dtourolle
0bd2747069
Replaces narrative claims with verified numbers across all report pages: - Cross-model held-out validation (LVFace/mbf/r18, all 5 held-out films): LVFace wins every film outright, not just "consistent with" the training-set pick. r50 dropped from the detailed comparison (gallery has ~30% fewer reference images per actor than the other three models on identical source photos). - Per-film training breakdown: LVFace does not win every training film (mbf beats it on Lord of War); the 75.3% macro figure hides a 10.7pp spread. - Gallery coverage computed per film (20.3%-78.6%) instead of one flat 67%-missing average. - Found and fixed a real scoring bug in optimize.py: a candidate whose hardest film's replay timed out was averaged over survivors instead of penalized, silently rewarding partial coverage. Affected 3 of 16 training combos; corrected throughout, and optimize.py now scores an incomplete evaluation f1=0.0 instead of averaging over whichever films happened to finish. - Every FPI frame in the deep dive now comes from the proper montage renderer (Onscreen/Offscreen panel, ghosts never drawn as boxes), never the bare-box debug overlay used earlier. - Every distinct out-of-cast name across all 9 films gets its own frame at its first appearance (9 names, 4 films), not a single-example spot check: 2 ground-truth gaps, 1 photograph misread as a person, 6 genuine lookalike confusions. - New methodology.md: the scene-level-vs-per-second scoring mismatch that the rest of the report assumes, written out once. - Cut the deadlock/gdb debugging narrative from the experiment log; kept the one fact that matters (KPN's node/network split lets the expensive GPU stage run once and the cheap stage replay against cached embeddings). - Plain declarative style throughout, no em dashes, no blog voice.
70 lines
3.1 KiB
Markdown
70 lines
3.1 KiB
Markdown
# Whole gallery vs. cast-restricted gallery
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Two ways to run the matcher. Full mode scores every detected face against
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the entire 2418-actor gallery. Restricted mode pre-filters each film's
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gallery down to just its Jellyfin-credited cast (typically around 15
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top-billed actors) before the matcher runs.
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## Result
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Averaged across the 3 compared models (r50 excluded, see
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[the full experiment log](model-bakeoff.md)) and both expansion settings, on
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the 4 training films:
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| scope | F1 | P | R | total misID |
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|---|---|---|---|---|
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| full | 71.1% | 89.6% | 59.6% | 1121 |
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| restricted | 75.9% | 90.4% | 65.6% | 299 |
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Restriction improves every metric at once, not a precision/recall trade:
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+4.8pp F1, +6.0pp recall, roughly a quarter the total misIDs. Fewer
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candidates in the matcher's search space means fewer opportunities for a
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lookalike false match, and the recall gain shows this does not cost real
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detections.
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Every model's best-scoring combo in the training matrix uses the
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restricted gallery:
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See [the full experiment log](model-bakeoff.md) for the complete table. One
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combo reaches zero true out-of-cast misidentifications,
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`arcface_w600k_mbf_restricted_exp` (F1 76.2%), and it is a restricted one,
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consistent with restriction, not expansion, being what suppresses cross-film
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confusions.
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The restriction effect (+4.8pp averaged across models) is larger than the
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model-choice effect: LVFace beats r18 by 6.2pp in full mode but beats mbf by
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3.3pp. Restriction is the single strongest lever in the matrix.
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## Why this is not the shipped default
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Cast restriction is implemented today only as an offline optimizer
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technique
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([`scripts/optimizer/cast_restrict.py`](https://REPOLINK/scripts/optimizer/cast_restrict.py)):
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it pre-builds a filtered gallery file per film using Jellyfin's cast list
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before the benchmark calls the matcher. There is no runtime "restrict to
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this title's credited cast" switch in the shipped application;
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`scene_analyze` always matches against whatever single gallery file it is
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given.
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Building this as a real feature requires:
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- A live Jellyfin cast lookup at analysis time. The title is already known,
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and [`scripts/run_from_jellyfin.py`](https://REPOLINK/scripts/run_from_jellyfin.py)
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already performs this lookup for its own `filter_gallery`-based
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restriction path; it is not wired into `scene_analyze` as a first-class
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option.
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- A decision on the fallback case: what happens to a real, uncredited
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cameo (see the Germar Terrell Gardner and Talia Balsam cases in the
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[LVFace deep dive](lvface-deep-dive.md#where-lvface-beat-x-ray)) if the
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restricted gallery never includes them at all.
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- Regenerating the restricted-gallery cache whenever a title's Jellyfin
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cast list changes.
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The shipped [`src/config.hpp`](https://REPOLINK/src/config.hpp) defaults use
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the full-mode winner (`LVFace-B_Glint360K_full_exp`, F1 75.3% training,
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67.4% held-out macro) rather than the higher-scoring `restricted_exp`
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(78.3%), because 78.3% describes a capability the application does not
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have yet.
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