957 lines
23 KiB
HTML
957 lines
23 KiB
HTML
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<meta name="description" content="Face-recognition pipeline for finding on-screen actor presence in film/TV, built on KPN++">
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<meta name="generator" content="mkdocs-1.6.1, mkdocs-material-9.7.7">
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<title>Best Model - scene-actor-extraction</title>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
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<div class="md-header__ellipsis">
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<div class="md-header__topic">
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<span class="md-ellipsis">
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scene-actor-extraction
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<div class="md-header__topic" data-md-component="header-topic">
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<span class="md-ellipsis">
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Best Model
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<script>var palette=__md_get("__palette");if(palette&&palette.color){if("(prefers-color-scheme)"===palette.color.media){var media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']");palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent")}for(var[key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
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<div class="md-header__source">
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<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction" title="Go to repository" class="md-source" data-md-component="source">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 7.1.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2025 Fonticons, Inc.--><path d="M439.6 236.1 244 40.5c-5.4-5.5-12.8-8.5-20.4-8.5s-15 3-20.4 8.4L162.5 81l51.5 51.5c27.1-9.1 52.7 16.8 43.4 43.7l49.7 49.7c34.2-11.8 61.2 31 35.5 56.7-26.5 26.5-70.2-2.9-56-37.3L240.3 199v121.9c25.3 12.5 22.3 41.8 9.1 55-6.4 6.4-15.2 10.1-24.3 10.1s-17.8-3.6-24.3-10.1c-17.6-17.6-11.1-46.9 11.2-56v-123c-20.8-8.5-24.6-30.7-18.6-45L142.6 101 8.5 235.1C3 240.6 0 247.9 0 255.5s3 15 8.5 20.4l195.6 195.7c5.4 5.4 12.7 8.4 20.4 8.4s15-3 20.4-8.4l194.7-194.7c5.4-5.4 8.4-12.8 8.4-20.4s-3-15-8.4-20.4"/></svg>
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<div class="md-source__repository">
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dtourolle/scene-actor-extraction
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</a>
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Home
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<li class="md-tabs__item md-tabs__item--active">
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<a href="./" class="md-tabs__link">
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Findings
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</a>
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</li>
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<li class="md-tabs__item">
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<a href="../rep4-optimizer-results/" class="md-tabs__link">
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Rep4 Bake-off & Re-tune (full log)
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</a>
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</li>
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<li class="md-tabs__item">
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<a href="../optimizer-experiments/" class="md-tabs__link">
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Optimizer Experiments (prior round)
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</a>
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</li>
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<li class="md-tabs__item">
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<a href="../service-conversion/" class="md-tabs__link">
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Service Conversion (proposal)
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</a>
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</li>
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<main class="md-main" data-md-component="main">
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<nav class="md-nav md-nav--primary md-nav--lifted" aria-label="Navigation" data-md-level="0">
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<label class="md-nav__title" for="__drawer">
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<a href=".." title="scene-actor-extraction" class="md-nav__button md-logo" aria-label="scene-actor-extraction" data-md-component="logo">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
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</a>
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scene-actor-extraction
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</label>
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<div class="md-nav__source">
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<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction" title="Go to repository" class="md-source" data-md-component="source">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 7.1.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2025 Fonticons, Inc.--><path d="M439.6 236.1 244 40.5c-5.4-5.5-12.8-8.5-20.4-8.5s-15 3-20.4 8.4L162.5 81l51.5 51.5c27.1-9.1 52.7 16.8 43.4 43.7l49.7 49.7c34.2-11.8 61.2 31 35.5 56.7-26.5 26.5-70.2-2.9-56-37.3L240.3 199v121.9c25.3 12.5 22.3 41.8 9.1 55-6.4 6.4-15.2 10.1-24.3 10.1s-17.8-3.6-24.3-10.1c-17.6-17.6-11.1-46.9 11.2-56v-123c-20.8-8.5-24.6-30.7-18.6-45L142.6 101 8.5 235.1C3 240.6 0 247.9 0 255.5s3 15 8.5 20.4l195.6 195.7c5.4 5.4 12.7 8.4 20.4 8.4s15-3 20.4-8.4l194.7-194.7c5.4-5.4 8.4-12.8 8.4-20.4s-3-15-8.4-20.4"/></svg>
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<div class="md-source__repository">
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dtourolle/scene-actor-extraction
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</a>
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Home
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<li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
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<label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="">
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<span class="md-ellipsis">
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Findings
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</span>
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<span class="md-nav__icon md-icon"></span>
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</label>
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<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="true">
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<span class="md-nav__icon md-icon"></span>
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Findings
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<span class="md-ellipsis">
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Best Model
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</span>
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<span class="md-nav__icon md-icon"></span>
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</label>
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<a href="./" class="md-nav__link md-nav__link--active">
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<span class="md-ellipsis">
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Best Model
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</span>
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</a>
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<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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<label class="md-nav__title" for="__toc">
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<span class="md-nav__icon md-icon"></span>
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Table of contents
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</label>
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<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
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<li class="md-nav__item">
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<a href="#first-signal-calibration-curves" class="md-nav__link">
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<span class="md-ellipsis">
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First signal: calibration curves
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="#second-signal-f1-on-the-actual-benchmark" class="md-nav__link">
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<span class="md-ellipsis">
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Second signal: F1 on the actual benchmark
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="#caveat-model-choice-is-an-operational-change" class="md-nav__link">
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<span class="md-ellipsis">
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Caveat: model choice is an operational change
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</span>
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</a>
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<span class="md-ellipsis">
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Gallery Scope (Full vs. Limited)
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Pose Expansion
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</a>
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LVFace Deep Dive
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Rep4 Bake-off & Re-tune (full log)
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</span>
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Optimizer Experiments (prior round)
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Service Conversion (proposal)
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<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
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<div class="md-sidebar__scrollwrap">
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<div class="md-sidebar__inner">
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<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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<label class="md-nav__title" for="__toc">
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Table of contents
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<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
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<a href="#first-signal-calibration-curves" class="md-nav__link">
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First signal: calibration curves
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Second signal: F1 on the actual benchmark
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Caveat: model choice is an operational change
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<article class="md-content__inner md-typeset">
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|
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|
||
<h1 id="which-embedding-model-is-best">Which embedding model is best?<a class="headerlink" href="#which-embedding-model-is-best" title="Permanent link">¶</a></h1>
|
||
<p>Four candidates went into the bake-off: three ArcFace variants (w600k-R50,
|
||
R18, w600k-MBF) and LVFace-B (Glint360K), a Vision-Transformer embedder that's
|
||
a drop-in replacement for ArcFace's <code>[N,3,112,112]</code> input / 512-d output. The
|
||
open question: is LVFace (455MB) actually better, or just the biggest?</p>
|
||
<h2 id="first-signal-calibration-curves">First signal: calibration curves<a class="headerlink" href="#first-signal-calibration-curves" title="Permanent link">¶</a></h2>
|
||
<p>Each gallery carries a fitted Platt sigmoid <code>P(match | cosine similarity) =
|
||
σ(a·sim + b)</code>, embedded directly in the gallery's HDF5 file
|
||
(<code>src/gallery/gallery_calibration.hpp</code>). This is a property of the embedding
|
||
space alone — computed from intra/inter-actor reference-image pairs, no
|
||
tracking or scene logic involved — so it's a clean first read on discriminative
|
||
power before running a single benchmark.</p>
|
||
<p><img alt="Calibrated P(match|similarity) for all four models" src="../assets/images/calibration_curves.png" /></p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>model</th>
|
||
<th><code>a</code> (steepness)</th>
|
||
<th>boundary at P=0.5</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td><strong>LVFace-B Glint360K</strong></td>
|
||
<td><strong>17.7</strong></td>
|
||
<td><strong>sim 0.228</strong></td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace w600k-MBF</td>
|
||
<td>16.2</td>
|
||
<td>sim 0.267</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace w600k-R50</td>
|
||
<td>15.4</td>
|
||
<td>sim 0.301</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace R18</td>
|
||
<td>15.3</td>
|
||
<td>sim 0.309</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>LVFace has both the steepest transition and the lowest decision boundary — it
|
||
separates same-actor from different-actor reference pairs more confidently, at
|
||
a <em>lower</em> similarity threshold, than any ArcFace variant. That's a genuine
|
||
head start before the tracking/scoring pipeline is even involved.</p>
|
||
<h2 id="second-signal-f1-on-the-actual-benchmark">Second signal: F1 on the actual benchmark<a class="headerlink" href="#second-signal-f1-on-the-actual-benchmark" title="Permanent link">¶</a></h2>
|
||
<p>Best full-gallery (no cast-restriction) result per model, from the 16-combo
|
||
rep4 matrix (<code>rep4-optimizer-results.md</code>):</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>model</th>
|
||
<th>F1</th>
|
||
<th>P</th>
|
||
<th>R</th>
|
||
<th>misID</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td><strong>LVFace-B Glint360K</strong></td>
|
||
<td><strong>75.3%</strong></td>
|
||
<td>89.7%</td>
|
||
<td><strong>65.4%</strong></td>
|
||
<td>232</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace w600k-MBF</td>
|
||
<td>74.2%</td>
|
||
<td>87.4%</td>
|
||
<td>64.4%</td>
|
||
<td>57</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace R18</td>
|
||
<td>69.1%</td>
|
||
<td>87.6%</td>
|
||
<td>57.7%</td>
|
||
<td>242</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace w600k-R50</td>
|
||
<td>68.5%</td>
|
||
<td>94.0%</td>
|
||
<td>54.1%</td>
|
||
<td>150</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>LVFace wins outright, with the highest recall of any full-mode combo. This
|
||
reverses an earlier conclusion from a prior (superseded) benchmarking pass
|
||
using a scene-union metric, which found the three models statistically
|
||
indistinguishable (~85% each) and concluded LVFace wasn't worth its size — that
|
||
metric hid out-of-cast false positives behind a gallery∩cast recall mask (see
|
||
<code>optimizer-experiments.md</code>); the per-second metric used here does not.</p>
|
||
<p>Held-out validation (5 films never seen by the optimizer) confirms LVFace's
|
||
lead holds up out of sample — see the deep-dive page for the full breakdown,
|
||
including where it fails.</p>
|
||
<h2 id="caveat-model-choice-is-an-operational-change">Caveat: model choice is an operational change<a class="headerlink" href="#caveat-model-choice-is-an-operational-change" title="Permanent link">¶</a></h2>
|
||
<p>Switching the default embedder isn't just flipping a config value — the
|
||
gallery itself is model-specific (embeddings from different models aren't
|
||
comparable), so any existing gallery built against ArcFace w600k-R50 needs to
|
||
be rebuilt from source images against LVFace before the new default takes
|
||
effect. <code>scripts/optimizer/reembed_gallery.py</code> does this from a reference
|
||
gallery's cached source images without re-downloading anything.</p>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
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</article>
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