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.
98 lines
5.1 KiB
Markdown
98 lines
5.1 KiB
Markdown
# Pose expansion: does "learning" new poses mid-film help?
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`expand_gallery` (`src/gallery/track_gallery.hpp`) promotes a confidently-identified
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track's novel-pose reference views into a per-film, in-memory gallery annex — the
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idea being that once the pipeline is sure who someone is, a pose it hasn't seen
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before (turned head, different lighting) becomes a free extra reference for
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recognising that actor again later in the same film, without touching the baked
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gallery.
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## The training-set signal
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Averaged across all 4 models, on the 4 films used for optimization:
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| scope | expansion | F1 | R | misID |
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|---|---|---|---|---|
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| full | off | 71.2% | 58.3% | 209 |
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| full | **on** | 71.2% | 59.7% | **864** |
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| restricted | off | 73.6% | 61.3% | 194 |
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| restricted | **on** | **75.4%** | **64.5%** | 135 |
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In `restricted` mode (matcher's candidate set capped to the film's own credited
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cast) expansion looked like a clean win: +1.8pp F1, +3.2pp recall, misID actually
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lower. In `full` mode it looked flat-to-costly: ~0 F1 change, recall +1.4pp, but
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misID roughly quadrupled (209 → 864) — see `rep4-optimizer-results.md` for the
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per-model breakdown. That's the number that motivated this page: **does turning
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expansion on actually change what gets recognised, frame by frame, or is the
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aggregate F1 shift something else?**
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## Held-out test: does it reproduce?
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Same model + same tuned config, `expand_gallery` toggled on vs. off, nothing else
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changed — full gallery mode, per-second scoring against X-Ray. This isolates
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expansion from every other variable (config, model, threshold) that differs
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between the training-set `exp`/`noexp` rows above.
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**LVFace-B Glint360K, all 5 held-out films** (films never seen by the optimizer):
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| film | F1 (exp) | F1 (noexp) | TPI Δ | FN Δ |
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|---|---|---|---|---|
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| Benny & Joon | 83.0% | 83.0% | -2 | +2 |
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| Downton Abbey: A New Era | 56.1% | 56.2% | -7 | +7 |
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| Lovelace | 77.5% | 77.4% | +33 | -33 |
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| The Many Saints of Newark | 46.3% | 46.3% | +2 | -2 |
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| Valerian and the City of a Thousand Planets | 74.1% | 74.1% | +2 | -2 |
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**ArcFace R18** (Benny & Joon, r18's own tuned config): F1 77.1% for both, TPI/FN
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identical, FPI differs by 2 (noise).
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**Every film, both models tested: F1 within 0.1–0.2pp, TPI/FN swings in the tens
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out of tens of thousands.** That's noise, not a signal — expansion made no
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measurable difference to per-second onscreen identification anywhere it was
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tested on unseen data.
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## Two bugs this required catching (this section's own methodology)
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Getting to the clean table above took two wrong turns, both worth recording
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since they're exactly the kind of error that produces a false positive "look,
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expansion helped!" finding:
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1. **Timeout truncation.** The first Downton Abbey `exp` replay was cut off by a
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60s subprocess timeout at ~76% through the film (5589 of 7368 expected
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seconds) — a genuinely large, silent data loss that showed up as a large,
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convincing-looking TPI gap (47938 vs 52032) purely because one run had a
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quarter of the film missing. Caught by comparing `n_seconds` between runs
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before trusting any score delta; fixed by re-running with a longer timeout.
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2. **Bbox-matching bug.** An early per-second raw-annotation diff matched each
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`exp` detection to the *first* `noexp` detection with IoU > 0.5, not the
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*best*-overlapping one. With 3 faces close together in frame, this produced
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spurious "disagreements" (e.g. "exp says Aidan Quinn, noexp says Johnny
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Depp" at the same seconds) that vanished entirely once the match picked the
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true best-IoU candidate — both configs had actually output the exact same
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three names at the exact same three boxes.
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Both bugs independently pointed toward "expansion is doing something," and both
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were artifacts of the comparison harness, not the pipeline. Worth remembering
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when a before/after diff looks dramatic: check that the two runs actually cover
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the same seconds, and match entities by best overlap, not first-found.
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## What this means
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The training-set aggregate effect (particularly the ~4x misID increase in full
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mode) doesn't reproduce on held-out data — at minimum it's far smaller than the
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training-set numbers suggested, and plausibly it's sampling variation from only
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4 training films rather than a real, generalizable mechanism. This doesn't mean
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`expand_gallery` never does anything (the mechanism is real — see
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`track_gallery.hpp`'s promotion logging: tracks *do* get confirmed and views *do*
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get promoted into the annex on every film tested), only that **whatever effect
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it has on final per-second identification was too small to detect against 5
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held-out films** with this scoring method. A cleaner test would need either many
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more held-out films or a metric that can see the annex's direct contribution
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(e.g. tagging which reference embedding won each match), neither of which this
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pass had budget for.
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**Practical takeaway**: don't treat the training-set `exp` vs `noexp` numbers in
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`rep4-optimizer-results.md` as proof that expansion changes real-world behavior
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in either direction — on the evidence gathered so far, it doesn't move the
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needle enough to see.
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