make_figures.py gains the DE-landscape, calibration, and per-film leave-one-out
holdout figures used by the experiment log. regen_frame_examples.sh replays all
nine films with the shipped learned-boundary flood config and draws GT-aware
TP/FP/FN frames, so every annotated image in the docs is reproducible.
- 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.
The parser reads a tag up to end of line, so `# TRACES: GR-004 | SR-001 —
prose` swallowed the prose into the tag and the row went unmatched. Splitting
the comment leaves the tag greppable by the same pattern as the code tags and
the commit trailers, which is the point of the house format.
Mechanical throughout; no logic touched. The regenerated report reflects this
session's new tags: 137 -> 148 found, and one more tagged-but-unexecuted, which
is the SuperHero accuracy assertion that is documented but not yet a test.
The cross-source study needs two 4K recordings and a hand-sorted set of
face crops, neither of which belongs in git. Adds an xsource target to
both artifact scripts.
Push uploads the clips as-is (already compressed) and zips labelling/.
Pull fetches both and regenerates frames with ffmpeg rather than
downloading them: ~320 MB of PNG that is deterministic from the clips.
The extraction settings are pinned in the script, not left to the
caller, because the manifests key on frame filenames and on detection
order within each frame — verify_labels.py runs afterwards and fails
loudly if they drift.
Pull refuses to overwrite an existing labelling/. It is human ground
truth: somebody looked at 167 crops and placed each one, and silently
replacing that with a remote copy would destroy the expensive half of
the study.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
TRACES: VR-013
Gallery from one recording, probes from another, sweeping the probe's
input resolution end to end. VR-005 asked the same question over gallery
mugshots but degraded an already-aligned 112x112 crop with alignment held
perfect, so it isolates the embedder. Here the whole frame is downscaled
before the detector, so detection and landmark regression degrade with
it — which is most of the difference.
Corpus is two 4096x2160 clips of one shoot, four people, hand-sorted.
Ground truth is sorted by hand and gated by verify_labels.py; labels
carried down the scales geometrically by box position, never by
embedding similarity, which would keep only the faces the embedder
already gets right and drop the ones the sweep exists to find.
Findings, all scored through the production gallery sigmoid at
prob_threshold 0.754 — never a raw cosine:
- Holding 90% of the plateau needs ~50 px end to end, against VR-005's
~22 px. min_face_px at 40 looks right; 32 would admit faces in the
falling region.
- FPI is 0.0% at every scale. Resolution loss goes entirely to TBI.
- The ceiling is cross-view, not resolution: everyone matches themselves
within a recording (0.55-0.85) and collapses across two (0.14-0.45,
threshold 0.335). Only the subject with frontal *gallery* references
identified reliably, whatever their probe pose — so the lever is
gallery pose coverage, not a better landmark source.
- Averaging SCRFD's overlapping detections instead of discarding them at
NMS lifts cross-recording TPI 41% -> 49%, for one forward pass and no
extra model.
Four identities and one shoot, so the shape is the result and the
absolute rates are not. Both clips contain all four people, so there is
no out-of-gallery class and the 10x-weighted out-of-cast misID is
untested here.
Clips, frames, hand-sorted crops and results are gitignored and belong
in the artifact registry — the sorting is human ground truth and
expensive to redo.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
TRACES: VR-013 | AR-002, AR-005, AR-024
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.
dtourolle
