deep dive](lvface-deep-dive.md) for the full breakdown, including
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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`](https://REPOLINK/scripts/optimizer/reembed_gallery.py)
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does this from a reference gallery's cached source images without
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re-downloading anything.
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