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.
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scene-actor-extraction
A face-recognition pipeline that finds when each actor appears on screen in a film or TV episode — built on KPN++ (a C++20 Kahn Process Network library) for the detect → track → match → scene pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated optimizer.
Start here — four questions this bake-off answers
- Which model is best? — calibration curves first (discriminative power, independent of any threshold), then F1 on the actual benchmark. LVFace-B Glint360K wins both.
- Whole gallery vs. limited (cast-restricted) gallery — restricting the matcher to a film's credited cast is a clean win on every axis (+3.3pp F1, less than a third the misIDs), but isn't a shipped runtime feature yet.
- Does pose expansion help? — a real training-set effect that didn't reproduce on 5 held-out films once two methodology bugs were caught and fixed. An honest null result, not a forced narrative.
- Deep dive: LVFace-B Glint360K — the winning model's held-out generalization gap, its two real failure modes (frozen-bbox "ghost tracks"), and one case where it correctly identified an actor that the X-Ray ground truth itself failed to credit.
The full technical log
- Rep4 model bake-off + threshold re-tune —
the complete experiment log behind the four pages above: the ROCm teardown
deadlock root cause and fix, DE concurrency tuning, the full 16-combo
results table, and every caveat. This is where the shipped
src/config.hppdefaults come from. - Optimizer experiments (prior round) — the earlier scene-union-metric tuning pass, superseded by the per-second metric used in rep4 but kept for the ground-truth/architecture background.
- Service conversion (proposal) — design sketch for an idle-GPU Docker worker, not yet built.
Reproducing the benchmarks
Gallery .h5 files, embedding dumps, the X-Ray corpus, montage frame images,
and DE trajectories are not committed to this repository — they're pushed to
the Gitea package registry and pulled on demand:
scripts/artifacts/pull_artifacts.sh galleries
scripts/artifacts/pull_artifacts.sh experiment-data
scripts/artifacts/pull_artifacts.sh montage-frames <film-slug>
See scripts/artifacts/push_artifacts.sh for the upload side (requires a
GITEA_TOKEN with package write scope).