1140 lines
29 KiB
HTML
1140 lines
29 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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<link rel="canonical" href="https://pages.tourolle.paris/dtourolle/scene-actor-extraction/best-model/">
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<title>Best Model - scene-actor-extraction</title>
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<style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
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<header class="md-header" data-md-component="header">
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<nav class="md-header__inner md-grid" aria-label="Header">
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<a href=".." title="scene-actor-extraction" class="md-header__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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<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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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 7a5 5 0 0 1 5 5 5 5 0 0 1-5 5 5 5 0 0 1-5-5 5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3 3 3 0 0 0 3 3 3 3 0 0 0 3-3 3 3 0 0 0-3-3m0-7 2.39 3.42C13.65 5.15 12.84 5 12 5s-1.65.15-2.39.42zM3.34 7l4.16-.35A7.2 7.2 0 0 0 5.94 8.5c-.44.74-.69 1.5-.83 2.29zm.02 10 1.76-3.77a7.131 7.131 0 0 0 2.38 4.14zM20.65 7l-1.77 3.79a7.02 7.02 0 0 0-2.38-4.15zm-.01 10-4.14.36c.59-.51 1.12-1.14 1.54-1.86.42-.73.69-1.5.83-2.29zM12 22l-2.41-3.44c.74.27 1.55.44 2.41.44.82 0 1.63-.17 2.37-.44z"/></svg>
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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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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.52 6.52 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5"/></svg>
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Initializing search
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</div>
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</div>
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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>
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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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</nav>
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<div class="md-container" data-md-component="container">
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<nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
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<a href=".." class="md-tabs__link">
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Home
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<li class="md-tabs__item">
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<a href="../methodology/" class="md-tabs__link">
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How We Score Against X-Ray
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</a>
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</li>
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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="../model-bakeoff/" class="md-tabs__link">
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Full Experiment 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="../service-conversion/" class="md-tabs__link">
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Service Conversion (proposal)
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</a>
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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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<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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scene-actor-extraction
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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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dtourolle/scene-actor-extraction
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Home
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<span class="md-ellipsis">
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How We Score Against X-Ray
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</a>
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<span class="md-ellipsis">
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Findings
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</span>
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Best Model
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Best Model
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First signal: calibration curves
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Second signal: held-out F1
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Full training-matrix picture
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Gallery Scope (Full vs. Limited)
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Pose Expansion
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LVFace Deep Dive
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Full Experiment Log
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Service Conversion (proposal)
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First signal: calibration curves
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<article class="md-content__inner md-typeset">
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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>Three ArcFace variants (w600k-R50, R18, w600k-MBF) and LVFace-B (Glint360K,
|
||
455MB) were compared. r50 is excluded from the training/held-out comparison
|
||
below; its gallery has roughly 30% fewer reference images per actor than the
|
||
other three on the identical source photos, which confounds a direct score
|
||
comparison (see <a href="../model-bakeoff/">the full experiment log</a> for detail). It
|
||
remains in the calibration comparison, which does not depend on the gallery
|
||
image count.</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>, stored directly in the gallery HDF5
|
||
(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/e5885977dfd04741a0d1bb7aaf0270d9c40a2fa8/src/gallery/gallery_calibration.hpp"><code>src/gallery/gallery_calibration.hpp</code></a>).
|
||
This is a property of the embedding space alone, computed from intra- and
|
||
inter-actor reference-image pairs with no tracking or scene logic involved,
|
||
so it is a clean first read on discriminative power before running a
|
||
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>a (steepness)</th>
|
||
<th>boundary at P=0.5</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>LVFace-B Glint360K</td>
|
||
<td>17.7</td>
|
||
<td>sim 0.228</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,
|
||
separating same-actor from different-actor reference pairs more confidently
|
||
at a lower similarity than any ArcFace variant.</p>
|
||
<h2 id="second-signal-held-out-f1">Second signal: held-out F1<a class="headerlink" href="#second-signal-held-out-f1" title="Permanent link">¶</a></h2>
|
||
<p>Each model's own tuned <code>full_exp</code> config, replayed against the 5 films the
|
||
optimizer never saw and scored the same way:</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>film</th>
|
||
<th>LVFace F1</th>
|
||
<th>mbf F1</th>
|
||
<th>r18 F1</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Benny & Joon</td>
|
||
<td>83.0%</td>
|
||
<td>78.5%</td>
|
||
<td>77.1%</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Lovelace</td>
|
||
<td>77.5%</td>
|
||
<td>73.7%</td>
|
||
<td>72.2%</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Valerian and the City of a Thousand Planets</td>
|
||
<td>74.1%</td>
|
||
<td>70.2%</td>
|
||
<td>71.0%</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Downton Abbey: A New Era</td>
|
||
<td>56.2%</td>
|
||
<td>55.0%</td>
|
||
<td>53.0%</td>
|
||
</tr>
|
||
<tr>
|
||
<td>The Many Saints of Newark</td>
|
||
<td>46.3%</td>
|
||
<td>44.5%</td>
|
||
<td>42.1%</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>macro average</strong></td>
|
||
<td><strong>67.4%</strong></td>
|
||
<td><strong>64.4%</strong></td>
|
||
<td><strong>63.1%</strong></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>LVFace scores highest on all 5 held-out films; the ranking never flips
|
||
between models. Total misID count across the 5 films: LVFace 1032, mbf
|
||
2197, r18 1224. LVFace has less than half mbf's misID total and still
|
||
scores higher on every film.</p>
|
||
<p>Held-out results are stronger evidence than training results, because
|
||
training numbers can reflect what the optimizer was tuned to fit rather
|
||
than general performance. On training data, the ordering is not as clean:</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>film</th>
|
||
<th>LVFace F1</th>
|
||
<th>mbf F1</th>
|
||
<th>r18 F1</th>
|
||
<th>best</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Café Society</td>
|
||
<td>68.1%</td>
|
||
<td>62.2%</td>
|
||
<td>60.1%</td>
|
||
<td>LVFace</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Lord of War</td>
|
||
<td>75.6%</td>
|
||
<td>77.2%</td>
|
||
<td>75.6%</td>
|
||
<td>mbf</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Scarface</td>
|
||
<td>71.5%</td>
|
||
<td>68.6%</td>
|
||
<td>64.1%</td>
|
||
<td>LVFace</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Sound of Metal</td>
|
||
<td>78.8%</td>
|
||
<td>76.5%</td>
|
||
<td>71.6%</td>
|
||
<td>LVFace</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>mbf beats LVFace on Lord of War (77.2% vs 75.6%), the only film in either
|
||
table where LVFace does not score highest. LVFace's training-set macro
|
||
average (75.3%, see <a href="../model-bakeoff/">the full experiment log</a>) is not a
|
||
uniform win across every film it contributes to; the held-out result, where
|
||
LVFace wins all 5 films outright, is the stronger claim.</p>
|
||
<p>This reverses an earlier, superseded benchmarking pass that used a
|
||
scene-union metric and found the three models statistically
|
||
indistinguishable (around 85% each), concluding LVFace was not worth its
|
||
size. That metric masked out-of-cast false positives behind a
|
||
gallery-intersect-cast recall filter; the per-second metric used here does
|
||
not.</p>
|
||
<h2 id="full-training-matrix-picture">Full training-matrix picture<a class="headerlink" href="#full-training-matrix-picture" title="Permanent link">¶</a></h2>
|
||
<p><img alt="All 12 combos ranked by training-set F1" src="../assets/images/rep4_matrix_f1.png" /></p>
|
||
<p>Best full-gallery combo per model (all three are <code>full_exp</code>), from the
|
||
training matrix in <a href="../model-bakeoff/">the full experiment log</a>:</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>model</th>
|
||
<th>F1</th>
|
||
<th>P</th>
|
||
<th>R</th>
|
||
<th>misID</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>LVFace-B Glint360K</td>
|
||
<td>75.3%</td>
|
||
<td>89.7%</td>
|
||
<td>65.4%</td>
|
||
<td>232</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace w600k-MBF</td>
|
||
<td>72.0%</td>
|
||
<td>87.7%</td>
|
||
<td>61.4%</td>
|
||
<td>240</td>
|
||
</tr>
|
||
<tr>
|
||
<td>ArcFace R18</td>
|
||
<td>69.1%</td>
|
||
<td>87.6%</td>
|
||
<td>57.7%</td>
|
||
<td>242</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>LVFace leads within both the restricted and full gallery modes, visible
|
||
directly in the chart above without reading the table. The three models'
|
||
misID counts on the full gallery are nearly identical (232/240/242); LVFace's
|
||
lead here is a precision-and-recall lead, not a misID one.</p>
|
||
<h2 id="operational-note">Operational note<a class="headerlink" href="#operational-note" title="Permanent link">¶</a></h2>
|
||
<p>Switching the default embedder is not a config change alone; the gallery
|
||
is model-specific, since embeddings from different models are not
|
||
comparable. Any existing gallery built against a different model must be
|
||
rebuilt from source images before the new default takes effect.
|
||
<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/e5885977dfd04741a0d1bb7aaf0270d9c40a2fa8/scripts/optimizer/reembed_gallery.py"><code>scripts/optimizer/reembed_gallery.py</code></a>
|
||
does this from a reference gallery's cached source images without
|
||
re-downloading anything.</p>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
</article>
|
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