docs: full data-grounded rewrite of the performance report
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.
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@@ -27,8 +27,11 @@ RESULTS = REPO / "experiments/results"
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# Same model -> color mapping as calibration_chart.py, so identity is stable
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# across every figure in the report.
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# r50 is dropped from the bake-off: its 4 combos ran under the old, narrower
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# anneal/extinction bounds and were never re-run wide, so they are not comparable
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# on those two params (and two of them were truncation-corrupted). Its slug stays
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# out of this map so it never appears in a figure or legend.
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MODEL_COLOURS = {
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"arcface_w600k_r50": ("ArcFace w600k-R50", "#2a78d6"),
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"arcface_r18": ("ArcFace R18", "#008300"),
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"arcface_w600k_mbf": ("ArcFace w600k-MBF", "#e87ba4"),
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"LVFace-B_Glint360K": ("LVFace-B Glint360K", "#eda100"),
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@@ -59,9 +62,37 @@ plt.rcParams.update({
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})
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TRAJ = REPO / "experiments/trajectories"
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def clean_best(combo: str) -> dict:
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"""Best-F1 eval for a combo, restricted to FULL-COVERAGE evals.
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The optimizer averages F1 (and *sums* TPI/misID) over only the films whose
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replay subprocess didn't time out (optimize.py: `per_film = [... if m is not
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None]`). A candidate whose hardest film timed out is therefore scored on an
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easier subset, which inflates its F1 — and DE will happily converge onto such
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a candidate. `rep4_best_*.json` recorded exactly that kind of eval for at
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least one combo (arcface_w600k_mbf_full_noexp: reported 74.2% F1 came from an
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eval with TPI 12645, a third of that combo's median).
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We recover comparable numbers straight from the trajectory: take the median
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TPI across all evals (full 4-film coverage) and keep only evals within 30% of
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it, then pick the highest-F1 survivor. No re-running — the honest best config
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is already in the sweep, just not the one `argmax f1` picked.
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"""
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evals = [json.loads(l) for l in open(TRAJ / f"rep4_{combo}.jsonl")]
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tpis = sorted(e["TPI"] for e in evals)
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med = tpis[len(tpis) // 2]
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clean = [e for e in evals if e["TPI"] >= 0.7 * med]
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return max(clean, key=lambda e: e["f1"])
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def training_best() -> dict:
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with open(RESULTS / "rep4_best_LVFace-B_Glint360K_full_exp.json") as f:
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return json.load(f)["best"]
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# LVFace-B_Glint360K_full_exp is the shipped combo; its reported best is a
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# full-coverage eval (TPI 47757 ≈ median), so clean_best returns the same
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# config — but route it through clean_best so every figure uses one path.
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return clean_best("LVFace-B_Glint360K_full_exp")
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def fig_holdout_f1(out: Path):
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@@ -100,18 +131,16 @@ def fig_holdout_f1(out: Path):
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def fig_rep4_matrix(out: Path):
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combos = []
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for path in sorted(RESULTS.glob("rep4_best_*.json")):
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stem = path.stem[len("rep4_best_"):]
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for path in sorted(TRAJ.glob("rep4_*.jsonl")):
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combo = path.stem[len("rep4_"):]
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for slug in MODEL_COLOURS:
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if stem.startswith(slug):
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mode = stem[len(slug) + 1:] # e.g. full_exp
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with open(path) as f:
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best = json.load(f)["best"]
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combos.append((slug, mode, best["f1"] * 100))
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if combo.startswith(slug):
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mode = combo[len(slug) + 1:] # e.g. full_exp
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combos.append((slug, mode, clean_best(combo)["f1"] * 100))
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break
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combos.sort(key=lambda c: c[2])
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fig, ax = plt.subplots(figsize=(9, 6.2))
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fig, ax = plt.subplots(figsize=(9, 5.2))
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ax.grid(axis="y", visible=False)
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labels = []
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for i, (slug, mode, f1) in enumerate(combos):
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@@ -126,7 +155,7 @@ def fig_rep4_matrix(out: Path):
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ax.set_yticks(range(len(combos)), labels, fontsize=9)
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ax.set_xlim(65, 80)
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ax.set_xlabel("training-set per-second F1 (%)")
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ax.set_title("All 16 combos — filled dot = cast-restricted gallery, open = full",
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ax.set_title("All 12 combos — filled dot = cast-restricted gallery, open = full",
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loc="left", fontsize=12, pad=12)
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handles = [plt.Line2D([], [], marker="o", ls="", ms=9, color=c, label=l)
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for _, (l, c) in MODEL_COLOURS.items()]
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@@ -199,13 +228,16 @@ def fig_downton_timeline(out: Path, t0: int = 7100, t1: int = 7340):
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trk = np.array(trk)
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dc = np.array([det.get(s, 0) for s in t])
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fig, ax = plt.subplots(figsize=(9.5, 4.4))
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fig, ax = plt.subplots(figsize=(9.5, 4.8))
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ax.grid(axis="x", visible=False)
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ax.fill_between(t, dc, step="mid", color=GREEN, alpha=0.25, zorder=2)
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ax.step(t, dc, where="mid", color=GREEN, lw=2, zorder=3)
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ax.step(t, trk, where="mid", color=BLUE, lw=2, zorder=4)
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ax.fill_between(t, dc, step="mid", color=GREEN, alpha=0.22, zorder=2)
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ax.step(t, dc, where="mid", color=GREEN, lw=2, zorder=3,
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label="faces seen by detector")
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ax.step(t, trk, where="mid", color=BLUE, lw=2, zorder=4,
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label="actors reported by tracker")
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# longest contiguous run of "detector sees nothing, tracker still reporting"
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# (i.e. every reported actor is extinction-bridged, not detected this second)
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ghost = (dc == 0) & (trk > 0)
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runs, start = [], None
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for i, g in enumerate(ghost):
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@@ -219,15 +251,11 @@ def fig_downton_timeline(out: Path, t0: int = 7100, t1: int = 7340):
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if runs:
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i0, i1 = max(runs, key=lambda r: r[1] - r[0])
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g0, g1 = t[i0], t[i1]
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ax.axvspan(g0, g1, color=RED, alpha=0.08, zorder=1)
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ax.annotate(f"{g1 - g0}s of credits: 0 faces detected,\n"
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f"{trk[i0]} actors still reported (frozen boxes)",
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((g0 + g1) / 2, 20.5), ha="center", va="bottom",
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fontsize=10, color=RED)
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ax.text(t0 + 4, 27.3, "actors reported by tracker", color=BLUE,
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fontsize=10.5, va="bottom")
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ax.text(t0 + 4, 11.5, "faces seen by detector", color=GREEN,
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fontsize=10.5, va="bottom")
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ax.axvspan(g0, g1, color=RED, alpha=0.08, zorder=1,
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label=f"{g1 - g0}s bridged: 0 faces detected,\n"
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f"{trk[i0]} actors carried by their\nextinction window")
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ax.legend(loc="upper right", frameon=True, framealpha=0.92,
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edgecolor=GRID, fontsize=9.5)
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ax.set_xlabel("film time (s)")
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ax.set_ylabel("count")
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ax.set_ylim(0, 31)
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