dtourolle
26de01b2e3
Extends the VR-005 protocol -- hold out one mugshot per actor, degrade only the probe, match against a gallery held at native resolution, decide through the Platt calibration -- from one axis to two, over 1670 actors rather than 100. Joint rather than separable, because the interaction is the question: a 16 px face upscaled to 112 has already lost its high frequencies, so further blur costs it almost nothing, while the same blur at full resolution is expensive. Sweeping the axes independently would measure each with the other implicitly at its best and miss that entirely. Three blur families, compared at matched per-axis PSF spread rather than at equal raw parameter. Optical defocus is a uniform disc whose transfer function is a jinc with exact zeros, not a Gaussian that merely rolls off, and it is also how a face ends up large and useless -- the case a size filter cannot catch. Sweeping Gaussian alone, as the first version did, understates real lens blur by a factor of five in error rate. Every candidate measure is scored on every degraded crop and the candidates are ranked by how well each predicts the pipeline's actual decision, not by how smooth its synthetic ladder looks. Both a pooled and a within-cell AUC are reported: they answer different questions and the candidates rank differently under each. Runs through sae_embed throughout. Stages gains optional engine paths so the same study can drive a TRT build, which is what makes the full grid five minutes rather than four and a half hours. TRACES: VR-012, AR-028, AR-029 | SR-002
scripts/validation — per-scene actor-presence eval
Validates the pipeline's per-scene "who's on screen" output against external
ground truth, offline. Annealing (anneal_sec) means an actor's presence is only
defined after the whole file is merged into [start,end] windows, so we cannot
score live: process → write the pipeline JSON → sample timepoints → compare
predicted vs ground-truth presence sets → micro-sum TP/FP/FN → precision/recall/F1.
Ground-truth sources
| Source | Semantics | Fair to a face pipeline? | What it measures |
|---|---|---|---|
| MovieNet-PS | on-screen face presence per shot | yes — like-for-like | recognition accuracy |
| Amazon X-Ray (Zenodo) | cast-in-scene (incl. off-camera / non-speaking) | no — penalizes by design | coverage ceiling; recall gap = actors we structurally can't see |
- MovieNet is the honest recognition number.
- X-Ray is an upper bound: its recall gap tells you how much presence is off-camera cast a face detector can never reach — not a pipeline error.
X-Ray dataset: Zenodo DOI 10.5281/zenodo.17659734 (CC-BY-4.0). Per movie it ships
people.csv, scenes.csv, people_in_scenes.csv.
Usage
# against Amazon X-Ray CSVs for one title
python scripts/validation/sample_eval.py \
--pred "Scene in a Mall.json" \
--xray /data/xray/<movie_dir> \
--gallery gallery_arcface_w600k_r50.json \
--step 1.0
# against MovieNet-PS for one title
python scripts/validation/sample_eval.py \
--pred out.json \
--movienet /data/movienet --split Train_app10 --title tt0032138 \
--gallery gallery_arcface_w600k_r50.json
Sampling modes
--step Sregular grid every S s (default 1.0) — time-weighted headline number.--random NN uniform-random timepoints (for confidence intervals).--scene-anchoredone timepoint per GT scene midpoint — the literal X-Ray "did I get this scene's cast right?" question; neutralizes long-scene bias.
Ground truth is compared raw (annealing is not applied to GT).
Matching & masking
Identity is provider-agnostic (identity.py): each actor is the set of every key
we can derive — imdb:nm…, tmdb:…, jf:…, name:<normalized>. Predicted and GT
actors match iff their key-sets intersect, so an output carrying only tmdb/jellyfin
ids still joins X-Ray's nm ids via the normalized-name fallback.
Scoring is masked to gallery ∩ GT: a GT actor absent from the gallery is
ignored (not an FN), so we measure pipeline accuracy, not gallery coverage. Without
--gallery the mask falls back to GT ∩ pred keys. --no-mask disables it.
Exact id join via the tmdb→imdb crosswalk (recommended)
The gallery/pipeline output key actors by TMDB id (no nm…), while X-Ray and
MovieNet key on IMDb. They only overlap on the fuzzy name: key by default.
Build a cached tmdb→imdb table once and pass it with --crosswalk to turn the
name join into an exact id join:
# one-time: resolve every gallery tmdb id via TMDB /person/{id}/external_ids
python scripts/validation/tmdb_imdb_map.py \
--gallery gallery_arcface_w600k_r50.json \
--out scripts/validation/tmdb_imdb.json # TMDB_API_KEY from env/.env
# then score with exact ids
python scripts/validation/sample_eval.py --pred out.json --xray <dir> \
--gallery gallery_arcface_w600k_r50.json \
--crosswalk scripts/validation/tmdb_imdb.json
The table caches nulls (tmdb ids TMDB has no IMDb id for) and checkpoints, so a
re-run only resolves new ids. TMDB is authoritative for this crosswalk — there is
no clean free bulk tmdb_person ↔ nm file, so we query the API once and cache.
Minimum face size (VR-005)
min_face_size.py is a separate, self-contained study: it needs no video and no
ground truth, only the gallery mugshot cache. It holds out one image per actor,
degrades that probe to each candidate face size and matches it against a gallery
held at native resolution, reporting TPI/FPI per size — the measurement that
replaces AR-002's 66×66 px estimate.
python scripts/validation/min_face_size.py \
--images images --gallery gallery_lvface.h5 \
--arcface models/LVFace-B_Glint360K.onnx \
--actors 100 --out experiments/results/vr005_min_face_size
FPI grows with the number of actors competing, so a 100-actor run understates it
against a library of thousands: read FPI as relative across sizes, not as an
absolute rate. Re-run per --arcface model to see whether min_face_px should be
one constant or scale with the embedder (GR-004).
Files
sample_eval.py— CLI scorer.ground_truth.py—XRayGroundTruth,MovieNetGroundTruthloaders.identity.py— provider-agnostic match keys.tmdb_imdb_map.py— build/consult the cachedtmdb→imdbcrosswalk.min_face_size.py— VR-005 probe-size sweep (see above).test_sample_eval.py— self-contained tests (python scripts/validation/test_sample_eval.py).