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The July report (4-model ArcFace/LVFace bake-off, pre-opencv5 framework, 3-film training + held-out validation) is superseded by the opencv5 build: single-model LVFace-B, a 6-knob DE sweep over all 9 films, flood-fill presence, and the registry/decode fixes. Rather than overwrite it, archive it date-suffixed and start the current report fresh. - Rename the six July result pages to *-2026-07.md, rewrite their intra-archive cross-links, and add an "Archived (July 2026)" banner to each. - mkdocs nav: current report at top, the July set under an Archive section. - New docs/methodology.md for the opencv5 run: corrects the withdrawn anneal_sec/extinction_sec presence bridging (windows are now [first_seen, last_seen], AR-012/013), documents the two presence modes (track_extent / flood), and records that every eval scores all 9 films. The current experiment log (model-bakeoff.md) and Home rewrite land once the DE sweep converges and the final optimum is known.
72 lines
3.3 KiB
Markdown
72 lines
3.3 KiB
Markdown
> **Archived (July 2026).** This report covers the pre-opencv5 framework and the 4-model ArcFace/LVFace bake-off. It is superseded by the current [experiment log](model-bakeoff.md) for the opencv5 build. Kept for provenance; the numbers here are historical.
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# 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-2026-07.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-2026-07.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-2026-07.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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