dtourolle
0bd2747069
Replaces narrative claims with verified numbers across all report pages: - Cross-model held-out validation (LVFace/mbf/r18, all 5 held-out films): LVFace wins every film outright, not just "consistent with" the training-set pick. r50 dropped from the detailed comparison (gallery has ~30% fewer reference images per actor than the other three models on identical source photos). - Per-film training breakdown: LVFace does not win every training film (mbf beats it on Lord of War); the 75.3% macro figure hides a 10.7pp spread. - Gallery coverage computed per film (20.3%-78.6%) instead of one flat 67%-missing average. - Found and fixed a real scoring bug in optimize.py: a candidate whose hardest film's replay timed out was averaged over survivors instead of penalized, silently rewarding partial coverage. Affected 3 of 16 training combos; corrected throughout, and optimize.py now scores an incomplete evaluation f1=0.0 instead of averaging over whichever films happened to finish. - Every FPI frame in the deep dive now comes from the proper montage renderer (Onscreen/Offscreen panel, ghosts never drawn as boxes), never the bare-box debug overlay used earlier. - Every distinct out-of-cast name across all 9 films gets its own frame at its first appearance (9 names, 4 films), not a single-example spot check: 2 ground-truth gaps, 1 photograph misread as a person, 6 genuine lookalike confusions. - New methodology.md: the scene-level-vs-per-second scoring mismatch that the rest of the report assumes, written out once. - Cut the deadlock/gdb debugging narrative from the experiment log; kept the one fact that matters (KPN's node/network split lets the expensive GPU stage run once and the cheap stage replay against cached embeddings). - Plain declarative style throughout, no em dashes, no blog voice.
3.5 KiB
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, and scene pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated optimizer.
This is a correctly scored second from a held-out film, one the optimizer never saw during tuning:
Every visible face is named at 100% confidence (Chris Noth, Hank Azaria, Bobby Cannavale), the background extra is correctly left unnamed, and the two credited cast members without a visible face are correctly reported present but not visible. This matches Amazon X-Ray's own record for this second exactly.
Results are not uniform across films. The hardest held-out film scores 46% F1. This report documents why: one tunable trade (extinction bridging at hard cuts), one structural limit (X-Ray credits people whose faces never appear on screen), and a small number of cases where the pipeline is correct and X-Ray's ground truth is not. Read how we score against X-Ray first. X-Ray's ground truth is scene-level; the pipeline's output is per-second. That difference shapes every finding below.
Findings
-
:material-trophy:{ .lg .middle } Which model is best?
Calibration curves first, independent of any threshold, then held-out F1 across three models. LVFace-B Glint360K wins both, and wins on every held-out film.
-
:material-filter:{ .lg .middle } Whole vs. cast-restricted gallery
Restricting the matcher to a film's credited cast improves F1, recall, and misID rate at once, but is not a shipped runtime feature yet.
-
:material-account-convert:{ .lg .middle } Does pose expansion help?
A training-set effect that did not reproduce on 5 held-out films once two methodology bugs in the comparison harness were found and fixed.
-
:material-magnify-expand:{ .lg .middle } Deep dive: LVFace-B Glint360K
The held-out generalization gap, the two mechanisms behind its errors, and every distinct case where it names someone outside the film's credited cast.
Full experiment log
- Full experiment log: the complete log behind the
four pages above, including how replaying against cached embeddings
inside the same KPN network makes a full model and configuration
comparison practical, the full results table, and every caveat. This is
where the shipped
src/config.hppdefaults come from. - Service conversion (proposal): design sketch for a native idle-GPU worker gated on screen lock, 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 are
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, which requires a GITEA_TOKEN with package write
scope.