A boosted-tree scene-boundary detector that replaces the grayscale
histogram-correlation cut detector as the flood-fill boundary source, and
substantially improves actor-presence accuracy.
Downstream result (per-second X-Ray presence F1, macro over 9 films):
track_extent 62.3% | flood + histogram cuts 64.0% | flood + this 76.9%
+12.9pp, and it wins on every film — notably fixing the histogram flood's
Scarface collapse (61 -> 41 -> 71) and lifting Downton 41 -> 84.
Design (each choice measured — see the memory / report):
- XGBoost REGRESSOR on a ±3s window of DELTA features (symmetric RGB-hist
and audio-PSD deltas at k=1,2,4,8s + ramp bank + time-since-last-peak
debounce). Raw histograms dilute; deltas separate boundaries ~4-5x.
- SOFT Gaussian proximity target (sigma=10s) so near-misses train as
near-correct, not hard negatives; regression -> smooth score -> NMS peaks.
- KNEE per-film threshold: self-calibrates the boundary count to ~the true
scene count, no global rate. Evaluated at ±20s (X-Ray scenes ~170s).
- Trained on all 9 films (Cafe/Scarface low-contrast grades must be seen).
Honest held-out ~41% boundary-F1 @±20s vs ~27% grayscale.
Scripts: train_xgb_boundary.py (shipped detector), extract_audio_features.py
(per-second log-PSD), downstream_presence.py (the A/B above), density_floor.py
(fallback for detection-starved films), plus the LSTM/DE explorations kept
for provenance. Model: models/scene_boundary_xgb.json.
Not yet wired into the live C++ pipeline — boundaries are a post-EOF step in
the sink (like flood-fill itself); libxgboost C++ integration is the next step.
- run_xray_lvface_opencv5.sh: end-to-end X-Ray benchmark (scene_analyze
per film → sample_eval) on the current build with LVFace-B.
- dump_lvface_opencv5.sh: fresh LVFace-B embedding dumps (plain front-half,
histogram cuts baked in) for the optimizer replay corpus. No decode-fps
cap — that only mattered under parallel dumping; serial it just halved
throughput.
- .gitignore: ignore build-*/ out-of-tree build dirs.
Implements the content-derived spectral-peak signature from
JRay-public-server/SPEC.md §3 so a truth file is self-identifying: 120 s
window centred on the media midpoint, mono at 11025 Hz, 4096/1024 Hann
STFT, 32 log-spaced bins over 300-3000 Hz, one byte per frame (5-bit peak
band + 2-bit energy class), base64, `v1:` prefix.
Audio decode is a second stream from the FFmpeg libraries the pipeline
already links for video; libswresample is added to the existing
ffmpeg_libs interface target. The FFT is written out rather than pulled
from a library for the same reason the plugin vendors one: the output has
to be bit-identical across two languages, so a dependency whose version
could change the numerics is a liability.
The server spec fixes the geometry but not enough to reproduce a byte
stream — Hann periodicity, band aggregation, the energy-class definition,
tie-breaking and the base64 alphabet are all unconstrained by it. Those
are pinned in audio_signature.hpp and mirrored in the golden fixture, so
the plugin can be implemented from the fixture alone.
IR-005: tests/fixtures/audio/ carries a deterministic 120 s tone (FLAC —
lossless, so identical PCM to the WAV make_fixture.py emits, and 3.5x
smaller in git) plus the signature it must produce, the decoded-PCM
checksum and the full parameter contract. That directory is the artefact
shared with the plugin repo; the PCM checksum is separate from the
signature so a codec-level difference is distinguishable from a DSP one.
IR-007: media under 120 s emits no signature. Same for a file with no
audio stream or one that will not open — UR-9 is an enhancement and must
never be able to break a fetch.
Verified against an independent Python reference implementation: same
bytes. All 32 bands and all 4 energy classes appear in the golden vector,
and the window-centring test wraps the fixture in 90 s of silence either
side and requires the golden value back.
Not wired into the truth-file output yet — that is the schema_version
bump under IR-002/IR-003 and is deliberately out of scope here.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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.
- switch report frames to the scene best/worst montage renderer
(Onscreen/Offscreen panels + TPI/FPI/FN legend): perfect-second hero,
wedding couple, funeral 19-of-20, polygraph bridging, crew-scene FN
ceiling, Robert Patrick ground-truth gap, rapid-cut double label,
Herbie Hancock on an in-fiction screen
- deep dive restructured: extinction bridging framed as designed
behavior with a measurable cost (debug overlay draws the boxes; the
shipped output is presence windows), plus the face-vs-presence
ceiling and two X-Ray-is-wrong exhibits
- Material polish: light/dark palette toggle, landing-page grid cards,
figure/caption CSS, how-to-read admonition; site_url set so 404 links
resolve under the Pages subpath
- README: perfect-second and screen-call frames committed (gitignore
exceptions), readme_example.jpg retired
- build_site.sh: stage_frame helper downscales montage frames to 1920px
and pulls any missing montage-frames packages
- experiment_charts.py generates 4 figures from experiments/ artifacts:
held-out per-film F1, 16-combo ranking, DE search landscape, and the
Downton detector-vs-tracker ghost timeline (replaces the blank
title-card screenshot)
- new frames: 19-correct wedding shot (success case), Many Saints
ghost-vs-unknown frame (three error classes in one image)
- rename rep4-optimizer-results.md -> model-bakeoff.md; rep4 kept only
as the on-disk artifact prefix, explained once
- repo file references are now links via https://REPOLINK/<path>
placeholders; build_site.sh pins them to the HEAD commit's raw URLs
and fails the build if a linked path doesn't exist at HEAD
- drop references to removed scripts (scene_score.py, score_config.py)
and to session-memory names; mark artifact-registry paths with their
pull commands
- commit readme_example.jpg + pipeline_topology.svg so README renders
on the plain Gitea repo view
- deploy_pages.sh: push built site/ to the gitea-pages branch
docs/rep4-optimizer-results.md is the main deliverable: the model bake-off +
threshold re-tune experiment log, including the ROCm teardown deadlock root
cause and fix, DE concurrency tuning, the 16-combo results table, held-out
validation against 5 films never seen by the optimizer (macro F1 67.4% vs.
75.3% training — a real generalization gap), the frozen-bbox "ghost track"
failure mode found via annotated frame evidence, calibration curves per model,
and an isolated-effects breakdown of gallery scope vs. pose expansion.
MkDocs site (mkdocs.yml, docs/index.md) renders docs/*.md; scripts/docs/
pulls referenced images from the artifact registry and generates the
calibration chart at build time (see the tooling commit) rather than
committing images to the repo.
experiments/ now keeps only scripts + README + SESSION_STATE.md in git — every
data artifact (galleries, dumps, X-Ray corpus, montage frames, trajectories,
manifests, results) moved to the Gitea package registry. film-lut.template.json
is the committed placeholder for the gitignored file-lut.json (real local
movie paths, never shared — some source filenames carry scene-release tags).
Adds models/transnetv2.onnx (via Git LFS, matching the other ONNX models) for
the new scene-detection path.
These are regenerable per-run outputs that were polluting the worktree:
calibration caches (*.calib_cache.csv/png), cameo detection run outputs
(cameo_progress.txt, cameo_report.txt), and scene-gap analysis plots.
dtourolle
