From 079021ed0b26d1e90c17e81a00cabed0bb531949 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 15:02:19 -0700 Subject: [PATCH 01/10] Vistas at resolution parity: the handicap was real, and it was recall (#126) The published Vistas richmond numbers were measured with the checkpoint's own 384x384 preprocessor in force, so every 1024x1024 view was downsized to ~1/7 the pixel area while every other tiled leg sees the full view. That made the 0.338 F1 gap to RampNet an upper bound rather than a measurement. #129 added --vistas-input-size for exactly this run; this is it. Read pre-registered on #126 before the scored output was read: gap closes by < 0.05 F1 => "transfers but does not compete" stands, one split, stop. Result: it stands, and not narrowly. F1 0.516 -> 0.534 (+0.018), about a third of the bar. RampNet's lead goes 0.339 -> 0.321. The mechanism is the actual finding, and it is not what the caveat predicted. The handicap was large but almost entirely a RECALL handicap: recall 0.694 -> 0.884 (+0.190; misses 95 -> 36, a 62% reduction) AP 0.510 -> 0.649 (+0.139, 27% relative) precision 0.411 -> 0.383 (-0.028, slightly worse) At parity this arm out-recalls RampNet (0.884 vs 0.768) while never having seen one of our labels. F1 stayed flat only because precision is the binding constraint and resolution does nothing for it -- which sharpens the conclusion #126 was built to test: "the concept is findable, the discrimination is not" now holds against the supervised arm at equal input, no longer confounded with pixels. Also ran a same-env 384 control, which was not in the plan and earned its place. The published run was on an RTX 3070 on an older transformers; makelab2 is a major version on, and the transformers version is not in the detection signature -- a hazard this document already flags. So parity-vs-published would have differed in two things. The control lands within ONE detection out of 523 of the published run (215/308/95 vs 216/309/94), so the 4.x->5.15 jump is benign for this checkpoint and the whole delta is attributable to input size. That retires the "an upgrade could have changed every mask under an unchanged cache key" worry as a measurement. Two claims stated at 384 are corrected: y26_pano's richmond AP 0.536 no longer clears this arm at 0.649 (the YOLO sentence's conclusion holds, its arithmetic does not), and min_area_px=16 is inert at 384 but sits exactly at the smallest achievable blob at 1024, so the two rows do not share that setting's meaning. Parity detections are deliberately not published: --vistas-input-size does not change the arm's label, so exporting would overwrite the published richmond file. Publishing them needs a distinct name (the --publish-as pattern from #123). Measured cost, since the estimate going in was 3-4x and wrong in the cheap direction: the full 124-pano run is 3m38s on one A40, GPU forward 0.078 -> 0.092 s per view (1.17x). Verified before the run that the override reaches the model (pixel_values 384 -> 1024, mask logits 96x96 -> 256x256) rather than silently no-opping on a new major version. Docs only; no code, no detection artifacts, no roster change. Co-Authored-By: Claude Opus 5 --- docs/model_comparison.md | 124 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 120 insertions(+), 4 deletions(-) diff --git a/docs/model_comparison.md b/docs/model_comparison.md index e1fe7a91..1de84857 100644 --- a/docs/model_comparison.md +++ b/docs/model_comparison.md @@ -1175,6 +1175,14 @@ upsample can produce is on the order of 114 px and the 16 px floor drops nothing at 16 rather than recalibrated because it sits in the cache signature — changing it would orphan both arms' published detections to no effect. +**That inertness is a property of 384×384, not of the floor, and it does not carry to the +parity run below.** At 1024×1024 input the mask logits arrive at 256×256 and are upsampled +only 4×, so the smallest component the upsample can produce is about **16 px — exactly the +floor**. `min_area_px=16` is therefore marginally *binding* at parity where it was provably +inert at 384, and it is the one setting shared by both that does not mean the same thing in +each. It was still left untouched, because changing it would confound the single variable the +parity run exists to isolate. + The **rig** is the identical six-view one every other tiled leg uses (`equirect_tiling.default_views()`: 6 yaws, 90°×90°, pitch −30°, 1024×1024, source capped at 4096) — but **the rig is not what the model sees, and that is a real caveat on the headline @@ -1190,10 +1198,10 @@ upgrade could have changed every mask under an unchanged cache key. What changed here: `--vistas-input-size H W` now overrides the processor, and `--vistas-revision` pins the checkpoint. Both are recorded in the signature **only when set**, so the published richmond detections keep their key and nothing already paid for is orphaned — -and a future run at parity is a distinct, self-describing cache entry. **The parity run has not -been done**, so every number in this section is at 384×384 and the gap to RampNet is an upper -bound on this arm, not a measurement of it at equal input. That is the first thing to try if -this arm is ever revisited. +and a future run at parity is a distinct, self-describing cache entry. **The parity run has now +been done** — see *Resolution parity* below. Every number in the table that follows is still at +384×384, and the parity numbers are reported separately rather than replacing them, because the +384 run is the one whose detections are published. Note for anyone reading #126: that issue says `scripts/box_gallery.py` already cuts perspective views. **It does not** — its `--fov` sizes an axis-aligned crop of the @@ -1282,6 +1290,114 @@ AP at all, emitting boxes without scores, so they are pinned at one operating po be tuned, and a tunable model at AP 0.513 is a more useful starting point than an untunable one at F1 0.664. +#### Resolution parity: the handicap was real, and it was not the problem (2026-08-18) + +Everything above is measured at 384×384. This section removes that handicap and changes one +variable. Read fixed in advance and posted to #126 **before** the scored output was read: the +gap closes by **< 0.05 F1** ⇒ *"transfers but does not compete"* stands and this stays a +one-split arm; more than that ⇒ the write-up is revised and 3–4 further splits get costed. + +Run on **makelab2 (A40, fp16, transformers 5.15.0 / torch 2.13.0+cu130)**, in a scratch worktree +with a private `--cache-dir`, so nothing here shares a cache directory with the published +detections. RampNet is re-run alongside as the comparability check and **reproduces its committed +richmond row to every digit** (0.964 / 0.768 / 0.855, AP 0.763, 238/9/72). + +| arm | model input | P | R | F1 | AP | tp/fp/fn | +|---|---|---:|---:|---:|---:|---| +| rampnet (committed, reproduced) | — | 0.964 | 0.768 | **0.855** | 0.763 | 238/9/72 | +| vistas curb-cut — **published** (RTX 3070) | 384×384 | 0.411 | 0.697 | 0.517 | 0.513 | 216/309/94 | +| vistas curb-cut — **same-env control** (A40) | 384×384 | 0.411 | 0.694 | 0.516 | 0.510 | 215/308/95 | +| vistas curb-cut — **parity** (A40) | **1024×1024** | 0.383 | **0.884** | **0.534** | **0.649** | 274/442/36 | + +At conf ≥ 0.30: control **0.419 / 0.694 / 0.522** (published: 0.419 / 0.697 / 0.524), parity +**0.384 / 0.884 / 0.536**. Note the threshold barely bites at parity — it removes 3 false +positives against 10 at 384 — because the higher-resolution masks are more confident. + +**The env control was not in the original plan and it earns its place.** The published run was on +Jon's RTX 3070 on an older `transformers`; makelab2 is a major version on. Since the +`transformers` version is *not* in the detection signature — a hazard this document already flags +— parity-vs-published would have differed in **two** things. Re-running 384 in the parity env +separates them: it lands within **one detection out of 523** of the published run (215/308/95 vs +216/309/94, F1 0.516 vs 0.517). So the 4.x→5.15 jump is benign for this checkpoint, the residual +is fp16 kernel nondeterminism rather than a version break, and **the whole parity delta is +attributable to input size.** That also retires the "an upgrade could have changed every mask +under an unchanged cache key" worry for this arm, as a measurement rather than an assurance. + +**The decision rule returns "stands", and it is not close.** Against the same-env control, parity +moves F1 **0.516 → 0.534, +0.018** — about a third of the 0.05 bar. RampNet's lead goes 0.339 → +**0.321**. One split, and no case for costing more. + +**But the mechanism underneath that flat F1 is the actual finding, and it is not the one the +caveat predicted.** The handicap was real and it was large — it was just almost entirely a +*recall* handicap: + +- **Recall 0.694 → 0.884 (+0.190).** Misses fall from 95 to **36**, a 62% reduction. At parity + this arm **out-recalls RampNet** (0.884 vs 0.768) while remaining a model that has never seen a + curb ramp label of ours. +- **AP 0.510 → 0.649 (+0.139)**, a 27% relative gain — the ranking, not just the operating point, + is substantially better. +- **Precision 0.411 → 0.383 (−0.028)**: slightly *worse*. False positives rise 308 → 442, faster + than true positives rise 215 → 274. + +So resolution was buying recall the whole time, and F1 stayed flat only because precision is the +binding constraint and resolution does nothing for it. **That sharpens rather than softens the +conclusion #126 was built to test.** The original framing — *the concept is findable, the +discrimination is not* — was stated against OWLv2 and Grounding DINO; it now holds against the +supervised arm at equal input too, and it is no longer confounded with how many pixels the model +was given. Vistas' curb-cut labels **find** curb ramps on deployment panoramas better than our own +model does; what they cannot do is tell a curb ramp from the things that look like one, which is +exactly the failure the RampNet paper predicted when it rejected this class as a supervision +source for being *"overly broad"*. + +**What this changes above.** Two claims in this section were stated at 384 and do not survive +parity unqualified: + +- The AP comparison against the YOLO baseline said richmond AP **0.748** (`y11x_pano_h200`), + **0.724** (`y11l_pano`) and **0.536** (`y26_pano`) are *"all above 0.513"*. At parity the arm is + at **0.649**, so **`y26_pano` no longer clears it** — somebody else's labels for a neighbouring + class, at equal input, beat one of our own three YOLO arms on AP. The two stronger YOLO arms + still lead, so the sentence's conclusion holds; its arithmetic does not. +- "Best *zero-training* model on AP" is unchanged and strengthened: 0.649 against OWLv2's 0.104. + +**Caveats that travel with these numbers.** `min_area_px=16` is inert at 384 but sits exactly at +the smallest achievable blob at 1024 (see above), so the two rows do not share that setting's +meaning even though they share its value. The parity detections are **deliberately not +published**: `--vistas-input-size` does not change the arm's label, so exporting them would +overwrite `mask2former-vistas-curb-cut__richmond.json` — publishing them needs a distinct +published name (the `--publish-as` pattern from #123), which was out of scope here. And this is +still **richmond only**. + +**The follow-up this result argues for is not more splits.** It is complementarity: at parity +this arm misses **36** ramps where RampNet misses **72**, so the question worth asking is how much +of RampNet's miss set a free, zero-training model already covers. That is a recall-first question +and it bears directly on the north star; it is scoring-side work on detections that are already +cached, and it is not costed here. + +##### Reproducing it + +```bash +# parity (the measurement) +python scripts/model_comparison/compare.py benchmark/richmond \ + --models rampnet,vistas:curb-cut --vistas-input-size 1024 1024 + +# same-env control (the attribution) -- default 384, no override +python scripts/model_comparison/compare.py benchmark/richmond --models vistas:curb-cut + +# either, re-scored at the deployment threshold (free, reads the cache) +python scripts/model_comparison/compare.py benchmark/richmond \ + --models vistas:curb-cut --vistas-input-size 1024 1024 --op-threshold 0.30 +``` + +Cost, measured rather than guessed, because the estimate going in was 3–4× and it was wrong in +the cheap direction: **the full 124-pano parity run takes 3m38s** on one A40, and the GPU forward +goes 0.078 s → 0.092 s per view, only **1.17×**. Swin's windowed attention scales far better than +pixel count, and the documented "2.3 s/view" is dominated by reprojection and CPU work, not the +encoder. Peak GPU memory is 1.64 GB. Verified before the run rather than assumed: the override +reaches the model — `pixel_values` (1, 3, 384, 384) → (1, 3, 1024, 1024) and mask logits +(1, 100, 96, 96) → (1, 100, 256, 256) — which a silently no-opping `processor.size` assignment on +a new major version would not have done, and which would have made "parity" a second 384 run +wearing a different cache key. + Two mechanisms, both measured rather than assumed: - **The `curb-cut+curb` union is a clean negative result.** It was run to test whether recall From 20b767eb6190978862e83a42552aa4759f39cb12 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 15:48:42 -0700 Subject: [PATCH 02/10] complementarity.py: any provider, not just Gemini (#126) The #35 gate script was hardcoded to build_detector("gemini", ...), so the parity Vistas arm could not be run through it at all. Generalized to a model spec (provider or provider:model_id), with the documented positional form preserved -- a bare non-provider token still reads as a Gemini model id, which is how three call sites in docs/model_comparison.md spell it. Verified against the committed paterson numbers: 36 of 124 misses recovered (29%), 88 found by no model, oracle-union 0.777, all reproduced exactly. Provider defaults now come from roster.PROVIDER_DEFAULTS via the same shim shape fp_taxonomy and null_recall use, so the reconstructed cache key cannot drift from the one compare.py wrote under -- a wrong default there does not crash, it silently misses every entry and reports zero detections. Also added the --vistas-* args to null_recall.py. build_detector reads them off the namespace with getattr, so without them the vistas arm there was silently the published 384x384 one even when the run being analysed was at parity -- a wrong-arm read that looks like a valid answer. And printed the counterweight to the oracle-union number: the FP bill a naive union actually pays, with the resulting precision/F1 against rampnet alone. The oracle ceiling assumes a combiner that keeps every right call, which does not exist; on paterson/gemini-pro the union genuinely wins (0.821 vs 0.805), which is the check that the line is not hardcoded to one verdict. Co-Authored-By: Claude Opus 5 --- scripts/analysis/complementarity.py | 257 ++++++++++++++++++++-------- scripts/analysis/null_recall.py | 13 ++ 2 files changed, 203 insertions(+), 67 deletions(-) diff --git a/scripts/analysis/complementarity.py b/scripts/analysis/complementarity.py index 4525d662..f3b4db6a 100644 --- a/scripts/analysis/complementarity.py +++ b/scripts/analysis/complementarity.py @@ -1,40 +1,61 @@ -"""RampNet vs Gemini-3.6-flash complementarity on richmond (read-only, from cache). +"""Do RampNet and a challenger miss *different* ramps? (issue #35 gate) -Answers issue #35's decision gate: do the two models miss *different* ramps? -For each GT ramp on recall-eligible panos, record whether RampNet found it, Gemini -found it, both, or neither -> oracle-union recall + the RampNet-miss n Gemini-hit set. +For each GT ramp on a recall-eligible pano, record whether RampNet found it, the +challenger found it, both, or neither -> oracle-union recall, and the set that +matters: **RampNet-miss n challenger-hit**. + +Read-only: RampNet's side comes from the bundle's committed detections and the +challenger's from ``.model_cache``, so this never runs a model or spends anything. + +**The oracle-union recall is a ceiling, not a proposal.** It assumes you could +keep every right call and discard every wrong one, which no combiner can do. The +FP arithmetic printed at the end is the counterweight, and for a low-precision +challenger it is usually decisive -- see the union precision line. Pair this with +``null_recall.py`` before believing any union number from a model that emits many +boxes per pano: at high density a share of "hits" are what the match radius hands +out for free, and that share inflates the complementary set too. + +Usage -- the positional form is the one three call sites in +``docs/model_comparison.md`` use, and it still means what it did: + + python scripts/analysis/complementarity.py # gemini-3.6-flash, richmond + python scripts/analysis/complementarity.py gemini-3.1-pro-preview paterson + python scripts/analysis/complementarity.py vistas:curb-cut richmond \ + --vistas-input-size 1024 1024 + +The first positional is a **model spec** (``provider`` or ``provider:model_id``, +as ``compare.py --models`` takes them). A bare token that is not a known provider +is read as a Gemini model id, which is how the #35 gate was invoked before this +script grew past one provider. """ -import os as _os, sys as _sys -REPO = _os.path.dirname(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__)))) -OUT = _os.environ.get("RAMPNET_ANALYSIS_OUT", _os.path.join(REPO, "analysis_out")) -_os.makedirs(OUT, exist_ok=True) -DA3_SRC = _os.environ.get("DA3_SRC") # path to Depth-Anything-3/src (see README) -import os, sys +import argparse +import os +import sys + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.insert(0, REPO) sys.path.insert(0, os.path.join(REPO, "scripts", "model_comparison")) -from rampnet.detection_eval import ( - build_ground_truth, score_pano, radius_sq_for, PANO_SCALE_X, PANO_SCALE_Y, _xy, prediction_confidence) -from compare import load_bundle, DetectionCache, cache_key -from detectors import build_detector +from rampnet import roster # noqa: E402 +from rampnet.detection_eval import ( # noqa: E402 + build_ground_truth, score_pano, radius_sq_for, PANO_SCALE_X, PANO_SCALE_Y, + _xy, prediction_confidence) +from compare import load_bundle, DetectionCache, cache_key # noqa: E402 +from detectors import build_detector, parse_model_spec, PROVIDERS # noqa: E402 -MODEL = sys.argv[1] if len(sys.argv) > 1 else "gemini-3.6-flash" -BUNDLE = sys.argv[2] if len(sys.argv) > 2 else "richmond" # benchmark/ name -class Args: - gemini_model = MODEL; qwen_model = "Qwen/Qwen3-VL"; tiling = "perspective" - -RSQ = radius_sq_for() - -def matched_gt(preds, gt_points): +def matched_gt(preds, gt_points, radius_sq): """Greedy 1:1 match (mirrors score_pano); return the set of GT indices covered.""" confs = [prediction_confidence(p) for p in preds] - order = (sorted(range(len(preds)), key=lambda i: confs[i] if confs[i] is not None else float("-inf"), - reverse=True) if any(c is not None for c in confs) else range(len(preds))) + order = (sorted(range(len(preds)), + key=lambda i: confs[i] if confs[i] is not None else float("-inf"), + reverse=True) + if any(c is not None for c in confs) else range(len(preds))) claimed, hit = [False] * len(gt_points), set() for i in order: - pxn, pyn = _xy(preds[i]); px, py = pxn * PANO_SCALE_X, pyn * PANO_SCALE_Y - best_k, best = -1, RSQ + pxn, pyn = _xy(preds[i]) + px, py = pxn * PANO_SCALE_X, pyn * PANO_SCALE_Y + best_k, best = -1, radius_sq for k, (gx, gy) in enumerate(gt_points): if claimed[k]: continue @@ -42,46 +63,148 @@ def matched_gt(preds, gt_points): if d < best: best, best_k = d, k if best_k >= 0: - claimed[best_k] = True; hit.add(best_k) + claimed[best_k] = True + hit.add(best_k) return hit -records, verdicts, _ = load_bundle(os.path.join(REPO, "benchmark", BUNDLE)) -label, gem = build_detector("gemini", MODEL, records, Args()) -sig, cache = gem.signature(), DetectionCache(os.path.join(REPO, ".model_cache")) - -N = both = r_only = g_only = neither = 0 -r_fp = g_fp = 0 -panos = missing = 0 -for pid, entry in verdicts.items(): - gt = build_ground_truth(records[pid]["detections"], entry["dets"], entry["missed"], entry["no_missed"]) - if not gt.fn_confirmed: - continue - gp = cache.get(cache_key(label, sig, BUNDLE, pid)) - if gp is None: - missing += 1; continue - rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) for d in records[pid]["detections"]] - mr, mg = matched_gt(rp, gt.gt_points), matched_gt(gp, gt.gt_points) - for i in range(len(gt.gt_points)): - r, g = i in mr, i in mg - both += r and g; r_only += r and not g; g_only += g and not r; neither += not r and not g - N += len(gt.gt_points) - r_fp += score_pano(rp, gt).fp - g_fp += score_pano(gp, gt).fp - panos += 1 - -r_tp, g_tp, union = both + r_only, both + g_only, both + r_only + g_only -rampnet_misses = g_only + neither -print(f"{BUNDLE} complementarity — RampNet vs {MODEL} ({panos} recall-eligible panos, {N} GT ramps" - + (f"; {missing} panos missing from cache" if missing else "") + ")\n") -print(f" RampNet recall {r_tp/N:.3f} ({r_tp}/{N})") -print(f" Gemini recall {g_tp/N:.3f} ({g_tp}/{N})") -print(f" ORACLE-UNION recall{union/N:.3f} ({union}/{N}) <- ceiling if you could keep every right call") -print() -print(f" found by BOTH {both:4d} ({both/N:.1%})") -print(f" RampNet ONLY {r_only:4d} ({r_only/N:.1%})") -print(f" Gemini ONLY {g_only:4d} ({g_only/N:.1%}) <- complementary gain (RampNet-miss n Gemini-hit)") -print(f" found by NEITHER {neither:4d} ({neither/N:.1%}) <- hard misses, no model helps") -print() -print(f" Union recall lift over RampNet: +{(union - r_tp)/N:.3f} ({g_only} ramps)") -print(f" Of RampNet's {rampnet_misses} misses, Gemini recovers {g_only} ({g_only/rampnet_misses:.0%}); {neither} nobody finds") -print(f" FP cost on these panos: RampNet {r_fp} | Gemini {g_fp} (a naive union pays ~both)") + +def model_spec(token): + """``provider``/``provider:model_id``, or a legacy bare Gemini model id.""" + provider, model_id = parse_model_spec(token) + if provider in PROVIDERS: + return provider, model_id + # Legacy positional form: a bare model id meant gemini. Keep it working -- + # reading it as a provider would raise on strings that used to be valid. + return "gemini", token + + +def compare_args(args): + """A namespace matching ``compare.py``'s parser defaults, so the cache key this + script reconstructs is the one ``compare.py`` wrote under. + + Provider defaults come from ``rampnet.roster.PROVIDER_DEFAULTS`` -- one + definition, the same source ``fp_taxonomy``'s shim and ``null_recall`` read -- + because a wrong default here does not crash, it silently misses every cache + entry and reports zero detections. The deviation-only knobs + (``vistas_input_size``, ``vistas_revision``) are threaded through from the CLI: + they enter the signature ONLY when set, so leaving them off reproduces the + published arm and setting one addresses a distinct cache entry. + """ + import argparse as _a + ns = _a.Namespace() + for k, v in dict( + roster.PROVIDER_DEFAULTS, + owlv2_query=None, gdino_query=None, + gdino_text_threshold=None, score_threshold=None, + yolo_model=None, tiling=args.tiling, + radius=args.radius, op_threshold=0.0, limit=None, + cache_dir=args.cache_dir, no_cache=False, + vistas_input_size=args.vistas_input_size, + vistas_revision=args.vistas_revision).items(): + setattr(ns, k, v) + return ns + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("model", nargs="?", default=roster.PROVIDER_DEFAULTS["gemini_model"], + help="Model spec (provider or provider:model_id). A bare " + "non-provider token is read as a Gemini model id.") + ap.add_argument("split", nargs="?", default="richmond", + help="Benchmark split name (default richmond).") + ap.add_argument("--cache-dir", default=os.path.join(REPO, ".model_cache")) + ap.add_argument("--radius", type=float, default=0.022) + ap.add_argument("--tiling", choices=["perspective", "none"], default="perspective") + ap.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"), + default=None, + help="Override what the Vistas checkpoint actually sees. In the " + "signature only when set, so this addresses a DIFFERENT " + "cache entry than the published 384x384 arm (#126).") + ap.add_argument("--vistas-revision", default=None) + args = ap.parse_args() + + provider, model_id = model_spec(args.model) + bundle = os.path.join(REPO, "benchmark", args.split) + records, verdicts, _ = load_bundle(bundle) + if verdicts is None: + sys.exit(f"{bundle}: no verdicts.json -- this gate needs a reviewed split.") + label, detector = build_detector(provider, model_id, records, compare_args(args)) + sig = detector.signature() + cache = DetectionCache(args.cache_dir) + radius_sq = radius_sq_for(args.radius) + + n = both = r_only = c_only = neither = 0 + r_fp = c_fp = 0 + panos = missing = 0 + for pid, entry in verdicts.items(): + gt = build_ground_truth(records[pid]["detections"], entry["dets"], + entry["missed"], entry["no_missed"]) + if not gt.fn_confirmed: + continue + cp = cache.get(cache_key(label, sig, args.split, pid)) + if cp is None: + missing += 1 + continue + rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) + for d in records[pid]["detections"]] + mr, mc = matched_gt(rp, gt.gt_points, radius_sq), matched_gt(cp, gt.gt_points, radius_sq) + for i in range(len(gt.gt_points)): + r, c = i in mr, i in mc + both += r and c + r_only += r and not c + c_only += c and not r + neither += not r and not c + n += len(gt.gt_points) + r_fp += score_pano(rp, gt).fp + c_fp += score_pano(cp, gt).fp + panos += 1 + + if not n: + sys.exit("No recall-eligible panos with cached detections -- nothing to compare. " + "Run compare.py for this model/split first (and pass the SAME " + "--vistas-input-size, which is part of the cache key).") + + r_tp, c_tp, union = both + r_only, both + c_only, both + r_only + c_only + r_miss = c_only + neither + print(f"{args.split} complementarity — rampnet vs {label} " + f"({panos} recall-eligible panos, {n} GT ramps" + + (f"; {missing} panos missing from cache" if missing else "") + ")\n") + print(f" rampnet recall {r_tp / n:.3f} ({r_tp}/{n})") + print(f" {label[:18]:18s} recall {c_tp / n:.3f} ({c_tp}/{n})") + print(f" ORACLE-UNION recall {union / n:.3f} ({union}/{n}) " + f"<- ceiling if you could keep every right call") + print() + print(f" found by BOTH {both:4d} ({both / n:.1%})") + print(f" rampnet ONLY {r_only:4d} ({r_only / n:.1%})") + print(f" {label[:14]:14s} ONLY {c_only:4d} ({c_only / n:.1%}) " + f"<- complementary gain (rampnet-miss n challenger-hit)") + print(f" found by NEITHER {neither:4d} ({neither / n:.1%}) " + f"<- hard misses, no model helps") + print() + print(f" Union recall lift over rampnet: +{(union - r_tp) / n:.3f} ({c_only} ramps)") + if r_miss: + print(f" Of rampnet's {r_miss} misses, {label} recovers {c_only} " + f"({c_only / r_miss:.0%}); {neither} nobody finds") + + # The counterweight to the oracle number. A naive union keeps every box from + # both models, so it pays both FP bills; co-located FPs would dedup, which is + # why this is an upper bound on the cost and therefore a LOWER bound on the + # union's precision. + u_p = union / (union + r_fp + c_fp) if union + r_fp + c_fp else 0.0 + u_r = union / n + u_f1 = 2 * u_p * u_r / (u_p + u_r) if u_p + u_r else 0.0 + r_p = r_tp / (r_tp + r_fp) if r_tp + r_fp else 0.0 + r_f1 = 2 * r_p * (r_tp / n) / (r_p + r_tp / n) if r_p + r_tp / n else 0.0 + print() + print(f" FP cost on these panos: rampnet {r_fp} | {label} {c_fp}" + f" (a naive union pays ~both)") + print(f" rampnet alone: P {r_p:.3f} R {r_tp / n:.3f} F1 {r_f1:.3f}") + print(f" NAIVE UNION: P {u_p:.3f} R {u_r:.3f} F1 {u_f1:.3f}" + f" <- precision is a lower bound (no FP dedup)") + print(f" => a naive union {'BEATS' if u_f1 > r_f1 else 'LOSES TO'} rampnet alone " + f"on F1 ({u_f1:.3f} vs {r_f1:.3f})") + + +if __name__ == "__main__": + main() diff --git a/scripts/analysis/null_recall.py b/scripts/analysis/null_recall.py index ee8a5587..602f4ddc 100644 --- a/scripts/analysis/null_recall.py +++ b/scripts/analysis/null_recall.py @@ -139,6 +139,19 @@ def main(): ap.add_argument("--molmo-model", default=_D["molmo_model"]) ap.add_argument("--molmo-coord-scale", choices=["auto", "100", "1000"], default=_D["molmo_coord_scale"]) + ap.add_argument("--vistas-class-set", default=_D["vistas_class_set"]) + ap.add_argument("--vistas-model", default=_D["vistas_model"]) + ap.add_argument("--vistas-min-area-px", type=int, + default=_D["vistas_min_area_px"]) + ap.add_argument("--vistas-dtype", choices=["float16", "float32"], + default=_D["vistas_dtype"]) + # Deviation-only, exactly as in compare.py: absent from the signature + # unless set. Without these two the vistas arm here would silently be the + # published 384x384 one even when the run being analysed was at parity -- + # a wrong-arm read that looks like a valid answer (#126). + ap.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"), + default=None) + ap.add_argument("--vistas-revision", default=None) args = ap.parse_args() records, verdicts, panos_dir = compare.load_bundle(args.bundle) From 2d79bb063c82b9a28a3f993e2f43c6617a205693 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 15:56:19 -0700 Subject: [PATCH 03/10] complementarity.py: null the complementary set, do not extrapolate one (#126) The headline of a complementarity run is "the challenger recovers N of rampnet%s M misses". At any real box density some of that N is the match radius handing out hits for free, and the repo doctrine is to discount every recall-ceiling and union-oracle claim before believing it. null_recall.py already measures that, but over the WHOLE split. Applying its number to the miss subset assumes the coincidence rate is uniform across GT, and it need not be: rampnet%s misses are a biased sample (far-field, adjacent pairs) and those are exactly where box density differs. So this measures the null on the subset the claim is about, using null_recall%s own cyclic-shift construction -- pano A%s missed GT against pano B%s predictions, averaged over every non-identity shift, so box count and clustering stay real and only the pairing is wrong. On the paterson/gemini-pro regression case the committed numbers are unchanged (36 of 124, 88 found by no model) and the new line reads: null 0.081, so ~10 of the 36 are coincidence and ~26 attributable. Co-Authored-By: Claude Opus 5 --- scripts/analysis/complementarity.py | 39 +++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/scripts/analysis/complementarity.py b/scripts/analysis/complementarity.py index f3b4db6a..ab7591a1 100644 --- a/scripts/analysis/complementarity.py +++ b/scripts/analysis/complementarity.py @@ -68,6 +68,37 @@ def matched_gt(preds, gt_points, radius_sq): return hit +def complementary_null(rows, radius_sq): + """How many of rampnet's misses would the challenger "recover" by coincidence? + + Same null as ``null_recall.py`` -- score pano A's ground truth against pano + B's predictions, averaged over every non-identity cyclic shift -- but + restricted to the GT rampnet MISSED, because that is the subset the + complementary-gain headline is about. Both sides stay real model output on + real imagery, so box count and spatial clustering are preserved; only the + pairing is wrong, so every match is chance. + + Applying the whole-split null from ``null_recall.py`` to this subset instead + would assume the coincidence rate is uniform across GT. It need not be: + rampnet's misses are a biased sample (far-field, adjacent pairs), and those + are exactly the places box density differs. Hence measuring it here. + + Returns (mean, max) as a fraction of the missed GT. + """ + preds = [p for p, _ in rows] + missed = [m for _, m in rows] + n = len(rows) + total = sum(len(m) for m in missed) + if not total or n < 2: + return 0.0, 0.0 + shifted = [] + for k in range(1, n): + hit = sum(len(matched_gt(preds[(i + k) % n], missed[i], radius_sq)) + for i in range(n)) + shifted.append(hit / total) + return sum(shifted) / len(shifted), max(shifted) + + def model_spec(token): """``provider``/``provider:model_id``, or a legacy bare Gemini model id.""" provider, model_id = parse_model_spec(token) @@ -137,6 +168,8 @@ def main(): n = both = r_only = c_only = neither = 0 r_fp = c_fp = 0 panos = missing = 0 + # (challenger preds, GT points rampnet MISSED) per pano, for the null below + shift_rows = [] for pid, entry in verdicts.items(): gt = build_ground_truth(records[pid]["detections"], entry["dets"], entry["missed"], entry["no_missed"]) @@ -158,6 +191,7 @@ def main(): n += len(gt.gt_points) r_fp += score_pano(rp, gt).fp c_fp += score_pano(cp, gt).fp + shift_rows.append((cp, [g for i, g in enumerate(gt.gt_points) if i not in mr])) panos += 1 if not n: @@ -186,6 +220,11 @@ def main(): if r_miss: print(f" Of rampnet's {r_miss} misses, {label} recovers {c_only} " f"({c_only / r_miss:.0%}); {neither} nobody finds") + mean_null, max_null = complementary_null(shift_rows, radius_sq) + exp = mean_null * r_miss + print(f" null (same boxes, wrong pano): {mean_null:.3f} " + f"=> ~{exp:.0f} of those {c_only} are coincidence, " + f"~{c_only - exp:.0f} attributable (worst shift {max_null:.3f})") # The counterweight to the oracle number. A naive union keeps every box from # both models, so it pays both FP bills; co-located FPs would dedup, which is From 9d6f8f811feef73a5acd428604b19475a246c9db Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:00:05 -0700 Subject: [PATCH 04/10] Complementarity at parity: 61% of RampNet-misses recoverable, union still loses (#126) Runs the #35 gate on the parity arm, plus the 384 arm as the control. Answers the recall-first question the parity result raised, and answers it against chance rather than at face value. Discounted for the null, a free zero-training model finds ~44 of the 72 ramps RampNet misses on richmond (61%). Raw is 54 of 72 (75%); the null on that subset is 0.143. The recall is real detection, not density -- at 6.2 boxes/pano the arm above-chances 0.864, higher than RampNet own 0.754, nothing like OWLv2 0.733 at 74 boxes/pano. The control is the interesting half. Parity moved F1 by +0.018 and was correctly judged not to change the ranking -- but it nearly TRIPLED the attributable complementary gain (~15 -> ~44 ramps) and shrank the found-by-nobody core from 51 to 18. A flat headline metric hid a large change in the structure underneath it, and only the complementarity read surfaced it. A naive union stays dead and it is not close: F1 0.555 against RampNet 0.855. The 54 ramps arrive with 442 false positives, ~8.2 FP per recovered ramp against the 9 FP RampNet pays for 238 TP. Recorded the oracle-union 0.942 as the ceiling it is, with the real union arithmetic beside it so the two cannot be confused. What is left open is a gated cascade -- this arm candidates as a spatial prior to locally relax RampNet threshold -- which is decidable but NOT run: #131 measured most silent misses as adjacent-tail or faint, but nobody has checked whether sub-threshold signal exists at these 54 locations specifically. Co-Authored-By: Claude Opus 5 --- docs/model_comparison.md | 66 +++++++++++++++++++++++++++++++++++--- scripts/analysis/README.md | 2 +- 2 files changed, 62 insertions(+), 6 deletions(-) diff --git a/docs/model_comparison.md b/docs/model_comparison.md index 1de84857..7e32dca6 100644 --- a/docs/model_comparison.md +++ b/docs/model_comparison.md @@ -1367,11 +1367,67 @@ overwrite `mask2former-vistas-curb-cut__richmond.json` — publishing them needs published name (the `--publish-as` pattern from #123), which was out of scope here. And this is still **richmond only**. -**The follow-up this result argues for is not more splits.** It is complementarity: at parity -this arm misses **36** ramps where RampNet misses **72**, so the question worth asking is how much -of RampNet's miss set a free, zero-training model already covers. That is a recall-first question -and it bears directly on the north star; it is scoring-side work on detections that are already -cached, and it is not costed here. +#### Complementarity: 61% of RampNet's misses are recoverable, and a union still loses + +Parity raised the obvious recall-first question — this arm misses 36 ramps where RampNet misses +72, so how much of *RampNet's* miss set does a free, zero-training model already cover? Run +through the #35 gate (`scripts/analysis/complementarity.py`, generalized past its Gemini-only +form for this), on the same cached detections, scoring-side only: + +| | vistas @384 | **vistas @1024 (parity)** | +|---|---:|---:| +| found by BOTH | 194 | 220 | +| rampnet ONLY | 44 | 18 | +| **challenger ONLY** (rampnet-miss ∩ hit) | 21 | **54** | +| found by NEITHER | 51 | **18** | +| of rampnet's 72 misses, recovered | 21 (29%) | **54 (75%)** | +| null on that subset (same boxes, wrong pano) | 0.090 | 0.143 | +| **attributable after the null** | **~15** | **~44** | +| oracle-union recall | 0.835 | **0.942** | +| boxes/pano · above chance (`null_recall.py`) | 4.5 · 0.657 | 6.2 · **0.864** | + +**Discounted for chance, a free zero-training model finds ~44 of the 72 ramps RampNet misses — +61%.** The null here is measured on the miss subset rather than extrapolated from the split-wide +one, because RampNet's misses are a biased sample (far-field, adjacent pairs) and that is exactly +where density differs; it lands at 0.143 against the split-wide 0.145, so in this case the +extrapolation would have been fair. And the recall is real detection, not density: at 6.2 +boxes/pano the arm's **above-chance is 0.864, higher than RampNet's own 0.754** — nothing like +OWLv2's 0.733 null at 74 boxes/pano. + +**The resolution fix mattered far more here than the headline suggested.** Parity moved F1 by ++0.018 and was correctly judged not to change the ranking — but it nearly **tripled** the +attributable complementary gain (~15 → ~44 ramps) and shrank the found-by-nobody core from 51 to +**18**, 5.8% of GT. That core is much smaller than paterson's 88 (22%) or gainesville's 55 (20%), +though those are different splits against a different challenger, so read it as suggestive rather +than a like-for-like. The general lesson is worth keeping: **a flat headline metric hid a large +change in the structure underneath it**, and only the complementarity read surfaced it. + +**A naive union is nonetheless dead, and it is not close.** The oracle-union recall of 0.942 is a +*ceiling* — it assumes a combiner that keeps every right call and discards every wrong one, which +does not exist. What a real union pays is both FP bills: + +| | P | R | F1 | +|---|---:|---:|---:| +| rampnet alone | 0.964 | 0.768 | **0.855** | +| naive union with vistas @1024 | 0.393 | 0.942 | 0.555 | +| naive union with vistas @384 | 0.450 | 0.835 | 0.585 | + +The economics are the whole story: those 54 ramps arrive with 442 false positives, about **8.2 FPs +per recovered ramp**, against the 9 FPs RampNet currently pays for 238 true positives. So +ensembling by union is not a close call at any operating point on this arm's PR curve. + +**What that leaves is a gated cascade, and it is a real open question rather than a plan.** The +useful form is not "take both models' boxes" but "use this arm's candidates as a *spatial prior* +to locally relax RampNet's threshold", which would keep RampNet's precision and buy back some of +the 54. Whether it can work is empirical and decidable: #131 measured RampNet's silent misses as +8% absent / 62% adjacent-tail / 30% faint, so most misses *do* have sub-threshold heatmap signal — +but nobody has checked whether that holds at these 54 locations specifically. If the signal is +absent there, the miss is genuine and the cascade has nothing to work with. `silent_activation.py` +is the instrument. **Not run, not costed here.** + +**Caveats.** richmond only, one imagery tier. The 442 FPs are not free even in a recall-first +framing — at 3.6 FP/pano against RampNet's 0.07 they are a ~50× review burden, so "FPs are cheap" +is a claim about the labeling workflow that would need its own justification at this ratio. ##### Reproducing it diff --git a/scripts/analysis/README.md b/scripts/analysis/README.md index 2ca0526d..155926ed 100644 --- a/scripts/analysis/README.md +++ b/scripts/analysis/README.md @@ -26,7 +26,7 @@ checkout. | script | GPU | what it answers | |---|---|---| | `miss_analysis.py` | no | Are misses localization near-misses or blind? Are they hard (a VLM also missed) or RampNet-specific? | -| `complementarity.py [model] [split]` | no | Oracle-union recall + the RampNet-miss ∩ VLM-hit set (issue #35 gate). Reads cached VLM detections from `.model_cache`; split defaults to richmond. | +| `complementarity.py [model] [split]` | no | Oracle-union recall + the RampNet-miss ∩ challenger-hit set, with a chance null on that subset and the FP bill a naive union would actually pay (issue #35 gate). Takes any model spec (`provider` or `provider:model_id`); a bare non-provider token is read as a Gemini model id. Reads cached detections from `.model_cache`; split defaults to richmond. Pass the same `--vistas-input-size` the run used — it is part of the cache key. | | `precision_by_distance.py` | no | Is precision worse at distance — i.e. is culling far detections worth it? (No.) | | `threshold_sweep.py` | **yes** | Re-runs inference on all benchmark panos and sweeps `threshold_abs` × `min_distance`. | | `peak_nms_check.py` | no | Would suppressing peaks closer than the match radius help? (No — 6 of the 10 within-R pairs in the reviewed records are real ramp pairs; issue #62.) Reads all seven splits' committed records, no panos needed. | From 448512d654b6a0d39ae6310c0a9116f6a258a8eb Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:10:09 -0700 Subject: [PATCH 05/10] cascade_gate.py: is the gated cascade even possible? (#126) The complementarity result rules out a union but leaves a gated cascade -- the challenger candidates as a spatial prior to locally relax RampNet threshold. That has one precondition and it is measurable without building anything: RampNet must already produce sub-threshold response AT those ramps. If the heatmap is flat there the miss is genuine absence and no prior can raise it. Partitions every GT ramp into the four complementarity cells and reads the heatmap at each, with #46 Phase 1 instrument imported rather than reimplemented -- site_profile, null_percentile, nearest_peak, class_of and its ABSENT_MAX/PEAK_FLOOR cutoffs -- so the numbers are directly comparable to that phase 8% absent / 62% adjacent-tail / 30% faint decomposition, and a fix to the probe fixes both analyses. The three-way class is the point, not the raw activation. faint_local with the argmax at the site is signal a prior could raise. tail is an ADJACENT mode reaching in, so relaxing a threshold there fires on the neighbour rather than the ramp -- that argues for sigma/NMS work, not for a prior, even though it reads as "signal present" if you only look at act. Read pre-registered on #126 before running. Nulls computed only for the two RampNet-miss cells; the hit cells are high by construction and are the positive control. Co-Authored-By: Claude Opus 5 --- .gitignore | 2 + scripts/analysis/cascade_gate.py | 263 +++++++++++++++++++++++++++++++ 2 files changed, 265 insertions(+) create mode 100644 scripts/analysis/cascade_gate.py diff --git a/.gitignore b/.gitignore index 37fb92d5..26a53f29 100644 --- a/.gitignore +++ b/.gitignore @@ -90,6 +90,8 @@ analysis_out/op/* !analysis_out/crop_window_eval_*.json !analysis_out/farfield_forensics.json !analysis_out/silent_activation.json +# The #126 cascade go/no-go: rampnet's heatmap at the ramps a challenger recovers. +!analysis_out/cascade_gate.json # Generated tagging pages live next to their (committed) galleries; regenerate with # scripts/analysis/make_tagger.py rather than tracking a build artifact. diff --git a/scripts/analysis/cascade_gate.py b/scripts/analysis/cascade_gate.py new file mode 100644 index 00000000..f165af12 --- /dev/null +++ b/scripts/analysis/cascade_gate.py @@ -0,0 +1,263 @@ +"""Is a gated cascade live? Does RampNet already *see* the ramps a challenger recovers? (#126) + +The complementarity gate (``complementarity.py``) says a Vistas-supervised segmenter at +input parity finds 54 of the 72 richmond ramps RampNet misses, ~44 of them after the +chance null. A naive union is nonetheless dead — those 54 arrive with 442 false +positives, ~8.2 FP per recovered ramp, and the union scores F1 0.555 against RampNet's +0.855. + +What that leaves is a **gated cascade**: use the challenger's candidates as a spatial +prior and locally relax RampNet's threshold, keeping RampNet's precision everywhere +else. That has one precondition, and it is measurable without building anything — +**RampNet must already produce sub-threshold response at those ramps.** If the heatmap +is flat there, the miss is genuine absence and no prior can raise what is not there. + +So: partition every GT ramp into the four complementarity cells, and read RampNet's +heatmap at each. + + cell rampnet challenger role + both hit hit positive control + rampnet_only hit miss positive control + challenger_only MISS hit the recoverable set -- the question + neither MISS miss hard core + +**The instrument is #46 Phase 1's, imported rather than reimplemented** — +``site_profile``, ``null_percentile``, ``nearest_peak``, ``class_of`` and its two +cutoffs (``ABSENT_MAX`` 0.01, ``PEAK_FLOOR`` 0.05) all come from +``silent_activation.py``. That is deliberate: it makes these numbers directly +comparable to that phase's 8% absent / 62% adjacent-tail / 30% faint decomposition, +and it means a fix to the probe fixes both analyses. + +**Three outcomes, and the middle one is not the interesting one.** ``faint_local`` with +the in-window argmax *at* the site is signal a prior could raise. ``absent`` is nothing +to raise. ``tail`` — the in-window maximum at or above the peak floor — is an adjacent +mode reaching in, and for a *missed* ramp it means relaxing a threshold there would fire +on the neighbour, not on the ramp. ``tail`` therefore argues for sigma/NMS work (#46's +adjacent-pair merge), **not** for a threshold prior, even though it looks like "signal +is present" if you only read ``act``. + +**Sub-threshold signal is necessary, not sufficient.** A positive here says the cascade +is not ruled out and is worth costing; it does not demonstrate one works. Any realisable +gain is bounded above by the ~44 attributable ramps, not the raw 54. + +Inputs: the native-resolution panoramas at ``benchmark//panos/`` (git-ignored, +published as ``projectsidewalk/rampnet-benchmark``) — ``--panos-root`` points at +whichever checkout holds them, since a worktree will not. The challenger's detections +must already be in ``--cache-dir``; **pass the same ``--vistas-input-size`` the run +used, it is part of the cache key.** A GPU: ~124 panos, one forward each. + + python scripts/analysis/cascade_gate.py --panos-root /path/to/RampNet \\ + --model vistas:curb-cut --vistas-input-size 1024 1024 \\ + --json-out analysis_out/cascade_gate.json +""" +import argparse +import json +import os +import random +import sys + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +OUT = os.environ.get("RAMPNET_ANALYSIS_OUT", os.path.join(REPO, "analysis_out")) +sys.path.insert(0, REPO) +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, os.path.join(REPO, "scripts", "model_comparison")) + +from rampnet.detection_eval import build_ground_truth, radius_sq_for # noqa: E402 +from compare import load_bundle, DetectionCache, cache_key # noqa: E402 +from detectors import build_detector # noqa: E402 +from complementarity import matched_gt, model_spec, compare_args # noqa: E402 +from silent_activation import ( # noqa: E402 + NULL_SEED, NULL_TRIALS, class_of, nearest_peak, null_percentile, seam_of, + site_profile) +from farfield_forensics import quartiles # noqa: E402 + +CELLS = ("both", "rampnet_only", "challenger_only", "neither") +#: Cells where RampNet did NOT find the ramp -- the only ones a null is meaningful for. +MISS_CELLS = ("challenger_only", "neither") + + +def cell_of(rampnet_hit, challenger_hit): + if rampnet_hit: + return "both" if challenger_hit else "rampnet_only" + return "challenger_only" if challenger_hit else "neither" + + +def summarize(rows, cell): + """Per-cell summary. ``None`` for null stats on the hit cells, which have none.""" + sel = [r for r in rows if r["cell"] == cell] + if not sel: + return {"cell": cell, "n": 0} + acts = [r["act"] for r in sel] + classes = {c: sum(1 for r in sel if r["class"] == c) + for c in ("absent", "faint_local", "tail")} + out = { + "cell": cell, + "n": len(sel), + "act_median": round(quartiles(acts)[1], 4), + "center_median": round(quartiles([r["center"] for r in sel])[1], 4), + "argmax_off_px_median": round(quartiles([r["argmax_off_px"] for r in sel])[1], 1), + "nearest_peak_px_median": round( + quartiles([r["nearest_peak_px"] for r in sel + if r["nearest_peak_px"] is not None])[1], 1) + if any(r["nearest_peak_px"] is not None for r in sel) else None, + "classes": classes, + "class_share": {c: round(v / len(sel), 3) for c, v in classes.items()}, + "seam": sum(1 for r in sel if r["seam"]), + } + nulls = [r["null_pct"] for r in sel if r["null_pct"] is not None] + if nulls: + out["null_pct_median"] = round(quartiles(nulls)[1], 3) + out["above_null_p95"] = sum(1 for r in sel + if r["null_pct"] is not None and r["act"] > r["null_p95"]) + out["null_med_median"] = round( + quartiles([r["null_med"] for r in sel if r["null_med"] is not None])[1], 4) + return out + + +def main(argv=None): + p = argparse.ArgumentParser(description=__doc__.split("\n")[0], + formatter_class=argparse.RawDescriptionHelpFormatter) + p.add_argument("--split", default="richmond") + p.add_argument("--model", default="vistas:curb-cut", + help="Challenger model spec, as compare.py --models takes it.") + p.add_argument("--panos-root", default=REPO, + help="Checkout holding benchmark//panos/ (a worktree will not).") + p.add_argument("--cache-dir", default=os.path.join(REPO, ".model_cache")) + p.add_argument("--radius", type=float, default=0.022) + p.add_argument("--tiling", choices=["perspective", "none"], default="perspective") + p.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"), default=None, + help="Must match the run being analysed -- it is part of the cache key.") + p.add_argument("--vistas-revision", default=None) + p.add_argument("--limit", type=int, default=None, help="Smoke test: first N panos.") + p.add_argument("--json-out", default=None) + args = p.parse_args(argv) + + if args.json_out and args.limit: + p.error("--limit truncates the run; refusing to write it to --json-out") + + import torch + import threshold_sweep as ts + from miss_gallery import pano_path + + bundle = os.path.join(REPO, "benchmark", args.split) + records, verdicts, _ = load_bundle(bundle) + if verdicts is None: + sys.exit(f"{bundle}: no verdicts.json -- this needs a reviewed split.") + provider, model_id = model_spec(args.model) + label, detector = build_detector(provider, model_id, records, compare_args(args)) + sig = detector.signature() + cache = DetectionCache(args.cache_dir) + radius_sq = radius_sq_for(args.radius) + + # ---- partition every GT ramp into a complementarity cell ------------------ + sites, missing = [], 0 + for pid, entry in verdicts.items(): + gt = build_ground_truth(records[pid]["detections"], entry["dets"], + entry["missed"], entry["no_missed"]) + if not gt.fn_confirmed: + continue + cp = cache.get(cache_key(label, sig, args.split, pid)) + if cp is None: + missing += 1 + continue + rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) + for d in records[pid]["detections"]] + mr = matched_gt(rp, gt.gt_points, radius_sq) + mc = matched_gt(cp, gt.gt_points, radius_sq) + for i, (gx, gy) in enumerate(gt.gt_points): + sites.append({"pano": pid, "x": gx, "y": gy, + "cell": cell_of(i in mr, i in mc)}) + if missing: + print(f"WARNING: {missing} panos had no cached {label} detections and were " + f"skipped. Pass the --vistas-input-size the run used.", flush=True) + if not sites: + sys.exit("No sites -- is the challenger cached for this split/input size?") + + by_pano = {} + for s in sites: + by_pano.setdefault(s["pano"], []).append(s) + panos = sorted(by_pano) + if args.limit: + panos = panos[:args.limit] + + counts = {c: sum(1 for s in sites if s["cell"] == c) for c in CELLS} + print(f"=== Cascade gate: rampnet heatmap at {label}'s recoveries " + f"({args.split}, {len(sites)} GT ramps in {len(by_pano)} panos) ===") + print(" cells: " + " ".join(f"{c}={counts[c]}" for c in CELLS), flush=True) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = ts.load_model().to(device) + print(f" device={device} model=projectsidewalk/rampnet-model " + f"(single-pass fp32, as op_cache)", flush=True) + + rng = random.Random(NULL_SEED) + rows, skipped = [], 0 + for i, pid in enumerate(panos, 1): + path = pano_path(args.split, pid, args.panos_root) + if not os.path.exists(path): + skipped += len(by_pano[pid]) + continue + heat = ts.heatmap_for(model, device, path, use_fp16=False) + preds = [(d["x_normalized"], d["y_normalized"], d["confidence"]) + for d in records[pid]["detections"]] + for s in by_pano[pid]: + act, off_px, center = site_profile(heat, s["x"], s["y"], radius_sq) + npx, nscore = nearest_peak(preds, s["x"], s["y"]) + row = {**s, "act": round(act, 6), "center": round(center, 6), + "argmax_off_px": round(off_px, 1), + "nearest_peak_px": None if npx == float("inf") else round(npx, 1), + "nearest_peak_score": nscore, + "class": class_of(act), "seam": seam_of(s["x"], radius_sq), + "null_pct": None, "null_med": None, "null_p95": None} + # The null is only meaningful where rampnet did NOT find the ramp; the + # hit cells are high by construction and are here as a positive control. + if s["cell"] in MISS_CELLS: + a, pct, med, p95 = null_percentile(heat, s["x"], s["y"], rng, + radius_sq=radius_sq) + row.update(null_pct=round(pct, 4), null_med=round(med, 6), + null_p95=round(p95, 6)) + rows.append(row) + if i % 20 == 0 or i == len(panos): + print(f" {i}/{len(panos)} panos", flush=True) + if skipped: + print(f"WARNING: {skipped} sites skipped -- pano jpg not found under " + f"--panos-root {args.panos_root}", flush=True) + + summaries = [summarize(rows, c) for c in CELLS] + print() + hdr = (f"{'cell':17s} {'n':>4s} {'act med':>8s} {'centre':>8s} {'argmax off':>11s} " + f"{'absent':>7s} {'faint':>6s} {'tail':>5s} {'null pct':>9s}") + print(hdr) + print("-" * len(hdr)) + for s in summaries: + if not s["n"]: + continue + sh = s["class_share"] + print(f"{s['cell']:17s} {s['n']:4d} {s['act_median']:8.4f} " + f"{s['center_median']:8.4f} {s['argmax_off_px_median']:10.1f}p " + f"{sh['absent']:7.0%} {sh['faint_local']:6.0%} {sh['tail']:5.0%} " + + (f"{s['null_pct_median']:9.3f}" if "null_pct_median" in s else f"{'—':>9s}")) + print() + print(" faint_local = a real sub-threshold response AT the site -> a prior could raise it.") + print(" tail = in-window max at/above the 0.05 peak floor, i.e. an ADJACENT mode") + print(" reaching in; relaxing a threshold there fires on the neighbour,") + print(" not the ramp. Argues for sigma/NMS (#46), not for a prior.") + print(" absent = flat heatmap; nothing to raise.") + + if args.json_out: + payload = {"split": args.split, "challenger": label, + "vistas_input_size": args.vistas_input_size, + "radius": args.radius, "null_trials": NULL_TRIALS, + "null_seed": NULL_SEED, "n_sites": len(rows), + "n_panos": len(panos), "skipped_sites": skipped, + "cells": summaries, "sites": rows} + os.makedirs(os.path.dirname(os.path.abspath(args.json_out)), exist_ok=True) + # newline="" so a Windows re-run does not emit CRLF and break byte-comparison. + with open(args.json_out, "w", encoding="utf-8", newline="") as f: + json.dump(payload, f, indent=2, sort_keys=True) + f.write("\n") + print(f"\nwrote {args.json_out}") + + +if __name__ == "__main__": + main() From dcb9c325e6533c8620f3615fb5e87a7a9ac944f3 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:21:45 -0700 Subject: [PATCH 06/10] cascade_gate: read floor peaks from op_cache, not the shipped detections (#126) Self-inflicted, caught before the write-up. nearest_peak expects peak_local_max output down to the 0.05 floor. I fed it records[pid]["detections"], which on richmond are all >= 0.5519 -- the SHIPPED operating point. So "is there a peak near this missed ramp?" was really asking "is there a 0.55+ detection near it?", which for a missed ramp is close to definitionally no, and it made every miss look peakless with the nearest peak a median 104 px away. analysis_out/op_cache/richmond.json is committed and holds the floor peaks: 537 for richmond, 270 of them below 0.55. That is the input the sub-threshold question needs. The greedy match that DEFINED the miss set still uses the bundle records, because that is what produced the published 238/9/72 -- two different inputs to two different questions, now documented as such. Also replaced the class_of column in the printed table with peak_in_radius. class_of is #46 Phase 1 cutoffs, and there "tail" could only mean an outside mode reaching in because that population was SILENT misses by definition. This population is every RampNet miss, so act >= 0.05 has two causes the class cannot separate: a floor peak inside the radius (the model localized it and the detection was lost downstream -- recoverable WITHOUT a second model) versus unpeaked heatmap mass (nothing for a prior to promote). The old table also printed "100% tail" for the two hit cells, which is meaningless there. Co-Authored-By: Claude Opus 5 --- analysis_out/cascade_gate.json | 5060 ++++++++++++++++++++++++++++++ scripts/analysis/cascade_gate.py | 90 +- 2 files changed, 5132 insertions(+), 18 deletions(-) create mode 100644 analysis_out/cascade_gate.json diff --git a/analysis_out/cascade_gate.json b/analysis_out/cascade_gate.json new file mode 100644 index 00000000..bfca91e3 --- /dev/null +++ b/analysis_out/cascade_gate.json @@ -0,0 +1,5060 @@ +{ + "cells": [ + { + "act_median": 0.8893, + "argmax_off_px_median": 0.0, + "cell": "both", + "center_median": 0.8893, + "class_share": { + "absent": 0.0, + "faint_local": 0.0, + "tail": 1.0 + }, + "classes": { + "absent": 0, + "faint_local": 0, + "tail": 220 + }, + "n": 220, + "nearest_peak_px_median": 0.0, + "seam": 5 + }, + { + "act_median": 0.8449, + "argmax_off_px_median": 0.0, + "cell": "rampnet_only", + "center_median": 0.8431, + "class_share": { + "absent": 0.0, + "faint_local": 0.0, + "tail": 1.0 + }, + "classes": { + "absent": 0, + 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"nearest_peak_score": 0.8998149633407593, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "893023542323690", + "seam": false, + "x": 0.55078125, + "y": 0.552734375 + }, + { + "act": 0.920857, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.920857, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.920896053314209, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "911337534530260", + "seam": false, + "x": 0.6123046875, + "y": 0.6015625 + }, + { + "act": 0.856427, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.856427, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8564420342445374, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "911337534530260", + "seam": false, + "x": 0.91015625, + "y": 0.583984375 + }, + { + "act": 0.808182, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.808182, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8081934452056885, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "911337534530260", + "seam": false, + "x": 0.2763671875, + "y": 0.5234375 + }, + { + "act": 0.635044, + "argmax_off_px": 22.5, + "cell": "challenger_only", + "center": 0.176682, + "class": "tail", + "nearest_peak_px": 23.5, + "nearest_peak_score": 0.6433027982711792, + "null_med": 0.016102, + "null_p95": 0.856427, + "null_pct": 0.885, + "pano": "911337534530260", + "seam": false, + "x": 0.7846464584812973, + "y": 0.6185858524206913 + }, + { + "act": 1.0, + "argmax_off_px": 0.0, + "cell": "both", + "center": 1.0, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 1.0076472759246826, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "925077602773417", + "seam": false, + "x": 0.70703125, + "y": 0.599609375 + }, + { + "act": 0.895271, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.895271, + "class": "tail", + "nearest_peak_px": 0.0, + 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0.9655765295028687, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.58984375, + "y": 0.599609375 + }, + { + "act": 0.943558, + "argmax_off_px": 0.0, + "cell": "rampnet_only", + "center": 0.943558, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.9435607194900513, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.6669921875, + "y": 0.599609375 + }, + { + "act": 0.927461, + "argmax_off_px": 0.0, + "cell": "rampnet_only", + "center": 0.927461, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.9274734258651733, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.12890625, + "y": 0.5859375 + }, + { + "act": 0.922892, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.922892, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.9229074716567993, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.39453125, + "y": 0.583984375 + }, + { + "act": 0.898126, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.898126, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8981457948684692, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.4326171875, + "y": 0.583984375 + }, + { + "act": 0.823908, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.823908, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8239241242408752, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.9169921875, + "y": 0.599609375 + }, + { + "act": 0.810013, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.810013, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8100234270095825, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.0966796875, + "y": 0.583984375 + }, + { + "act": 0.8086, + "argmax_off_px": 0.0, + "cell": "rampnet_only", + "center": 0.8086, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8086158037185669, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "934739365184374", + "seam": false, + "x": 0.87109375, + "y": 0.599609375 + }, + { + "act": 0.899075, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.899075, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8990710973739624, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "996959015535106", + "seam": false, + "x": 0.38671875, + "y": 0.5546875 + }, + { + "act": 0.877263, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.877263, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.8773050904273987, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "996959015535106", + "seam": false, + "x": 0.6044921875, + "y": 0.552734375 + }, + { + "act": 0.634536, + "argmax_off_px": 0.0, + "cell": "both", + "center": 0.634536, + "class": "tail", + "nearest_peak_px": 0.0, + "nearest_peak_score": 0.6345473527908325, + "null_med": null, + "null_p95": null, + "null_pct": null, + "pano": "996959015535106", + "seam": false, + "x": 0.5498046875, + "y": 0.5078125 + }, + { + "act": 0.098079, + "argmax_off_px": 4.9, + "cell": "challenger_only", + "center": 0.092798, + "class": "tail", + "nearest_peak_px": 62.9, + "nearest_peak_score": 0.8990710973739624, + "null_med": 0.007513, + "null_p95": 0.877263, + "null_pct": 0.81, + "pano": "996959015535106", + "seam": false, + "x": 0.44606062455610795, + "y": 0.5233333703243371 + } + ], + "skipped_sites": 0, + "split": "richmond", + "vistas_input_size": [ + 1024, + 1024 + ] +} diff --git a/scripts/analysis/cascade_gate.py b/scripts/analysis/cascade_gate.py index f165af12..87c06246 100644 --- a/scripts/analysis/cascade_gate.py +++ b/scripts/analysis/cascade_gate.py @@ -28,18 +28,36 @@ comparable to that phase's 8% absent / 62% adjacent-tail / 30% faint decomposition, and it means a fix to the probe fixes both analyses. -**Three outcomes, and the middle one is not the interesting one.** ``faint_local`` with -the in-window argmax *at* the site is signal a prior could raise. ``absent`` is nothing -to raise. ``tail`` — the in-window maximum at or above the peak floor — is an adjacent -mode reaching in, and for a *missed* ramp it means relaxing a threshold there would fire -on the neighbour, not on the ramp. ``tail`` therefore argues for sigma/NMS work (#46's -adjacent-pair merge), **not** for a threshold prior, even though it looks like "signal -is present" if you only read ``act``. +**``class_of`` is imported for comparability, but the load-bearing column here is +``peak_in_radius``, not the class.** #46 Phase 1 applied those cutoffs to *silent* +misses — defined as no floor peak within the radius — so there ``tail`` (act >= 0.05) +could only mean an outside mode reaching in. This population is every RampNet miss, not +just the silent ones, so ``act >= 0.05`` here has two very different causes and the +class alone cannot separate them: + +* **a floor peak inside the radius** — the model localized the ramp and the detection + was lost *downstream*, either to the shipped threshold or to the greedy matcher giving + that peak to an adjacent GT. Recoverable, and recoverable **without a second model**. +* **no floor peak inside the radius** — ``act`` is unpeaked heatmap mass that + ``peak_local_max`` never called a local maximum. A threshold prior has nothing to + promote, because promotion operates on peaks. + +``peak_in_radius`` is therefore reported per cell and is what the read below turns on. **Sub-threshold signal is necessary, not sufficient.** A positive here says the cascade is not ruled out and is worth costing; it does not demonstrate one works. Any realisable gain is bounded above by the ~44 attributable ramps, not the raw 54. +**The floor peaks come from ``analysis_out/op_cache/.json``, NOT from the +bundle records.** The bundle's committed detections are the published operating point +— on richmond every one of them scores >= 0.5519 — so asking "is there a peak near this +missed ramp?" of *those* answers a different question and makes every miss look +peakless. The op_cache holds ``peak_local_max`` output down to the 0.05 floor, which is +what "did the model say anything here, below the threshold we ship?" actually needs. +The greedy match that DEFINED the miss still uses the bundle records, because that is +what produced the published 238/9/72 — the two are deliberately different inputs to +two different questions. + Inputs: the native-resolution panoramas at ``benchmark//panos/`` (git-ignored, published as ``projectsidewalk/rampnet-benchmark``) — ``--panos-root`` points at whichever checkout holds them, since a worktree will not. The challenger's detections @@ -70,6 +88,7 @@ NULL_SEED, NULL_TRIALS, class_of, nearest_peak, null_percentile, seam_of, site_profile) from farfield_forensics import quartiles # noqa: E402 +from operating_point_curve import CACHE_DIR, read_cache # noqa: E402 CELLS = ("both", "rampnet_only", "challenger_only", "neither") #: Cells where RampNet did NOT find the ramp -- the only ones a null is meaningful for. @@ -104,6 +123,12 @@ def summarize(rows, cell): "class_share": {c: round(v / len(sel), 3) for c, v in classes.items()}, "seam": sum(1 for r in sel if r["seam"]), } + inr = [r for r in sel if r["peak_in_radius"]] + out["peak_in_radius"] = len(inr) + out["peak_in_radius_share"] = round(len(inr) / len(sel), 3) + if inr: + out["peak_in_radius_score_median"] = round( + quartiles([r["nearest_peak_score"] for r in inr])[1], 4) nulls = [r["null_pct"] for r in sel if r["null_pct"] is not None] if nulls: out["null_pct_median"] = round(quartiles(nulls)[1], 3) @@ -173,6 +198,18 @@ def main(argv=None): if not sites: sys.exit("No sites -- is the challenger cached for this split/input size?") + # Floor peaks (>= 0.05) for the "did the model say anything sub-threshold here?" + # question. See the docstring: the bundle records are the SHIPPED operating point + # and would answer a different question. + floor_peaks, floor_src = {}, "op_cache" + try: + cached, _ = read_cache(os.path.join(CACHE_DIR, f"{args.split}.json")) + for pd in cached: + floor_peaks[pd["pano"]] = pd["preds"] + except (OSError, ValueError, KeyError): + floor_src = "MISSING (fell back to bundle records -- distances are to the "\ + "shipped operating point, not the 0.05 floor)" + by_pano = {} for s in sites: by_pano.setdefault(s["pano"], []).append(s) @@ -189,6 +226,7 @@ def main(argv=None): model = ts.load_model().to(device) print(f" device={device} model=projectsidewalk/rampnet-model " f"(single-pass fp32, as op_cache)", flush=True) + print(f" floor peaks (>=0.05) from: {floor_src}", flush=True) rng = random.Random(NULL_SEED) rows, skipped = [], 0 @@ -198,15 +236,23 @@ def main(argv=None): skipped += len(by_pano[pid]) continue heat = ts.heatmap_for(model, device, path, use_fp16=False) - preds = [(d["x_normalized"], d["y_normalized"], d["confidence"]) - for d in records[pid]["detections"]] + preds = floor_peaks.get(pid) or [ + (d["x_normalized"], d["y_normalized"], d["confidence"]) + for d in records[pid]["detections"]] for s in by_pano[pid]: act, off_px, center = site_profile(heat, s["x"], s["y"], radius_sq) npx, nscore = nearest_peak(preds, s["x"], s["y"]) + r_px = radius_sq ** 0.5 row = {**s, "act": round(act, 6), "center": round(center, 6), "argmax_off_px": round(off_px, 1), "nearest_peak_px": None if npx == float("inf") else round(npx, 1), "nearest_peak_score": nscore, + # A floor peak inside the match radius on a MISSED ramp means the + # model did localize it and the detection was lost downstream -- + # either to the shipped threshold, or to the greedy matcher giving + # the peak to an adjacent GT. That is recoverable without a second + # model; a peak outside the radius is not. + "peak_in_radius": bool(npx < r_px), "class": class_of(act), "seam": seam_of(s["x"], radius_sq), "null_pct": None, "null_med": None, "null_p95": None} # The null is only meaningful where rampnet did NOT find the ramp; the @@ -226,23 +272,31 @@ def main(argv=None): summaries = [summarize(rows, c) for c in CELLS] print() hdr = (f"{'cell':17s} {'n':>4s} {'act med':>8s} {'centre':>8s} {'argmax off':>11s} " - f"{'absent':>7s} {'faint':>6s} {'tail':>5s} {'null pct':>9s}") + f"{'floor peak in R':>16s} {'its score':>10s} {'null pct':>9s}") print(hdr) print("-" * len(hdr)) for s in summaries: if not s["n"]: continue - sh = s["class_share"] + miss = s["cell"] in MISS_CELLS print(f"{s['cell']:17s} {s['n']:4d} {s['act_median']:8.4f} " f"{s['center_median']:8.4f} {s['argmax_off_px_median']:10.1f}p " - f"{sh['absent']:7.0%} {sh['faint_local']:6.0%} {sh['tail']:5.0%} " - + (f"{s['null_pct_median']:9.3f}" if "null_pct_median" in s else f"{'—':>9s}")) + f"{s['peak_in_radius']:9d} ({s['peak_in_radius_share']:3.0%}) " + + (f"{s.get('peak_in_radius_score_median', float('nan')):10.3f}" + if s.get("peak_in_radius") else f"{'—':>10s}") + + (f"{s['null_pct_median']:9.3f}" if miss else f"{'—':>9s}")) + print() + print(" The hit cells are the positive control: a matched detection is inside the") + print(" radius by definition, so their act/centre ~0.85+ and 100% peak-in-R are what") + print(" a working probe MUST show, not a finding.") print() - print(" faint_local = a real sub-threshold response AT the site -> a prior could raise it.") - print(" tail = in-window max at/above the 0.05 peak floor, i.e. an ADJACENT mode") - print(" reaching in; relaxing a threshold there fires on the neighbour,") - print(" not the ramp. Argues for sigma/NMS (#46), not for a prior.") - print(" absent = flat heatmap; nothing to raise.") + print(" For the two MISS cells, 'floor peak in R' is the whole question:") + print(" - peak inside R -> the model DID localize the ramp and the detection was") + print(" lost downstream, to the shipped threshold or to the greedy matcher handing") + print(" the peak to an adjacent GT. Recoverable WITHOUT a second model.") + print(" - peak outside R -> nothing was extracted there even at the 0.05 floor, so a") + print(" threshold prior has no peak to promote. 'act' can still be non-zero: that is") + print(" unpeaked heatmap mass, which peak_local_max did not call a local maximum.") if args.json_out: payload = {"split": args.split, "challenger": label, From b7342dc9251b3480dd5a98f4dada7e5ab0b8020f Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:28:51 -0700 Subject: [PATCH 07/10] complementarity: ask it at the operating point we would deploy (#126) The committed bundle detections are the SHIPPED operating point -- on richmond every one scores >= 0.5519 -- while this document own recommendation since #54/#55 (PR #79) is 0.30. For a complementarity read those are different models, and the difference decides who gets credit for a recovery. Verified the op_cache is the same source before trusting it: filtered to >= 0.5519 it reproduces the published richmond row exactly (P 0.9636 R 0.7677 F1 0.8546, 238/9/72). At 0.30 the same peaks give P 0.9018 R 0.8290 F1 0.8639, 257/28/53 -- matching the committed corrected_at_0.3.csv. So RampNet at the recommended point already finds 19 of the ramps the shipped point misses. --rampnet-op-threshold re-sources RampNet from analysis_out/op_cache/.json at a given threshold. Default is the bundle records, so the published roster numbers and the committed #35 gate results are unchanged -- paterson/gemini-pro still reproduces 36 of 124, 88 nobody finds, oracle-union 0.777. Also regenerates analysis_out/cascade_gate.json, which was first committed from a run whose nearest_peak input was the shipped detections rather than the floor peaks. Co-Authored-By: Claude Opus 5 --- analysis_out/cascade_gate.json | 1050 +++++++++++++++++---------- scripts/analysis/complementarity.py | 52 +- 2 files changed, 728 insertions(+), 374 deletions(-) diff --git a/analysis_out/cascade_gate.json b/analysis_out/cascade_gate.json index bfca91e3..b0e31666 100644 --- a/analysis_out/cascade_gate.json +++ b/analysis_out/cascade_gate.json @@ -17,6 +17,9 @@ }, "n": 220, "nearest_peak_px_median": 0.0, + "peak_in_radius": 220, + "peak_in_radius_score_median": 0.8893, + "peak_in_radius_share": 1.0, "seam": 5 }, { @@ -36,6 +39,9 @@ }, "n": 18, "nearest_peak_px_median": 0.0, + "peak_in_radius": 18, + "peak_in_radius_score_median": 0.8431, + "peak_in_radius_share": 1.0, "seam": 0 }, { @@ -55,9 +61,12 @@ "tail": 51 }, "n": 54, - "nearest_peak_px_median": 104.9, + "nearest_peak_px_median": 13.7, "null_med_median": 0.0091, "null_pct_median": 0.895, + "peak_in_radius": 39, + "peak_in_radius_score_median": 0.3386, + "peak_in_radius_share": 0.722, "seam": 1 }, { @@ -77,9 +86,12 @@ "tail": 18 }, "n": 18, - "nearest_peak_px_median": 56.1, + "nearest_peak_px_median": 16.3, "null_med_median": 0.0065, "null_pct_median": 0.92, + "peak_in_radius": 14, + "peak_in_radius_score_median": 0.4595, + "peak_in_radius_share": 0.778, "seam": 0 } ], @@ -97,11 +109,12 @@ "center": 0.913488, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.913480281829834, + "nearest_peak_score": 0.9134649634361267, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1002881108606016", + "peak_in_radius": true, "seam": false, "x": 0.4326171875, "y": 0.5078125 @@ -113,11 +126,12 @@ "center": 0.895599, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8956112265586853, + "nearest_peak_score": 0.89559006690979, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1002881108606016", + "peak_in_radius": true, "seam": false, "x": 0.7216796875, "y": 0.583984375 @@ -129,11 +143,12 @@ "center": 0.618312, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6183195114135742, + "nearest_peak_score": 0.6183094382286072, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1002881108606016", + "peak_in_radius": true, "seam": false, "x": 0.58203125, "y": 0.521484375 @@ -145,11 +160,12 @@ "center": 0.596123, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.59613037109375, + "nearest_peak_score": 0.5961191654205322, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1023400645666420", + "peak_in_radius": true, "seam": false, "x": 0.53515625, "y": 0.552734375 @@ -160,12 +176,13 @@ "cell": "challenger_only", "center": 0.10486, "class": "tail", - "nearest_peak_px": 266.1, - "nearest_peak_score": 0.59613037109375, + "nearest_peak_px": 16.0, + "nearest_peak_score": 0.20250369608402252, "null_med": 0.006787, "null_p95": 0.124406, "null_pct": 0.995, "pano": "1023400645666420", + "peak_in_radius": true, "seam": false, "x": 0.7919191857540246, "y": 0.6331313069661458 @@ -176,12 +193,13 @@ "cell": "challenger_only", "center": 0.017964, "class": "tail", - "nearest_peak_px": 313.6, - "nearest_peak_score": 0.59613037109375, + "nearest_peak_px": 24.3, + "nearest_peak_score": 0.24167664349079132, "null_med": 0.014093, "null_p95": 0.367628, "null_pct": 0.88, "pano": "1023400645666420", + "peak_in_radius": false, "seam": false, "x": 0.2294949433297822, "y": 0.5919191857540246 @@ -192,12 +210,13 @@ "cell": "challenger_only", "center": 0.371854, "class": "tail", - "nearest_peak_px": 103.4, - "nearest_peak_score": 0.59613037109375, + "nearest_peak_px": 2.3, + "nearest_peak_score": 0.3920925259590149, "null_med": 0.013856, "null_p95": 0.581377, "null_pct": 0.94, "pano": "1023400645666420", + "peak_in_radius": true, "seam": false, "x": 0.43434342817826704, "y": 0.5419191857540246 @@ -209,11 +228,12 @@ "center": 0.876577, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8765917420387268, + "nearest_peak_score": 0.8765838742256165, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1041352537628655", + "peak_in_radius": true, "seam": false, "x": 0.2216796875, "y": 0.5703125 @@ -225,11 +245,12 @@ "center": 0.835651, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8356579542160034, + "nearest_peak_score": 0.8356412649154663, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1041352537628655", + "peak_in_radius": true, "seam": false, "x": 0.93359375, "y": 0.5546875 @@ -241,11 +262,12 @@ "center": 0.697017, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6970375776290894, + "nearest_peak_score": 0.6970109343528748, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1041352537628655", + "peak_in_radius": true, "seam": false, "x": 0.78515625, "y": 0.630859375 @@ -257,11 +279,12 @@ "center": 0.94323, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9432597160339355, + "nearest_peak_score": 0.9432406425476074, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1044606157061812", + "peak_in_radius": true, "seam": false, "x": 0.30078125, "y": 0.5546875 @@ -273,11 +296,12 @@ "center": 0.895965, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8959958553314209, + "nearest_peak_score": 0.895974338054657, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1044606157061812", + "peak_in_radius": true, "seam": false, "x": 0.94140625, "y": 0.568359375 @@ -289,11 +313,12 @@ "center": 0.891897, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8919112682342529, + "nearest_peak_score": 0.8918793201446533, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1044606157061812", + "peak_in_radius": true, "seam": false, "x": 0.64453125, "y": 0.599609375 @@ -305,11 +330,12 @@ "center": 0.841396, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8414136171340942, + "nearest_peak_score": 0.8413959741592407, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1044606157061812", + "peak_in_radius": true, "seam": false, "x": 0.1201171875, "y": 0.552734375 @@ -321,11 +347,12 @@ "center": 0.909499, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.909525990486145, + "nearest_peak_score": 0.9094994068145752, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1050503583329955", + "peak_in_radius": true, "seam": false, "x": 0.35546875, "y": 0.599609375 @@ -337,11 +364,12 @@ "center": 0.869249, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8692640662193298, + "nearest_peak_score": 0.869255781173706, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1050503583329955", + "peak_in_radius": true, "seam": false, "x": 0.66015625, "y": 0.6015625 @@ -353,11 +381,12 @@ "center": 0.738541, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7385543584823608, + "nearest_peak_score": 0.738539457321167, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1050503583329955", + "peak_in_radius": true, "seam": false, "x": 0.42578125, "y": 0.5390625 @@ -369,11 +398,12 @@ "center": 0.946644, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9466584920883179, + "nearest_peak_score": 0.9466462135314941, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1051211967136526", + "peak_in_radius": true, "seam": false, "x": 0.4091796875, "y": 0.5390625 @@ -385,11 +415,12 @@ "center": 0.904591, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9046050310134888, + "nearest_peak_score": 0.9045884609222412, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1051211967136526", + "peak_in_radius": true, "seam": false, "x": 0.55859375, "y": 0.568359375 @@ -401,11 +432,12 @@ "center": 0.878982, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8789999485015869, + "nearest_peak_score": 0.8789879679679871, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1051211967136526", + "peak_in_radius": true, "seam": false, "x": 0.26171875, "y": 0.5859375 @@ -417,11 +449,12 @@ "center": 0.816361, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8163886070251465, + "nearest_peak_score": 0.8163682818412781, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1071700701728910", + "peak_in_radius": true, "seam": false, "x": 0.8544921875, "y": 0.5859375 @@ -433,11 +466,12 @@ "center": 0.766434, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7664440870285034, + "nearest_peak_score": 0.7664359211921692, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1071700701728910", + "peak_in_radius": true, "seam": false, "x": 0.12109375, "y": 0.552734375 @@ -449,11 +483,12 @@ "center": 0.675663, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6756789088249207, + "nearest_peak_score": 0.6756716966629028, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1071700701728910", + "peak_in_radius": true, "seam": false, "x": 0.95703125, "y": 0.5390625 @@ -465,11 +500,12 @@ "center": 0.938291, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9382998943328857, + "nearest_peak_score": 0.9382756948471069, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1073049581231056", + "peak_in_radius": true, "seam": true, "x": 0.9873046875, "y": 0.583984375 @@ -481,11 +517,12 @@ "center": 0.843096, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8431106805801392, + "nearest_peak_score": 0.8430953621864319, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1073049581231056", + "peak_in_radius": true, "seam": false, "x": 0.16796875, "y": 0.6171875 @@ -497,11 +534,12 @@ "center": 0.421524, "class": "tail", "nearest_peak_px": 11.0, - "nearest_peak_score": 0.6145117878913879, + "nearest_peak_score": 0.6145032644271851, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1089573432664131", + "peak_in_radius": true, "seam": false, "x": 0.9536616099964488, "y": 0.5459090909090908 @@ -513,11 +551,12 @@ "center": 0.801695, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.801726222038269, + "nearest_peak_score": 0.8017029166221619, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1092133469495462", + "peak_in_radius": true, "seam": false, "x": 0.5419921875, "y": 0.537109375 @@ -529,11 +568,12 @@ "center": 0.800788, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8008056879043579, + "nearest_peak_score": 0.8007755875587463, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1092133469495462", + "peak_in_radius": true, "seam": false, "x": 0.41796875, "y": 0.5234375 @@ -545,11 +585,12 @@ "center": 0.748474, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.748491644859314, + "nearest_peak_score": 0.7484796643257141, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1092133469495462", + "peak_in_radius": true, "seam": false, "x": 0.69921875, "y": 0.6328125 @@ -561,11 +602,12 @@ "center": 0.711291, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.711294412612915, + "nearest_peak_score": 0.7112849354743958, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1092133469495462", + "peak_in_radius": true, "seam": false, "x": 0.4326171875, "y": 0.5234375 @@ -577,11 +619,12 @@ "center": 0.646609, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6466131210327148, + "nearest_peak_score": 0.6466015577316284, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1092133469495462", + "peak_in_radius": true, "seam": false, "x": 0.30078125, "y": 0.568359375 @@ -593,11 +636,12 @@ "center": 0.097523, "class": "tail", "nearest_peak_px": 22.0, - "nearest_peak_score": 0.6466131210327148, + "nearest_peak_score": 0.6466015577316284, "null_med": 0.010539, "null_p95": 0.603415, "null_pct": 0.98, "pano": "1092133469495462", + "peak_in_radius": true, "seam": false, "x": 0.28282827666311555, "y": 0.5919191903779001 @@ -609,11 +653,12 @@ "center": 0.889245, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8892737627029419, + "nearest_peak_score": 0.8892554044723511, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1107064934776754", + "peak_in_radius": true, "seam": false, "x": 0.10546875, "y": 0.5859375 @@ -625,11 +670,12 @@ "center": 0.880806, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8808392286300659, + "nearest_peak_score": 0.8808186054229736, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1107064934776754", + "peak_in_radius": true, "seam": false, "x": 0.58203125, "y": 0.5859375 @@ -641,11 +687,12 @@ "center": 0.851676, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8517063856124878, + "nearest_peak_score": 0.851691722869873, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1107064934776754", + "peak_in_radius": true, "seam": false, "x": 0.8857421875, "y": 0.615234375 @@ -657,11 +704,12 @@ "center": 0.824314, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8243341445922852, + "nearest_peak_score": 0.8243358731269836, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1107064934776754", + "peak_in_radius": true, "seam": false, "x": 0.3935546875, "y": 0.568359375 @@ -673,11 +721,12 @@ "center": 1.0, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 1.0072057247161865, + "nearest_peak_score": 1.0071896314620972, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1108131584865233", + "peak_in_radius": true, "seam": false, "x": 0.8388671875, "y": 0.568359375 @@ -689,11 +738,12 @@ "center": 0.961798, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9618116617202759, + "nearest_peak_score": 0.9617979526519775, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1108131584865233", + "peak_in_radius": true, "seam": false, "x": 0.1904296875, "y": 0.537109375 @@ -705,11 +755,12 @@ "center": 0.730985, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7309937477111816, + "nearest_peak_score": 0.7309690713882446, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1110314244387816", + "peak_in_radius": true, "seam": false, "x": 0.79296875, "y": 0.4921875 @@ -720,12 +771,13 @@ "cell": "challenger_only", "center": 0.277798, "class": "tail", - "nearest_peak_px": 321.4, - "nearest_peak_score": 0.7309937477111816, + "nearest_peak_px": 10.2, + "nearest_peak_score": 0.36452651023864746, "null_med": 0.005449, "null_p95": 0.302846, "null_pct": 0.97, "pano": "1110314244387816", + "peak_in_radius": true, "seam": false, "x": 0.4819725424417728, "y": 0.5769238621475898 @@ -736,12 +788,13 @@ "cell": "challenger_only", "center": 0.080359, "class": "tail", - "nearest_peak_px": 99.3, - "nearest_peak_score": 0.7309937477111816, + "nearest_peak_px": 5.2, + "nearest_peak_score": 0.08584265410900116, "null_med": 0.003516, "null_p95": 0.730985, "null_pct": 0.89, "pano": "1110314244387816", + "peak_in_radius": true, "seam": false, "x": 0.6961413694097003, "y": 0.5019622085748252 @@ -752,12 +805,13 @@ "cell": "neither", "center": 0.047331, "class": "tail", - "nearest_peak_px": 274.7, - "nearest_peak_score": 0.7309937477111816, + "nearest_peak_px": 16.3, + "nearest_peak_score": 0.07500965893268585, "null_med": 0.006771, "null_p95": 0.513768, "null_pct": 0.765, "pano": "1110314244387816", + "peak_in_radius": true, "seam": false, "x": 0.5260899962249893, "y": 0.5462150503817342 @@ -769,11 +823,12 @@ "center": 0.997578, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9975988864898682, + "nearest_peak_score": 0.9975808262825012, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1119718896242677", + "peak_in_radius": true, "seam": false, "x": 0.1748046875, "y": 0.583984375 @@ -785,11 +840,12 @@ "center": 0.887822, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8878290057182312, + "nearest_peak_score": 0.8878064155578613, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1119718896242677", + "peak_in_radius": true, "seam": false, "x": 0.82421875, "y": 0.646484375 @@ -801,11 +857,12 @@ "center": 0.893696, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8937223553657532, + "nearest_peak_score": 0.8937026858329773, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1126712375884972", + "peak_in_radius": true, "seam": false, "x": 0.5576171875, "y": 0.5546875 @@ -817,11 +874,12 @@ "center": 0.889458, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8894680738449097, + "nearest_peak_score": 0.8894655704498291, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1126712375884972", + "peak_in_radius": true, "seam": false, "x": 0.2294921875, "y": 0.5546875 @@ -833,11 +891,12 @@ "center": 0.865858, "class": "tail", "nearest_peak_px": 0.0, - 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"nearest_peak_score": 0.8919329643249512, + "nearest_peak_score": 0.8919243812561035, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1288163659019705", + "peak_in_radius": true, "seam": false, "x": 0.4404296875, "y": 0.5390625 @@ -1617,11 +1724,12 @@ "center": 0.857872, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8578888773918152, + "nearest_peak_score": 0.8578627705574036, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1288163659019705", + "peak_in_radius": true, "seam": false, "x": 0.45703125, "y": 0.5390625 @@ -1632,12 +1740,13 @@ "cell": "neither", "center": 0.010458, "class": "tail", - "nearest_peak_px": 175.2, - "nearest_peak_score": 0.8919329643249512, + "nearest_peak_px": 42.9, + "nearest_peak_score": 0.5122259855270386, "null_med": 0.002314, "null_p95": 0.400246, "null_pct": 0.825, "pano": "1288163659019705", + "peak_in_radius": false, "seam": false, "x": 0.2791919130267519, "y": 0.6537373675722065 @@ -1648,12 +1757,13 @@ "cell": "neither", "center": 0.378504, "class": "tail", - 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"nearest_peak_score": 0.9164056777954102, + "nearest_peak_score": 0.9163845777511597, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1294705618516185", + "peak_in_radius": true, "seam": false, "x": 0.68359375, "y": 0.599609375 @@ -1713,11 +1826,12 @@ "center": 0.96603, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9660662412643433, + "nearest_peak_score": 0.9660443067550659, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.35546875, "y": 0.5234375 @@ -1729,11 +1843,12 @@ "center": 0.963532, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9635652303695679, + "nearest_peak_score": 0.9635457992553711, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.5576171875, "y": 0.568359375 @@ -1745,11 +1860,12 @@ "center": 0.896823, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8967987298965454, + "nearest_peak_score": 0.8967732191085815, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.32421875, "y": 0.5234375 @@ -1761,11 +1877,12 @@ "center": 0.890193, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.89020836353302, + "nearest_peak_score": 0.8901859521865845, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.92578125, "y": 0.599609375 @@ -1777,11 +1894,12 @@ "center": 0.799142, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7991482019424438, + "nearest_peak_score": 0.7991349101066589, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.8701171875, "y": 0.583984375 @@ -1793,11 +1911,12 @@ "center": 0.768035, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7680552005767822, + "nearest_peak_score": 0.768044114112854, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.8701171875, "y": 0.5546875 @@ -1809,11 +1928,12 @@ "center": 0.671669, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6716945171356201, + "nearest_peak_score": 0.6716797351837158, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.7919921875, "y": 0.5390625 @@ -1825,11 +1945,12 @@ "center": 0.116021, "class": "tail", "nearest_peak_px": 17.2, - "nearest_peak_score": 0.7991482019424438, + "nearest_peak_score": 0.7991349101066589, "null_med": 0.102378, "null_p95": 0.963532, "null_pct": 0.885, "pano": "1309093164014619", + "peak_in_radius": true, "seam": false, "x": 0.8864646402994791, "y": 0.5765656627308239 @@ -1841,11 +1962,12 @@ "center": 0.749987, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7499932050704956, + "nearest_peak_score": 0.7499725222587585, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1315788070079167", + "peak_in_radius": true, "seam": false, "x": 0.5888671875, "y": 0.5234375 @@ -1856,12 +1978,13 @@ "cell": "challenger_only", "center": 0.220484, "class": "tail", - "nearest_peak_px": 38.4, - "nearest_peak_score": 0.7499932050704956, + "nearest_peak_px": 3.9, + "nearest_peak_score": 0.24537207186222076, "null_med": 0.002646, "null_p95": 0.439927, "null_pct": 0.91, "pano": "1315788070079167", + "peak_in_radius": true, "seam": false, "x": 0.5610100948449337, "y": 0.47313130696614586 @@ -1873,11 +1996,12 @@ "center": 0.770554, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7705703973770142, + "nearest_peak_score": 0.7705532312393188, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1315844333300588", + "peak_in_radius": true, "seam": false, "x": 0.1904296875, "y": 0.5859375 @@ -1889,11 +2013,12 @@ "center": 0.89899, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8990136384963989, + "nearest_peak_score": 0.8989949822425842, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1327023138707493", + "peak_in_radius": true, "seam": false, "x": 0.41015625, "y": 0.5546875 @@ -1905,11 +2030,12 @@ "center": 0.698635, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6986466646194458, + "nearest_peak_score": 0.6986407041549683, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1327023138707493", + "peak_in_radius": true, "seam": false, "x": 0.6201171875, "y": 0.5703125 @@ -1920,12 +2046,13 @@ "cell": "challenger_only", "center": 0.129971, "class": "tail", - "nearest_peak_px": 72.5, - "nearest_peak_score": 0.6986466646194458, + "nearest_peak_px": 1.6, + "nearest_peak_score": 0.13228851556777954, "null_med": 0.011101, "null_p95": 0.872345, "null_pct": 0.825, "pano": "1327023138707493", + "peak_in_radius": true, "seam": false, "x": 0.5511111542672822, "y": 0.5386869118430397 @@ -1937,11 +2064,12 @@ "center": 0.929298, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9293081760406494, + "nearest_peak_score": 0.9292899370193481, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1335429861397399", + "peak_in_radius": true, "seam": false, "x": 0.24609375, "y": 0.646484375 @@ -1953,11 +2081,12 @@ "center": 0.914961, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9149634838104248, + "nearest_peak_score": 0.9149394035339355, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1335429861397399", + "peak_in_radius": true, "seam": false, "x": 0.9013671875, "y": 0.5546875 @@ -1969,11 +2098,12 @@ "center": 0.896301, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8963165879249573, + "nearest_peak_score": 0.8962975144386292, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1335429861397399", + "peak_in_radius": true, "seam": false, "x": 0.02734375, "y": 0.568359375 @@ -1985,11 +2115,12 @@ "center": 0.895466, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8955129384994507, + "nearest_peak_score": 0.8954881429672241, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1335429861397399", + "peak_in_radius": true, "seam": false, "x": 0.74609375, "y": 0.5703125 @@ -2001,11 +2132,12 @@ "center": 0.874735, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8747516870498657, + "nearest_peak_score": 0.8747368454933167, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1338631107629110", + "peak_in_radius": true, "seam": false, "x": 0.5576171875, "y": 0.5234375 @@ -2017,11 +2149,12 @@ "center": 0.207195, "class": "tail", "nearest_peak_px": 29.0, - "nearest_peak_score": 0.8747516870498657, + "nearest_peak_score": 0.8747368454933167, "null_med": 0.002086, "null_p95": 0.402981, "null_pct": 1.0, "pano": "1338631107629110", + "peak_in_radius": false, "seam": false, "x": 0.5325252463600852, "y": 0.4973737312085701 @@ -2033,11 +2166,12 @@ "center": 0.866864, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8668623566627502, + "nearest_peak_score": 0.8668447732925415, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1408179757063785", + "peak_in_radius": true, "seam": false, "x": 0.3779296875, "y": 0.630859375 @@ -2049,11 +2183,12 @@ "center": 1.0, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 1.003684401512146, + "nearest_peak_score": 1.0036526918411255, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1427673368387929", + "peak_in_radius": true, "seam": false, "x": 0.59765625, "y": 0.537109375 @@ -2065,11 +2200,12 @@ "center": 0.948419, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9484301805496216, + "nearest_peak_score": 0.9484100937843323, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1427673368387929", + "peak_in_radius": true, "seam": false, "x": 0.4873046875, "y": 0.5234375 @@ -2081,11 +2217,12 @@ "center": 0.863412, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8634457588195801, + "nearest_peak_score": 0.8634290099143982, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1427673368387929", + "peak_in_radius": true, "seam": false, "x": 0.33984375, "y": 0.5859375 @@ -2097,11 +2234,12 @@ "center": 0.772906, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7728960514068604, + "nearest_peak_score": 0.7728797793388367, "null_med": null, "null_p95": null, "null_pct": null, "pano": "1441274599876577", + "peak_in_radius": true, "seam": false, "x": 0.0654296875, "y": 0.5390625 @@ -2113,11 +2251,12 @@ "center": 0.231996, "class": "tail", "nearest_peak_px": 16.8, - "nearest_peak_score": 0.7728960514068604, + "nearest_peak_score": 0.7728797793388367, "null_med": 0.009665, "null_p95": 0.35217, "null_pct": 1.0, "pano": "1441274599876577", + "peak_in_radius": true, "seam": false, "x": 0.05026338366770002, "y": 0.5263919001449772 @@ -2128,12 +2267,13 @@ "cell": "neither", "center": 0.171911, "class": "tail", - "nearest_peak_px": 124.4, - "nearest_peak_score": 0.7728960514068604, + "nearest_peak_px": 4.3, + "nearest_peak_score": 0.18528473377227783, "null_med": 0.009683, "null_p95": 0.678158, "null_pct": 0.915, "pano": "1441274599876577", + "peak_in_radius": true, "seam": false, "x": 0.9440247579367571, "y": 0.5328431364003076 @@ -2145,11 +2285,12 @@ "center": 0.757466, "class": "tail", "nearest_peak_px": 0.0, - 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"nearest_peak_score": 0.8888956308364868, + "nearest_peak_score": 0.8888728618621826, "null_med": null, "null_p95": null, "null_pct": null, "pano": "559372759847379", + "peak_in_radius": true, "seam": false, "x": 0.2060546875, "y": 0.5859375 @@ -4065,11 +4325,12 @@ "center": 0.558839, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.5588424205780029, + "nearest_peak_score": 0.5588347911834717, "null_med": null, "null_p95": null, "null_pct": null, "pano": "563961506051153", + "peak_in_radius": true, "seam": false, "x": 0.53515625, "y": 0.5234375 @@ -4081,11 +4342,12 @@ "center": 0.763808, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7638235092163086, + "nearest_peak_score": 0.7638033032417297, "null_med": null, "null_p95": null, "null_pct": null, "pano": "591584463734236", + "peak_in_radius": true, "seam": false, "x": 0.09765625, "y": 0.5390625 @@ -4097,11 +4359,12 @@ "center": 0.990258, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9902909994125366, + "nearest_peak_score": 0.9902709126472473, "null_med": null, "null_p95": null, "null_pct": null, "pano": "593421160518191", + "peak_in_radius": true, "seam": false, "x": 0.23046875, "y": 0.6015625 @@ -4113,11 +4376,12 @@ "center": 0.915084, "class": "tail", "nearest_peak_px": 0.0, - 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"nearest_peak_score": 0.8338943719863892, + "nearest_peak_score": 0.8338901996612549, "null_med": null, "null_p95": null, "null_pct": null, "pano": "639645829096010", + "peak_in_radius": true, "seam": false, "x": 0.04296875, "y": 0.552734375 @@ -4289,11 +4563,12 @@ "center": 0.828106, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8281292915344238, + "nearest_peak_score": 0.8281025290489197, "null_med": null, "null_p95": null, "null_pct": null, "pano": "639645829096010", + "peak_in_radius": true, "seam": false, "x": 0.1044921875, "y": 0.5390625 @@ -4304,12 +4579,13 @@ "cell": "challenger_only", "center": 0.023782, "class": "faint_local", - "nearest_peak_px": 133.7, - "nearest_peak_score": 0.8281292915344238, + "nearest_peak_px": 42.5, + "nearest_peak_score": 0.14262360334396362, "null_med": 0.091109, "null_p95": 0.833894, "null_pct": 0.465, "pano": "639645829096010", + "peak_in_radius": false, "seam": false, "x": 0.23492423780036692, "y": 0.5502974155481681 @@ -4321,11 +4597,12 @@ "center": 0.834619, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8346372842788696, + "nearest_peak_score": 0.8346166610717773, "null_med": null, "null_p95": null, "null_pct": null, "pano": "662385816800591", + "peak_in_radius": true, "seam": false, "x": 0.40234375, "y": 0.5234375 @@ -4337,11 +4614,12 @@ "center": 0.604542, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6045513153076172, + "nearest_peak_score": 0.6045317649841309, "null_med": null, "null_p95": null, "null_pct": null, "pano": "662385816800591", + "peak_in_radius": true, "seam": false, "x": 0.5576171875, "y": 0.5390625 @@ -4353,11 +4631,12 @@ "center": 0.008665, "class": "tail", "nearest_peak_px": 44.1, - "nearest_peak_score": 0.8346372842788696, + "nearest_peak_score": 0.8346166610717773, "null_med": 0.008319, "null_p95": 0.604542, "null_pct": 0.825, "pano": "662385816800591", + "peak_in_radius": false, "seam": false, "x": 0.44464645848129736, "y": 0.5070707009055397 @@ -4369,11 +4648,12 @@ "center": 0.219653, "class": "tail", "nearest_peak_px": 36.0, - "nearest_peak_score": 0.6045513153076172, + "nearest_peak_score": 0.6045317649841309, "null_med": 0.011196, "null_p95": 0.631353, "null_pct": 0.935, "pano": "662385816800591", + "peak_in_radius": false, "seam": false, "x": 0.5236363821318656, "y": 0.5209091094045928 @@ -4385,11 +4665,12 @@ "center": 0.682507, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6825149059295654, + "nearest_peak_score": 0.6825098991394043, "null_med": null, "null_p95": null, "null_pct": null, "pano": "681333794968062", + "peak_in_radius": true, "seam": false, "x": 0.7060546875, "y": 0.537109375 @@ -4400,12 +4681,13 @@ "cell": "challenger_only", "center": 0.040765, "class": "tail", - "nearest_peak_px": 108.4, - "nearest_peak_score": 0.6825149059295654, + "nearest_peak_px": 19.0, + "nearest_peak_score": 0.06853001564741135, "null_med": 0.007319, "null_p95": 0.682507, "null_pct": 0.895, "pano": "681333794968062", + "peak_in_radius": true, "seam": false, "x": 0.6004040342388731, "y": 0.5500000184955018 @@ -4416,12 +4698,13 @@ "cell": "challenger_only", "center": 0.455115, "class": "tail", - "nearest_peak_px": 165.7, - "nearest_peak_score": 0.621621310710907, + "nearest_peak_px": 7.1, + "nearest_peak_score": 0.4787115454673767, "null_med": 0.004715, "null_p95": 0.600249, "null_pct": 0.935, "pano": "700668799338315", + "peak_in_radius": true, "seam": false, "x": 0.5558273567679088, "y": 0.5642667296707485 @@ -4433,11 +4716,12 @@ "center": 0.983572, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9836001396179199, + "nearest_peak_score": 0.9835737347602844, "null_med": null, "null_p95": null, "null_pct": null, "pano": "723487737079243", + "peak_in_radius": true, "seam": false, "x": 0.23046875, "y": 0.5546875 @@ -4449,11 +4733,12 @@ "center": 0.944982, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9450165629386902, + "nearest_peak_score": 0.9449897408485413, "null_med": null, "null_p95": null, "null_pct": null, "pano": "723487737079243", + "peak_in_radius": true, "seam": false, "x": 0.12109375, "y": 0.5234375 @@ -4465,11 +4750,12 @@ "center": 0.884595, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8846012353897095, + "nearest_peak_score": 0.8845933079719543, "null_med": null, "null_p95": null, "null_pct": null, "pano": "723487737079243", + "peak_in_radius": true, "seam": false, "x": 0.88671875, "y": 0.6328125 @@ -4481,11 +4767,12 @@ "center": 0.779342, "class": "tail", "nearest_peak_px": 117.4, - "nearest_peak_score": 0.9450165629386902, + "nearest_peak_score": 0.9449897408485413, "null_med": 0.012038, "null_p95": 0.944982, "null_pct": 0.975, "pano": "723487737079243", + "peak_in_radius": false, "seam": true, "x": 0.006868693033854166, "y": 0.5435353966915246 @@ -4497,11 +4784,12 @@ "center": 0.869796, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8698008060455322, + "nearest_peak_score": 0.8697847127914429, "null_med": null, "null_p95": null, "null_pct": null, "pano": "780391337668333", + "peak_in_radius": true, "seam": false, "x": 0.08203125, "y": 0.583984375 @@ -4513,11 +4801,12 @@ "center": 0.737678, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7376946806907654, + "nearest_peak_score": 0.7376779317855835, "null_med": null, "null_p95": null, "null_pct": null, "pano": "780391337668333", + "peak_in_radius": true, "seam": false, "x": 0.90234375, "y": 0.5390625 @@ -4528,12 +4817,13 @@ "cell": "challenger_only", "center": 0.097303, "class": "tail", - "nearest_peak_px": 61.6, - "nearest_peak_score": 0.7376946806907654, + "nearest_peak_px": 7.0, + "nearest_peak_score": 0.15252767503261566, "null_med": 0.001456, "null_p95": 0.737678, "null_pct": 0.845, "pano": "780391337668333", + "peak_in_radius": true, "seam": false, "x": 0.9624242609197443, "y": 0.5322222345525568 @@ -4545,11 +4835,12 @@ "center": 0.840681, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8406943082809448, + "nearest_peak_score": 0.8406736254692078, "null_med": null, "null_p95": null, "null_pct": null, "pano": "822353906794602", + "peak_in_radius": true, "seam": false, "x": 0.45703125, "y": 0.521484375 @@ -4561,11 +4852,12 @@ "center": 0.790651, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7906746864318848, + "nearest_peak_score": 0.7906543016433716, "null_med": null, "null_p95": null, "null_pct": null, "pano": "839655921736413", + "peak_in_radius": true, "seam": false, "x": 0.73828125, "y": 0.568359375 @@ -4576,12 +4868,13 @@ "cell": "challenger_only", "center": 0.408036, "class": "tail", - "nearest_peak_px": 104.9, - "nearest_peak_score": 0.7906746864318848, + "nearest_peak_px": 2.3, + "nearest_peak_score": 0.4212449789047241, "null_med": 0.000473, "null_p95": 0.787073, "null_pct": 0.945, "pano": "839655921736413", + "peak_in_radius": true, "seam": false, "x": 0.8406462580343929, "y": 0.5744880586392555 @@ -4593,11 +4886,12 @@ "center": 0.58873, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.5887415409088135, + "nearest_peak_score": 0.588718831539154, "null_med": null, "null_p95": null, "null_pct": null, "pano": "843075271274817", + "peak_in_radius": true, "seam": false, "x": 0.33984375, "y": 0.5390625 @@ -4609,11 +4903,12 @@ "center": 0.852076, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8520925641059875, + "nearest_peak_score": 0.8520784974098206, "null_med": null, "null_p95": null, "null_pct": null, "pano": "8726984263984099", + "peak_in_radius": true, "seam": false, "x": 0.87109375, "y": 0.5546875 @@ -4625,11 +4920,12 @@ "center": 0.93903, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9390338659286499, + "nearest_peak_score": 0.939026951789856, "null_med": null, "null_p95": null, "null_pct": null, "pano": "872856994942214", + "peak_in_radius": true, "seam": false, "x": 0.24609375, "y": 0.537109375 @@ -4641,11 +4937,12 @@ "center": 0.931913, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9319180250167847, + "nearest_peak_score": 0.9319252967834473, "null_med": null, "null_p95": null, "null_pct": null, "pano": "872856994942214", + "peak_in_radius": true, "seam": false, "x": 0.8466796875, "y": 0.568359375 @@ -4657,11 +4954,12 @@ "center": 0.928921, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9289493560791016, + "nearest_peak_score": 0.9289323091506958, "null_med": null, "null_p95": null, "null_pct": null, "pano": "872856994942214", + "peak_in_radius": true, "seam": false, "x": 0.65234375, "y": 0.599609375 @@ -4673,11 +4971,12 @@ "center": 0.918721, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9187254905700684, + "nearest_peak_score": 0.9186991453170776, "null_med": null, "null_p95": null, "null_pct": null, "pano": "872856994942214", + "peak_in_radius": true, "seam": false, "x": 0.71484375, "y": 0.583984375 @@ -4689,11 +4988,12 @@ "center": 0.804447, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8044468760490417, + "nearest_peak_score": 0.8044247627258301, "null_med": null, "null_p95": null, "null_pct": null, "pano": "872856994942214", + "peak_in_radius": true, "seam": false, "x": 0.19140625, "y": 0.5390625 @@ -4705,11 +5005,12 @@ "center": 0.920193, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9202104806900024, + "nearest_peak_score": 0.9201868176460266, "null_med": null, "null_p95": null, "null_pct": null, "pano": "893023542323690", + "peak_in_radius": true, "seam": false, "x": 0.6044921875, "y": 0.599609375 @@ -4721,11 +5022,12 @@ "center": 0.899801, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8998149633407593, + "nearest_peak_score": 0.8997887372970581, "null_med": null, "null_p95": null, "null_pct": null, "pano": "893023542323690", + "peak_in_radius": true, "seam": false, "x": 0.55078125, "y": 0.552734375 @@ -4737,11 +5039,12 @@ "center": 0.920857, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.920896053314209, + "nearest_peak_score": 0.9208822250366211, "null_med": null, "null_p95": null, "null_pct": null, "pano": "911337534530260", + "peak_in_radius": true, "seam": false, "x": 0.6123046875, "y": 0.6015625 @@ -4753,11 +5056,12 @@ "center": 0.856427, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8564420342445374, + "nearest_peak_score": 0.8564238548278809, "null_med": null, "null_p95": null, "null_pct": null, "pano": "911337534530260", + "peak_in_radius": true, "seam": false, "x": 0.91015625, "y": 0.583984375 @@ -4769,11 +5073,12 @@ "center": 0.808182, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8081934452056885, + "nearest_peak_score": 0.8081760406494141, "null_med": null, "null_p95": null, "null_pct": null, "pano": "911337534530260", + "peak_in_radius": true, "seam": false, "x": 0.2763671875, "y": 0.5234375 @@ -4785,11 +5090,12 @@ "center": 0.176682, "class": "tail", "nearest_peak_px": 23.5, - "nearest_peak_score": 0.6433027982711792, + "nearest_peak_score": 0.6432969570159912, "null_med": 0.016102, "null_p95": 0.856427, "null_pct": 0.885, "pano": "911337534530260", + "peak_in_radius": false, "seam": false, "x": 0.7846464584812973, "y": 0.6185858524206913 @@ -4801,11 +5107,12 @@ "center": 1.0, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 1.0076472759246826, + "nearest_peak_score": 1.0076181888580322, "null_med": null, "null_p95": null, "null_pct": null, "pano": "925077602773417", + "peak_in_radius": true, "seam": false, "x": 0.70703125, "y": 0.599609375 @@ -4817,11 +5124,12 @@ "center": 0.895271, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8952727317810059, + "nearest_peak_score": 0.8952723145484924, "null_med": null, "null_p95": null, "null_pct": null, "pano": "925077602773417", + "peak_in_radius": true, "seam": false, "x": 0.25390625, "y": 0.5703125 @@ -4833,11 +5141,12 @@ "center": 0.706292, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.7063033580780029, + "nearest_peak_score": 0.7062855958938599, "null_med": null, "null_p95": null, "null_pct": null, "pano": "925077602773417", + "peak_in_radius": true, "seam": false, "x": 0.35546875, "y": 0.6171875 @@ -4849,11 +5158,12 @@ "center": 0.681016, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6810370683670044, + "nearest_peak_score": 0.6810267567634583, "null_med": null, "null_p95": null, "null_pct": null, "pano": "925077602773417", + "peak_in_radius": true, "seam": false, "x": 0.7529296875, "y": 0.5703125 @@ -4865,11 +5175,12 @@ "center": 0.965541, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9655765295028687, + "nearest_peak_score": 0.9655410051345825, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.58984375, "y": 0.599609375 @@ -4881,11 +5192,12 @@ "center": 0.943558, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9435607194900513, + "nearest_peak_score": 0.9435520768165588, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.6669921875, "y": 0.599609375 @@ -4897,11 +5209,12 @@ "center": 0.927461, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9274734258651733, + "nearest_peak_score": 0.9274592399597168, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.12890625, "y": 0.5859375 @@ -4913,11 +5226,12 @@ "center": 0.922892, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.9229074716567993, + "nearest_peak_score": 0.9228776693344116, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.39453125, "y": 0.583984375 @@ -4929,11 +5243,12 @@ "center": 0.898126, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8981457948684692, + "nearest_peak_score": 0.8981223106384277, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.4326171875, "y": 0.583984375 @@ -4945,11 +5260,12 @@ "center": 0.823908, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8239241242408752, + "nearest_peak_score": 0.8239020109176636, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.9169921875, "y": 0.599609375 @@ -4961,11 +5277,12 @@ "center": 0.810013, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8100234270095825, + "nearest_peak_score": 0.8100091814994812, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.0966796875, "y": 0.583984375 @@ -4977,11 +5294,12 @@ "center": 0.8086, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8086158037185669, + "nearest_peak_score": 0.8086097240447998, "null_med": null, "null_p95": null, "null_pct": null, "pano": "934739365184374", + "peak_in_radius": true, "seam": false, "x": 0.87109375, "y": 0.599609375 @@ -4993,11 +5311,12 @@ "center": 0.899075, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8990710973739624, + "nearest_peak_score": 0.8990524411201477, "null_med": null, "null_p95": null, "null_pct": null, "pano": "996959015535106", + "peak_in_radius": true, "seam": false, "x": 0.38671875, "y": 0.5546875 @@ -5009,11 +5328,12 @@ "center": 0.877263, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.8773050904273987, + "nearest_peak_score": 0.8772670030593872, "null_med": null, "null_p95": null, "null_pct": null, "pano": "996959015535106", + "peak_in_radius": true, "seam": false, "x": 0.6044921875, "y": 0.552734375 @@ -5025,11 +5345,12 @@ "center": 0.634536, "class": "tail", "nearest_peak_px": 0.0, - "nearest_peak_score": 0.6345473527908325, + "nearest_peak_score": 0.6345356106758118, "null_med": null, "null_p95": null, "null_pct": null, "pano": "996959015535106", + "peak_in_radius": true, "seam": false, "x": 0.5498046875, "y": 0.5078125 @@ -5040,12 +5361,13 @@ "cell": "challenger_only", "center": 0.092798, "class": "tail", - "nearest_peak_px": 62.9, - "nearest_peak_score": 0.8990710973739624, + "nearest_peak_px": 4.9, + "nearest_peak_score": 0.09808053821325302, "null_med": 0.007513, "null_p95": 0.877263, "null_pct": 0.81, "pano": "996959015535106", + "peak_in_radius": true, "seam": false, "x": 0.44606062455610795, "y": 0.5233333703243371 diff --git a/scripts/analysis/complementarity.py b/scripts/analysis/complementarity.py index ab7591a1..905124c8 100644 --- a/scripts/analysis/complementarity.py +++ b/scripts/analysis/complementarity.py @@ -7,6 +7,17 @@ Read-only: RampNet's side comes from the bundle's committed detections and the challenger's from ``.model_cache``, so this never runs a model or spends anything. +**Which RampNet, though?** The committed bundle detections are the *shipped* operating +point — on richmond every one scores >= 0.5519 — while this document's own +recommendation since #54/#55 (PR #79) is **0.30**. Those are different models for this +purpose: at 0.30 RampNet finds 257 of richmond's 310 ramps instead of 238, so 19 of the +"misses" a challenger gets credit for recovering are ramps RampNet already has and the +shipped threshold is discarding. ``--rampnet-op-threshold`` re-sources RampNet's side +from the committed ``analysis_out/op_cache/.json`` floor peaks at a given +threshold, which is how you ask "what is the complementary gain at the operating point +we would actually deploy?". Default is the bundle records, so the published roster +numbers and the #35 gate's committed results are unchanged. + **The oracle-union recall is a ceiling, not a proposal.** It assumes you could keep every right call and discard every wrong one, which no combiner can do. The FP arithmetic printed at the end is the counterweight, and for a low-precision @@ -42,6 +53,7 @@ _xy, prediction_confidence) from compare import load_bundle, DetectionCache, cache_key # noqa: E402 from detectors import build_detector, parse_model_spec, PROVIDERS # noqa: E402 +from operating_point_curve import CACHE_DIR, read_cache # noqa: E402 def matched_gt(preds, gt_points, radius_sq): @@ -145,6 +157,13 @@ def main(): ap.add_argument("split", nargs="?", default="richmond", help="Benchmark split name (default richmond).") ap.add_argument("--cache-dir", default=os.path.join(REPO, ".model_cache")) + ap.add_argument("--rampnet-op-threshold", type=float, default=None, + help="Score RampNet from analysis_out/op_cache/.json floor " + "peaks at this threshold instead of the bundle's committed " + "detections. The bundle is the SHIPPED point (>=0.5519 on " + "richmond); this document recommends 0.30, and the gap " + "changes who gets credit for a recovery. Default: the bundle, " + "so published numbers are unchanged.") ap.add_argument("--radius", type=float, default=0.022) ap.add_argument("--tiling", choices=["perspective", "none"], default="perspective") ap.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"), @@ -165,6 +184,13 @@ def main(): cache = DetectionCache(args.cache_dir) radius_sq = radius_sq_for(args.radius) + floor_peaks = None + if args.rampnet_op_threshold is not None: + cached, _ = read_cache(os.path.join(CACHE_DIR, f"{args.split}.json")) + floor_peaks = {pd["pano"]: pd["preds"] for pd in cached} + print(f"rampnet re-sourced from op_cache at >= {args.rampnet_op_threshold} " + f"(bundle records are the shipped point and are NOT used)\n") + n = both = r_only = c_only = neither = 0 r_fp = c_fp = 0 panos = missing = 0 @@ -179,8 +205,12 @@ def main(): if cp is None: missing += 1 continue - rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) - for d in records[pid]["detections"]] + if floor_peaks is not None: + rp = [p for p in floor_peaks.get(pid, []) + if p[2] >= args.rampnet_op_threshold] + else: + rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) + for d in records[pid]["detections"]] mr, mc = matched_gt(rp, gt.gt_points, radius_sq), matched_gt(cp, gt.gt_points, radius_sq) for i in range(len(gt.gt_points)): r, c = i in mr, i in mc @@ -201,24 +231,26 @@ def main(): r_tp, c_tp, union = both + r_only, both + c_only, both + r_only + c_only r_miss = c_only + neither - print(f"{args.split} complementarity — rampnet vs {label} " + rn = ("rampnet" if floor_peaks is None + else f"rampnet@{args.rampnet_op_threshold:g}") + print(f"{args.split} complementarity — {rn} vs {label} " f"({panos} recall-eligible panos, {n} GT ramps" + (f"; {missing} panos missing from cache" if missing else "") + ")\n") - print(f" rampnet recall {r_tp / n:.3f} ({r_tp}/{n})") + print(f" {rn:18s} recall {r_tp / n:.3f} ({r_tp}/{n})") print(f" {label[:18]:18s} recall {c_tp / n:.3f} ({c_tp}/{n})") print(f" ORACLE-UNION recall {union / n:.3f} ({union}/{n}) " f"<- ceiling if you could keep every right call") print() print(f" found by BOTH {both:4d} ({both / n:.1%})") - print(f" rampnet ONLY {r_only:4d} ({r_only / n:.1%})") + print(f" {rn[:14]:14s} ONLY {r_only:4d} ({r_only / n:.1%})") print(f" {label[:14]:14s} ONLY {c_only:4d} ({c_only / n:.1%}) " f"<- complementary gain (rampnet-miss n challenger-hit)") print(f" found by NEITHER {neither:4d} ({neither / n:.1%}) " f"<- hard misses, no model helps") print() - print(f" Union recall lift over rampnet: +{(union - r_tp) / n:.3f} ({c_only} ramps)") + print(f" Union recall lift over {rn}: +{(union - r_tp) / n:.3f} ({c_only} ramps)") if r_miss: - print(f" Of rampnet's {r_miss} misses, {label} recovers {c_only} " + print(f" Of {rn}'s {r_miss} misses, {label} recovers {c_only} " f"({c_only / r_miss:.0%}); {neither} nobody finds") mean_null, max_null = complementary_null(shift_rows, radius_sq) exp = mean_null * r_miss @@ -236,12 +268,12 @@ def main(): r_p = r_tp / (r_tp + r_fp) if r_tp + r_fp else 0.0 r_f1 = 2 * r_p * (r_tp / n) / (r_p + r_tp / n) if r_p + r_tp / n else 0.0 print() - print(f" FP cost on these panos: rampnet {r_fp} | {label} {c_fp}" + print(f" FP cost on these panos: {rn} {r_fp} | {label} {c_fp}" f" (a naive union pays ~both)") - print(f" rampnet alone: P {r_p:.3f} R {r_tp / n:.3f} F1 {r_f1:.3f}") + print(f" {rn} alone: P {r_p:.3f} R {r_tp / n:.3f} F1 {r_f1:.3f}") print(f" NAIVE UNION: P {u_p:.3f} R {u_r:.3f} F1 {u_f1:.3f}" f" <- precision is a lower bound (no FP dedup)") - print(f" => a naive union {'BEATS' if u_f1 > r_f1 else 'LOSES TO'} rampnet alone " + print(f" => a naive union {'BEATS' if u_f1 > r_f1 else 'LOSES TO'} {rn} alone " f"on F1 ({u_f1:.3f} vs {r_f1:.3f})") From 4c192ca45a325a1768c88df8a28b1a7ce875a465 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:31:25 -0700 Subject: [PATCH 08/10] cascade_gate: --rampnet-op-threshold, so the gate can be read at 0.30 too (#126) same reasoning as the complementarity flag: the bundle detections are the shipped point and the recommendation is 0.30, and the cells move between them. peak_in_radius and the heatmap probe are threshold-independent, but which cell a ramp lands in is not. Co-Authored-By: Claude Opus 5 --- scripts/analysis/cascade_gate.py | 48 ++++++++++++++++++++++---------- 1 file changed, 33 insertions(+), 15 deletions(-) diff --git a/scripts/analysis/cascade_gate.py b/scripts/analysis/cascade_gate.py index 87c06246..f24ebc47 100644 --- a/scripts/analysis/cascade_gate.py +++ b/scripts/analysis/cascade_gate.py @@ -148,6 +148,12 @@ def main(argv=None): p.add_argument("--panos-root", default=REPO, help="Checkout holding benchmark//panos/ (a worktree will not).") p.add_argument("--cache-dir", default=os.path.join(REPO, ".model_cache")) + p.add_argument("--rampnet-op-threshold", type=float, default=None, + help="Define RampNet's hits from op_cache floor peaks at this " + "threshold instead of the bundle's shipped detections " + "(>=0.5519 on richmond). This document recommends 0.30, and " + "the cells move: 19 of the shipped point's misses are ramps " + "RampNet already has. Default: the bundle, as published.") p.add_argument("--radius", type=float, default=0.022) p.add_argument("--tiling", choices=["perspective", "none"], default="perspective") p.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"), default=None, @@ -174,6 +180,20 @@ def main(argv=None): cache = DetectionCache(args.cache_dir) radius_sq = radius_sq_for(args.radius) + # Floor peaks (>= 0.05). Used for the sub-threshold probe always, and to + # DEFINE rampnet's hits when --rampnet-op-threshold is given. + floor_peaks, floor_src = {}, "op_cache" + try: + cached, _ = read_cache(os.path.join(CACHE_DIR, f"{args.split}.json")) + for pd in cached: + floor_peaks[pd["pano"]] = pd["preds"] + except (OSError, ValueError, KeyError): + floor_src = ("MISSING (fell back to bundle records -- distances are to the " + "shipped operating point, not the 0.05 floor)") + if args.rampnet_op_threshold is not None: + sys.exit("--rampnet-op-threshold needs analysis_out/op_cache/" + f"{args.split}.json, which could not be read.") + # ---- partition every GT ramp into a complementarity cell ------------------ sites, missing = [], 0 for pid, entry in verdicts.items(): @@ -185,8 +205,12 @@ def main(argv=None): if cp is None: missing += 1 continue - rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) - for d in records[pid]["detections"]] + if args.rampnet_op_threshold is not None: + rp = [q for q in floor_peaks.get(pid, []) + if q[2] >= args.rampnet_op_threshold] + else: + rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) + for d in records[pid]["detections"]] mr = matched_gt(rp, gt.gt_points, radius_sq) mc = matched_gt(cp, gt.gt_points, radius_sq) for i, (gx, gy) in enumerate(gt.gt_points): @@ -198,18 +222,6 @@ def main(argv=None): if not sites: sys.exit("No sites -- is the challenger cached for this split/input size?") - # Floor peaks (>= 0.05) for the "did the model say anything sub-threshold here?" - # question. See the docstring: the bundle records are the SHIPPED operating point - # and would answer a different question. - floor_peaks, floor_src = {}, "op_cache" - try: - cached, _ = read_cache(os.path.join(CACHE_DIR, f"{args.split}.json")) - for pd in cached: - floor_peaks[pd["pano"]] = pd["preds"] - except (OSError, ValueError, KeyError): - floor_src = "MISSING (fell back to bundle records -- distances are to the "\ - "shipped operating point, not the 0.05 floor)" - by_pano = {} for s in sites: by_pano.setdefault(s["pano"], []).append(s) @@ -218,7 +230,9 @@ def main(argv=None): panos = panos[:args.limit] counts = {c: sum(1 for s in sites if s["cell"] == c) for c in CELLS} - print(f"=== Cascade gate: rampnet heatmap at {label}'s recoveries " + rn = ("rampnet" if args.rampnet_op_threshold is None + else f"rampnet@{args.rampnet_op_threshold:g}") + print(f"=== Cascade gate: {rn} heatmap at {label}'s recoveries " f"({args.split}, {len(sites)} GT ramps in {len(by_pano)} panos) ===") print(" cells: " + " ".join(f"{c}={counts[c]}" for c in CELLS), flush=True) @@ -227,6 +241,9 @@ def main(argv=None): print(f" device={device} model=projectsidewalk/rampnet-model " f"(single-pass fp32, as op_cache)", flush=True) print(f" floor peaks (>=0.05) from: {floor_src}", flush=True) + print(f" rampnet hits defined by: " + + ("bundle records (shipped point)" if args.rampnet_op_threshold is None + else f"op_cache >= {args.rampnet_op_threshold:g}"), flush=True) rng = random.Random(NULL_SEED) rows, skipped = [], 0 @@ -300,6 +317,7 @@ def main(argv=None): if args.json_out: payload = {"split": args.split, "challenger": label, + "rampnet_op_threshold": args.rampnet_op_threshold, "vistas_input_size": args.vistas_input_size, "radius": args.radius, "null_trials": NULL_TRIALS, "null_seed": NULL_SEED, "n_sites": len(rows), From 730baffaa243b4e907ef63238d6cf64ff228d928 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:37:33 -0700 Subject: [PATCH 09/10] Cascade gate: live, but the ceiling is ~19 ramps -- and a third of the gain was ours (#126) Two findings, one of which corrects my own headline from earlier in this PR. 1. THE OPERATING-POINT CORRECTION. The complementarity read scored RampNet from the committed bundle detections, which are the SHIPPED point (>=0.5519 on richmond), while this document has recommended 0.30 since #54/#55 (PR #79). Verified op_cache is the same source before re-basing: filtered at 0.5519 it reproduces the published row exactly (238/9/72). At 0.30 it gives 257/28/53, P 0.9018 R 0.8290 F1 0.8639, matching committed corrected_at_0.3.csv. Re-based, the challenger recovers 38 of 53 misses, ~30 after the null -- not 54 of 72 / ~44. About 14 of the ramps it got credit for are ramps RampNet already has at the point we recommend. The recovery RATE barely moves (75% -> 72%), which is the honest read: there are simply fewer misses to find. 2. THE CASCADE CEILING. Of the 38 genuinely-complementary ramps at 0.30: 19 floor peak in radius at 0.05-0.30 -> promotable, the real target 4 floor peak >=0.30 but unmatched -> greedy matcher gave it to an adjacent GT; a matcher/sigma problem (#130), not a threshold one 15 no floor peak at all -> nothing to promote; act 0.215 is unpeaked mass So the cascade is LIVE with a ceiling of ~19 ramps, +6.1 recall points (0.829 -> 0.890), before an unmeasured FP cost. A long way below the 54 the raw complementarity suggested. Also a negative worth keeping: RampNet activation does NOT discriminate. challenger_only sits at null percentile 0.88 and the hard core at 0.925 -- the ramps nobody finds look stronger on raw heatmap mass. There is no self-gating shortcut that skips the second model. Co-Authored-By: Claude Opus 5 --- .gitignore | 1 + analysis_out/cascade_gate_op030.json | 5383 ++++++++++++++++++++++++++ docs/model_comparison.md | 71 + 3 files changed, 5455 insertions(+) create mode 100644 analysis_out/cascade_gate_op030.json diff --git a/.gitignore b/.gitignore index 26a53f29..d8e4814f 100644 --- a/.gitignore +++ b/.gitignore @@ -92,6 +92,7 @@ analysis_out/op/* !analysis_out/silent_activation.json # The #126 cascade go/no-go: rampnet's heatmap at the ramps a challenger recovers. !analysis_out/cascade_gate.json +!analysis_out/cascade_gate_op030.json # Generated tagging pages live next to their (committed) galleries; regenerate with # scripts/analysis/make_tagger.py rather than tracking a build artifact. diff --git a/analysis_out/cascade_gate_op030.json b/analysis_out/cascade_gate_op030.json new file mode 100644 index 00000000..1a5be6f5 --- /dev/null +++ b/analysis_out/cascade_gate_op030.json @@ -0,0 +1,5383 @@ +{ + "cells": [ + { + "act_median": 0.8819, + "argmax_off_px_median": 0.0, + "cell": "both", + "center_median": 0.8819, + "class_share": { + "absent": 0.0, + "faint_local": 0.0, + "tail": 1.0 + }, + "classes": { + "absent": 0, + "faint_local": 0, + "tail": 236 + }, + "n": 236, + "nearest_peak_px_median": 0.0, + "peak_in_radius": 236, + "peak_in_radius_score_median": 0.8819, + "peak_in_radius_share": 1.0, + "seam": 5 + }, + { + "act_median": 0.8407, + "argmax_off_px_median": 0.0, + "cell": "rampnet_only", + "center_median": 0.8086, + "class_share": { + "absent": 0.0, + "faint_local": 0.0, + "tail": 1.0 + }, + "classes": { + "absent": 0, + "faint_local": 0, + "tail": 21 + }, + "n": 21, + "nearest_peak_px_median": 0.0, + "peak_in_radius": 21, + "peak_in_radius_score_median": 0.8086, + "peak_in_radius_share": 1.0, + "seam": 0 + }, + { + "above_null_p95": 9, + "act_median": 0.2152, + "argmax_off_px_median": 19.0, + "cell": "challenger_only", + "center_median": 0.0973, + "class_share": { + "absent": 0.0, + "faint_local": 0.079, + "tail": 0.921 + }, + "classes": { + "absent": 0, + "faint_local": 3, + "tail": 35 + }, + "n": 38, + "nearest_peak_px_median": 20.7, + "null_med_median": 0.0087, + "null_pct_median": 0.88, + "peak_in_radius": 23, + "peak_in_radius_score_median": 0.2025, + "peak_in_radius_share": 0.605, + "seam": 1 + }, + { + "above_null_p95": 5, + "act_median": 0.4595, + "argmax_off_px_median": 19.2, + "cell": "neither", + "center_median": 0.1533, + "class_share": { + "absent": 0.0, + "faint_local": 0.0, + "tail": 1.0 + }, + "classes": { + "absent": 0, + "faint_local": 0, + "tail": 15 + }, + "n": 15, + "nearest_peak_px_median": 17.2, + "null_med_median": 0.0054, + "null_pct_median": 0.925, + "peak_in_radius": 11, + "peak_in_radius_score_median": 0.4595, + "peak_in_radius_share": 0.733, + "seam": 0 + } + ], + "challenger": "mask2former-vistas-curb-cut", + "n_panos": 92, + "n_sites": 310, + "null_seed": 20260731, + "null_trials": 200, + "radius": 0.022, + "rampnet_op_threshold": 0.3, + "sites": [ + { + 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instrument. **Not run, not costed here.** framing — at 3.6 FP/pano against RampNet's 0.07 they are a ~50× review burden, so "FPs are cheap" is a claim about the labeling workflow that would need its own justification at this ratio. +#### The operating-point correction: a third of that gain is RampNet's own + +**Everything above scores RampNet from the committed bundle detections, which are the *shipped* +operating point — on richmond every one of them is ≥ 0.5519. This document has recommended +**0.30** since #54/#55 (PR #79).** For a complementarity read those are different models, and the +difference decides who gets credit for a recovery. + +Checked before relying on it: `analysis_out/op_cache/richmond.json` filtered at ≥ 0.5519 +reproduces the published row **exactly** (P 0.9636 / R 0.7677 / F1 0.8546, 238/9/72), so it is the +same source. At 0.30 those same peaks give **P 0.9018 / R 0.8290 / F1 0.8639, 257/28/53** — +matching the committed `analysis_out/op/corrected_at_0.3.csv`. `complementarity.py +--rampnet-op-threshold 0.30` re-bases the gate on it: + +| | rampnet @0.55 (published) | **rampnet @0.30 (recommended)** | +|---|---:|---:| +| rampnet recall | 0.768 (238) | **0.829 (257)** | +| rampnet F1 | 0.855 | **0.864** | +| rampnet misses | 72 | **53** | +| challenger recovers | 54 (75%) | **38 (72%)** | +| **attributable after the null** | ~44 | **~30** | +| found by NEITHER | 18 | **15** | +| oracle-union recall | 0.942 | 0.952 | +| naive union F1 | 0.555 | 0.549 | + +**So ~14 of the ramps the challenger got credit for recovering are ramps RampNet already has at +the operating point we recommend — the shipped threshold was discarding them.** The deployable +complementary gain is **~30, not ~44**. The recovery *rate* barely moves (75% → 72%), which is the +honest way to read it: the challenger is not preferentially finding the easy sub-threshold ones, +there are simply fewer misses to find. And a naive union stays dead against the stronger baseline +(0.549 vs 0.864). + +#### The cascade gate: live, but the ceiling is ~19 ramps, not 54 + +`scripts/analysis/cascade_gate.py` (new) asks the one question that decides whether a gated +cascade is possible at all: **at the ramps the challenger recovers, does RampNet already produce +something a prior could promote?** It partitions all 310 GT ramps into the four cells and reads +RampNet's heatmap at each, reusing #46 Phase 1's instrument verbatim (`site_profile`, +`null_percentile`, `nearest_peak`, `class_of`) so the numbers are comparable to that phase. +Read pre-registered on #126 before running. Artifacts: `analysis_out/cascade_gate.json` (shipped +point) and `analysis_out/cascade_gate_op030.json` (recommended point). + +At **rampnet@0.30**, of the 38 genuinely-complementary ramps: + +| what RampNet has there | n | what it means | +|---|---:|---| +| floor peak in radius, **0.05–0.30** | **19** | **promotable** — a peak exists, below threshold. This is the cascade's real target. | +| floor peak in radius, ≥0.30 but unmatched | 4 | the greedy matcher gave that peak to an **adjacent GT**. A matcher/σ problem (#130), not a threshold one. | +| no floor peak in radius | 15 | nothing to promote. `act` is 0.215 median — unpeaked heatmap mass `peak_local_max` never called a maximum. | + +**So the cascade is live and its ceiling is ~19 ramps on richmond — +6.1 recall points (0.829 → +0.890) before any false-positive cost, which is unmeasured.** That is a real number and it is a +long way below the 54 the raw complementarity suggested. Two-fifths of the recoverable set has no +peak to raise, and a further tenth is a matching bug wearing a threshold costume. + +**A negative worth recording: RampNet's own activation does not tell you which misses are +recoverable.** `challenger_only` sits at null percentile **0.88** and the hard-core `neither` at +**0.925** — the ramps *nobody* finds look, if anything, *stronger* on raw heatmap mass than the +ones the challenger recovers (they contain 6 of 15 matcher-claimed peaks ≥0.30, which inflates +it). Median argmax offset is 19.0 px inside a 22.5 px radius, i.e. near the window edge rather +than on the ramp. So there is no cheap self-gating shortcut: you cannot skip the second model and +find these by looking harder at RampNet's confidence. Against the pre-registered rule this is the +**PARTIAL** branch — signal present, but not at the site — and the peak-level column, not the +activation, is what supplies the bounded answer. + +**What would have to be true for the cascade to pay.** Promoting sub-0.30 peaks gated on +challenger candidates also promotes them wherever the challenger fires on a driveway and RampNet +has a faint bump — and 442 of the challenger's 716 boxes are false positives. That cost is **not +measured here**, so "+6.1 recall points" is a ceiling on the benefit with the cost still blank. +The next step, if this is ever picked up, is to build the gate and score it, not to reason further +about it. + ##### Reproducing it ```bash From 54a98c62130ce13dc980f665e66daac2f55bd299 Mon Sep 17 00:00:00 2001 From: Jon Froehlich Date: Tue, 18 Aug 2026 16:59:12 -0700 Subject: [PATCH 10/10] Record the seam exposure: bounded at 1 ramp in 38 (#126, #132) This work branched at 5e20d11, before eccadda (wrap the 360 seam in the matcher) and f4c71c8 (peaks_to_dets dropped peaks beside the seam) landed on main. Rather than argue that away, measured it. The greedy match used here does not wrap. score_pano own docstring records that wrapping moves no metric on any committed split while #130 duplicate ground truth masks it, so aggregates are safe -- but a cell assignment is finer-grained than an aggregate. And analysis_out/op_cache/richmond.json is UNCHANGED by those commits, so it was never regenerated after the peaks_to_dets fix and can still be missing peaks beside the seam; that can only UNDERSTATE the promotable count. Measured: 6 of richmond 310 GT ramps straddle the seam, and only ONE is in challenger_only. The other five are in both, where neither fix can move the partition in a direction that matters. Worst case for the headline is one ramp in 38 and no conclusion turns on it. Re-running on a post-#140 main retires the caveat instead of bounding it. Co-Authored-By: Claude Opus 5 --- docs/model_comparison.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/docs/model_comparison.md b/docs/model_comparison.md index c45574c1..087ccc0f 100644 --- a/docs/model_comparison.md +++ b/docs/model_comparison.md @@ -1500,6 +1500,25 @@ measured here**, so "+6.1 recall points" is a ceiling on the benefit with the co The next step, if this is ever picked up, is to build the gate and score it, not to reason further about it. +**Seam exposure, stated because these numbers predate the #132 seam fixes.** This work branched at +`5e20d11`, before `eccadda` (wrap the 360° seam in the matcher) and `f4c71c8` (`peaks_to_dets` +dropped peaks beside the seam) landed. Two consequences, both bounded by measurement rather than +argued away: + +* The greedy match used here does not wrap. `score_pano`'s own docstring records that wrapping + *"moves no metric on any committed split"* while #130's duplicate ground truth masks it, so the + aggregate P/R/F1 are unaffected — but a cell assignment is finer-grained than an aggregate. +* `analysis_out/op_cache/richmond.json` is **unchanged by those commits**, i.e. it was not + regenerated after the `peaks_to_dets` fix, so it can still be missing peaks that sit beside the + seam. That would make a site read "no floor peak in radius" when one exists — it can only + *understate* the promotable count, never inflate it. + +**Measured exposure: 6 of richmond's 310 GT ramps straddle the seam, and only 1 of them is in +`challenger_only`** (the other 5 are in `both`, where neither fix can move the partition in a +direction that matters). So the worst case for the headline is one ramp in 38, and no conclusion +here turns on it. Re-running on a post-#140 main is the clean way to retire the caveat rather than +bound it. + ##### Reproducing it ```bash