dtourolle
4925443e56
Splits the rep4 write-up's key findings into their own linkable pages: - best-model.md: calibration curves first (discriminative power, independent of any threshold), then F1 on the benchmark — LVFace-B Glint360K wins both. - gallery-scope.md: whole vs. cast-restricted gallery, isolated from model and expansion choice — restriction wins on every axis, but isn't a shipped runtime feature yet. - pose-expansion.md: the training-set expand_gallery effect, and the held-out replication attempt that found it doesn't reproduce (5 films, 2 models, after catching and fixing a replay-timeout truncation bug and a bbox first-match-instead-of-best-match bug in the comparison harness itself). An honest null result, with the methodology errors documented since they're exactly the kind that manufacture a false "it works!" finding. - lvface-deep-dive.md: the winning model's held-out generalization gap, its two failure modes (frozen-bbox ghost tracks), and a verified case (cross- checked against Jellyfin's independent cast metadata) where LVFace correctly identified an actor that X-Ray's ground truth failed to credit. Adds a "report-highlights" artifact-registry package (scripts/artifacts/ push_artifacts.sh, pull_artifacts.sh) for hand-picked illustrative frames that aren't reproducible via the automated best/worst montage selection, and wires pulling it into scripts/docs/build_site.sh.
52 lines
2.5 KiB
Markdown
52 lines
2.5 KiB
Markdown
# scene-actor-extraction
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A face-recognition pipeline that finds when each actor appears on screen in a
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film or TV episode — built on [KPN++](https://gitea.tourolle.paris/dtourolle/KPN)
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(a C++20 Kahn Process Network library) for the detect → track → match → scene
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pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated optimizer.
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## Start here — four questions this bake-off answers
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- **[Which model is best?](best-model.md)** — calibration curves first
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(discriminative power, independent of any threshold), then F1 on the actual
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benchmark. LVFace-B Glint360K wins both.
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- **[Whole gallery vs. limited (cast-restricted) gallery](gallery-scope.md)** —
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restricting the matcher to a film's credited cast is a clean win on every
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axis (+3.3pp F1, less than a third the misIDs), but isn't a shipped runtime
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feature yet.
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- **[Does pose expansion help?](pose-expansion.md)** — a real training-set
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effect that didn't reproduce on 5 held-out films once two methodology bugs
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were caught and fixed. An honest null result, not a forced narrative.
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- **[Deep dive: LVFace-B Glint360K](lvface-deep-dive.md)** — the winning
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model's held-out generalization gap, its two real failure modes (frozen-bbox
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"ghost tracks"), and one case where it correctly identified an actor that
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the X-Ray ground truth itself failed to credit.
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## The full technical log
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- **[Rep4 model bake-off + threshold re-tune](rep4-optimizer-results.md)** —
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the complete experiment log behind the four pages above: the ROCm teardown
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deadlock root cause and fix, DE concurrency tuning, the full 16-combo
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results table, and every caveat. This is where the shipped `src/config.hpp`
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defaults come from.
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- **[Optimizer experiments (prior round)](optimizer-experiments.md)** — the
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earlier scene-union-metric tuning pass, superseded by the per-second metric
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used in rep4 but kept for the ground-truth/architecture background.
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- **[Service conversion (proposal)](service-conversion.md)** — design sketch
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for an idle-GPU Docker worker, not yet built.
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## Reproducing the benchmarks
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Gallery `.h5` files, embedding dumps, the X-Ray corpus, montage frame images,
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and DE trajectories are not committed to this repository — they're pushed to
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the Gitea package registry and pulled on demand:
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```bash
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scripts/artifacts/pull_artifacts.sh galleries
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scripts/artifacts/pull_artifacts.sh experiment-data
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scripts/artifacts/pull_artifacts.sh montage-frames <film-slug>
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```
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See `scripts/artifacts/push_artifacts.sh` for the upload side (requires a
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`GITEA_TOKEN` with package write scope).
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