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<h1 id="deep-dive-lvface-b-glint360k">Deep dive: LVFace-B Glint360K<a class="headerlink" href="#deep-dive-lvface-b-glint360k" title="Permanent link">&para;</a></h1>
<p>LVFace won the model bake-off (see <code>best-model.md</code>) and is the shipped default
embedder. This page is the honest accounting of how it actually performs —
including where it's wrong, and one case where the ground truth itself is
wrong and LVFace is right.</p>
<h2 id="training-vs-held-out-the-generalization-gap">Training vs. held-out: the generalization gap<a class="headerlink" href="#training-vs-held-out-the-generalization-gap" title="Permanent link">&para;</a></h2>
<p>The shipped config (<code>prob_threshold=0.754, anneal_sec=35.54,
extinction_sec=57.43, expand_gallery=true</code>) was tuned against 4 films. Scored
against the 5 films the optimizer never saw:</p>
<table>
<thead>
<tr>
<th>film</th>
<th>F1</th>
<th>P</th>
<th>R</th>
<th>TPI</th>
<th>FPI</th>
<th>misid</th>
<th>FN</th>
</tr>
</thead>
<tbody>
<tr>
<td>Benny &amp; Joon</td>
<td>83.0%</td>
<td>89.1%</td>
<td>77.7%</td>
<td>15125</td>
<td>1846</td>
<td>0</td>
<td>4337</td>
</tr>
<tr>
<td>Lovelace</td>
<td>77.5%</td>
<td>90.3%</td>
<td>67.9%</td>
<td>14990</td>
<td>1085</td>
<td>58</td>
<td>7085</td>
</tr>
<tr>
<td>Valerian and the City of a Thousand Planets</td>
<td>74.1%</td>
<td>97.1%</td>
<td>60.0%</td>
<td>18663</td>
<td>548</td>
<td>0</td>
<td>12467</td>
</tr>
<tr>
<td>Downton Abbey: A New Era</td>
<td>56.2%</td>
<td>97.8%</td>
<td>39.4%</td>
<td>52027</td>
<td>1173</td>
<td>0</td>
<td>80084</td>
</tr>
<tr>
<td><strong>The Many Saints of Newark</strong></td>
<td><strong>46.3%</strong></td>
<td><strong>54.7%</strong></td>
<td>40.1%</td>
<td>15922</td>
<td>4394</td>
<td><strong>974</strong></td>
<td>23791</td>
</tr>
<tr>
<td><strong>macro average</strong></td>
<td><strong>67.4%</strong></td>
<td>85.8%</td>
<td>57.0%</td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
<p><strong>67.4% held-out vs. 75.3% on training</strong> — an ~8pp drop, and a <strong>37pp spread
between the best and worst held-out film</strong>. The config does not generalize
uniformly; two films are outright failure cases, for two different reasons.</p>
<h2 id="failure-mode-1-frozen-bbox-ghost-tracks">Failure mode 1: frozen-bbox "ghost tracks"<a class="headerlink" href="#failure-mode-1-frozen-bbox-ghost-tracks" title="Permanent link">&para;</a></h2>
<p>Both Many Saints of Newark (974 misIDs) and Downton Abbey (FN=80084, the worst
recall of the five) trace to the same root cause, verified directly against
the raw per-frame stream and the HDF5 dump's own detection counts — not
inferred from the score alone.</p>
<p><img alt="Frozen ghost boxes over background, The Many Saints of Newark" src="../assets/images/many_saints_ghost_fpi.jpg" /></p>
<p>At this second, three of the four labeled boxes ("Jon Bernthal", "Joey Diaz",
"Billy Magnussen") sit over empty background — a blurred wall, hanging
plates — with no face in them. The real face in frame carries a second,
colliding label from another frozen box.</p>
<p><img alt="15 ghost boxes over a blank title card, Downton Abbey: A New Era" src="../assets/images/downton_abbey_ghost_fpi.jpg" /></p>
<p>This is the starkest case: <strong>15 actors named, all wrong, over a completely
blank closing title card.</strong> Confirmed against the dump directly: <code>face_count</code>
is 0 from this point onward (no detector output at all), yet the same 15
identities keep appearing with the <em>exact same bounding box, unchanged to the
pixel</em>, for 57+ consecutive seconds.</p>
<p>This is <code>SceneTrackerFunc::active_[actor_idx].last_bbox</code>
(<code>src/nodes/scene_tracker_node.hpp</code>) being re-emitted unchanged — the
extinction state machine working exactly as coded, not a bug. The film cuts
from a packed group shot straight into 40+ seconds of blank titles/credits,
and <code>extinction_sec=57.4</code> is comfortably long enough to bridge that entire gap
without expiring, so the tracker faithfully reports "last known position" for
a cast that is no longer on screen at all. <code>extinction_sec</code> was tuned toward
long windows specifically because they bridge real gaps (occlusion, a turned
face) in most training footage — this is the cost side of that trade,
surfacing only when a film has a long enough faceless stretch to expose it.</p>
<h2 id="failure-mode-2-a-genuine-misid-for-contrast">Failure mode 2: a genuine misID (for contrast)<a class="headerlink" href="#failure-mode-2-a-genuine-misid-for-contrast" title="Permanent link">&para;</a></h2>
<p>Not every held-out failure is a ghost. This is a real face, correctly
detected, confidently misidentified:</p>
<p><em>(same many_saints_ghost_fpi.jpg frame above also shows Leslie Odom Jr.'s box
carrying a second, colliding "Michael Gandolfini" label — two real tracks'
frozen positions happening to overlap, not a detection error.)</em></p>
<h2 id="where-lvface-beat-x-ray">Where LVFace beat X-Ray<a class="headerlink" href="#where-lvface-beat-x-ray" title="Permanent link">&para;</a></h2>
<p>Not every "misID" is actually wrong. <code>second_score.py</code> counts a name as a true
out-of-cast misID whenever the named actor isn't in X-Ray's credited cast list
for the film at all — but X-Ray's cast list is itself incomplete.</p>
<p><img alt="LVFace correctly identifies Germar Terrell Gardner, uncredited by X-Ray" src="../assets/images/germar_beats_xray.jpg" /></p>
<p>Germar Terrell Gardner — a real, clean, high-confidence detection — is counted
as a misID here because he doesn't appear in X-Ray's <code>people.csv</code> for The Many
Saints of Newark at all. But Jellyfin's independent cast metadata <em>does</em> credit
him for this exact film (cross-checked via <code>experiments/manifests/
jellyfin_casts.json</code>, a completely separate data source from X-Ray). This
isn't a lookalike error or a gallery mixup — it's the pipeline correctly
recognising a real cast member that one ground-truth source happened to omit.</p>
<p>This doesn't mean every flagged misID is secretly correct — Many Saints'
974-count total is still overwhelmingly the frozen-bbox failure mode above,
not uncredited-but-real cameos. But it's a reminder that the X-Ray corpus is a
convenient, large-scale ground truth, not a perfect one, and the "misID" number
in any of these tables has some irreducible noise floor from ground-truth gaps
in the other direction too.</p>
<h2 id="summary">Summary<a class="headerlink" href="#summary" title="Permanent link">&para;</a></h2>
<p>LVFace is the right default: it wins the model comparison outright, and its
failures are traceable, understood, and mostly attributable to one tunable
knob (<code>extinction_sec</code>) rather than the embedder itself. The held-out
generalization gap (75.3% → 67.4%) is real and should be treated as the honest
expected performance, not the training-set number.</p>
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