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+laurens_mapillary,0.9,0.95,10,0,1.0,0.7224598312333834,1.0 +laurens_mapillary,0.95,1.0,0,0,,, budapest_district5,0.05,0.1,7,107,0.06140350877192982,0.030058899274456374,0.12134434032853611 budapest_district5,0.1,0.15,7,52,0.11864406779661017,0.058675082779140006,0.22523875773415053 budapest_district5,0.15,0.2,11,39,0.22,0.12753786224302288,0.35241815689459555 diff --git a/analysis_out/op/confidence_calibration.json b/analysis_out/op/confidence_calibration.json index 101347a9..d91d2e76 100644 --- a/analysis_out/op/confidence_calibration.json +++ b/analysis_out/op/confidence_calibration.json @@ -1632,7 +1632,7 @@ ] } ], - "laurens": [ + "laurens_mapillary": [ { "lo": 0.05, "hi": 0.1, @@ -2562,7 +2562,7 @@ "annapolis", "paterson", "gainesville", - "laurens" + "laurens_mapillary" ], "bins": [ { diff --git a/analysis_out/op/low_floor_sweep.csv b/analysis_out/op/low_floor_sweep.csv index 558c9dff..70f91edb 100644 --- a/analysis_out/op/low_floor_sweep.csv +++ 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+laurens_mapillary,0.85,0.96,0.0963855421686747,0.17518248175182483,0.8045552759497747,0.9929039496133008,0.06562746137893502,0.13940841879268231,24,1,225,0,0.26595744680851063,94 +laurens_mapillary,0.9,1.0,0.040160642570281124,0.07722007722007722,0.7224598312333834,1.0,0.021958086517276817,0.0723365232304862,10,0,239,0,0.10638297872340426,94 budapest_district5,0.05,0.40754039497307004,0.7566666666666667,0.52975495915986,0.36750098633164935,0.4488464458131845,0.7050638169697229,0.8017792222981063,227,330,73,29,4.688,125 budapest_district5,0.1,0.4966139954853273,0.7333333333333333,0.5921938088829071,0.45028353526548665,0.5430026762242226,0.6805715919237879,0.7801948074106021,220,223,80,24,3.736,125 budapest_district5,0.15,0.5546875,0.71,0.6228070175438596,0.5046792744832106,0.6036123579770515,0.6562534723961281,0.7584362870048228,213,171,87,23,3.256,125 diff --git a/analysis_out/op/recall_by_distance.csv b/analysis_out/op/recall_by_distance.csv index ec387033..147b116c 100644 --- a/analysis_out/op/recall_by_distance.csv +++ b/analysis_out/op/recall_by_distance.csv @@ -20,9 +20,9 @@ paterson,far (>25 m),88,0.4318181818181818,0.5227272727272727,8 gainesville,near (<12.5 m),99,0.7777777777777778,0.8383838383838383,6 gainesville,mid (12.5-25 m),123,0.7398373983739838,0.8617886178861789,15 gainesville,far (>25 m),50,0.3,0.42,6 -laurens,near (<12.5 m),180,0.4388888888888889,0.5722222222222222,24 -laurens,mid (12.5-25 m),60,0.3,0.4666666666666667,10 -laurens,far (>25 m),9,0.0,0.0,0 +laurens_mapillary,near (<12.5 m),180,0.4388888888888889,0.5722222222222222,24 +laurens_mapillary,mid (12.5-25 m),60,0.3,0.4666666666666667,10 +laurens_mapillary,far (>25 m),9,0.0,0.0,0 budapest_district5,near (<12.5 m),150,0.58,0.6933333333333334,17 budapest_district5,mid (12.5-25 m),88,0.4431818181818182,0.6363636363636364,17 budapest_district5,far (>25 m),50,0.48,0.58,5 diff --git a/analysis_out/op/storage_floor.csv b/analysis_out/op/storage_floor.csv index 6bd0b4e1..65351567 100644 --- a/analysis_out/op/storage_floor.csv +++ b/analysis_out/op/storage_floor.csv @@ -6,7 +6,7 @@ morgantown,267,7,12,0.8876404494382022,0.8614232209737828,0.8164794007490637,0.7 annapolis,294,9,10,0.9013605442176871,0.8707482993197279,0.8367346938775511,0.7380952380952381,265,256,246,217 paterson,395,1,9,0.7569620253164557,0.7544303797468355,0.7316455696202532,0.6810126582278481,299,298,289,269 gainesville,272,6,14,0.8897058823529411,0.8676470588235294,0.8161764705882353,0.6727941176470589,242,236,222,183 -laurens,249,22,21,0.7831325301204819,0.6947791164658634,0.6104417670682731,0.3895582329317269,195,173,152,97 +laurens_mapillary,249,22,21,0.7831325301204819,0.6947791164658634,0.6104417670682731,0.3895582329317269,195,173,152,97 budapest_district5,300,7,18,0.7566666666666667,0.7333333333333333,0.6733333333333333,0.51,227,220,202,153 sao_paulo,281,2,10,0.8683274021352313,0.8612099644128114,0.8256227758007118,0.6512455516014235,244,242,232,183 manual_gold,3919,19,48,0.920387854044399,0.9155396784894105,0.9032916560347027,0.8494513906608829,3607,3588,3540,3329 diff --git a/analysis_out/op/tp_origin_by_bin.csv b/analysis_out/op/tp_origin_by_bin.csv index dd682487..b114b183 100644 --- a/analysis_out/op/tp_origin_by_bin.csv +++ b/analysis_out/op/tp_origin_by_bin.csv @@ -69,16 +69,16 @@ gainesville,0.6,31,0 gainesville,0.7,35,2 gainesville,0.8,55,0 gainesville,0.9,47,0 -laurens,0.0,0,22 -laurens,0.1,0,21 -laurens,0.2,0,21 -laurens,0.3,0,17 -laurens,0.4,0,16 -laurens,0.5,9,1 -laurens,0.6,24,0 -laurens,0.7,20,0 -laurens,0.8,34,0 -laurens,0.9,10,0 +laurens_mapillary,0.0,0,22 +laurens_mapillary,0.1,0,21 +laurens_mapillary,0.2,0,21 +laurens_mapillary,0.3,0,17 +laurens_mapillary,0.4,0,16 +laurens_mapillary,0.5,9,1 +laurens_mapillary,0.6,24,0 +laurens_mapillary,0.7,20,0 +laurens_mapillary,0.8,34,0 +laurens_mapillary,0.9,10,0 budapest_district5,0.0,0,7 budapest_district5,0.1,0,18 budapest_district5,0.2,0,9 diff --git a/analysis_out/op_cache/laurens.json b/analysis_out/op_cache/laurens.json deleted file mode 100644 index 90bd8dc2..00000000 --- a/analysis_out/op_cache/laurens.json +++ /dev/null @@ -1 +0,0 @@ -{"city": "laurens", "meta": {"score_floor": 0.05, "min_distance": 10, "radius_normalized": 0.022, "fp16": false, "tta": false, "n_panos": 94, "deployed_threshold": 0.55, "model": "projectsidewalk/rampnet-model", "device": "cuda"}, "panos": [{"pano": "1824905744811261", "preds": [[0.59765625, 0.7890625, 0.15207543969154358], [0.41015625, 0.818359375, 0.052808139473199844]], "gt": {"gt_points": [], "ignore_points": [], "fn_confirmed": true}}, {"pano": "1895225997772744", "preds": [[0.15234375, 0.5703125, 0.6234790086746216], [0.2607421875, 0.583984375, 0.30449867248535156], [0.96484375, 0.583984375, 0.057276010513305664]], "gt": {"gt_points": [[0.15234375, 0.5703125], [0.2517912679877559, 0.5842889315974601]], "ignore_points": [], "fn_confirmed": true}}, {"pano": "2102336717175440", "preds": [], 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[0.33240055512047995, 0.5821636246713204], [0.5374564123178409, 0.5401988685483061]], "fn_confirmed": true}}, {"pano": "704289842736043", "preds": [[0.37109375, 0.5859375, 0.5778204202651978], [0.03515625, 0.521484375, 0.09836509078741074], [0.41015625, 0.818359375, 0.05982570722699165]], "gt": {"gt_points": [], "ignore_points": [], "fn_confirmed": true}}]} \ No newline at end of file diff --git a/analysis_out/scoreboard.json b/analysis_out/scoreboard.json index c86c8a65..b0416fa0 100644 --- a/analysis_out/scoreboard.json +++ b/analysis_out/scoreboard.json @@ -7,7 +7,7 @@ "annapolis", "paterson", "gainesville", - "laurens" + "laurens_mapillary" ], "city_splits": [ "richmond", @@ -17,7 +17,8 @@ "annapolis", "paterson", "gainesville", - "laurens", + "laurens_mapillary", + "laurens_gsv", "budapest_district5", "sao_paulo" ], @@ -29,12 +30,14 @@ "annapolis", "paterson", "gainesville", - "laurens", + "laurens_mapillary", + "laurens_gsv", "budapest_district5", "sao_paulo", "manual_gold" ], "held_out": { + "laurens_gsv": "second imagery arm of laurens, which is already pooled through laurens_mapillary -- the two arms sample one town and largely the same physical ramps (59% of gsv panos within 20 m of a mapillary one, median NN 17.2 m), so pooling both would double-count them and break the independence the Wilson intervals assume (GT is HIGH confidence; held out for non-independence, not GT quality)", "budapest_district5": "single-rater GT at low reviewer confidence (docs/model_comparison.md: do not pool)", "sao_paulo": "non-US city \u2014 the pooled recommendation is a US-deployment basis (GT is HIGH reviewer confidence; held out for geography, not GT quality)", "manual_gold": "in-distribution GSV + independently-labelled GT (in-domain reference, not a deployment city)" @@ -57,6 +60,31 @@ "f1": 0.826036 } } + }, + "google/owlv2-large-patch14-ensemble": { + "ap": 0.088684, + "n_points": 64083, + "marks": {} + }, + "IDEA-Research/grounding-dino-base": { + "ap": 0.034544, + "n_points": 71701, + "marks": {} + }, + "y11l_pano": { + "ap": 0.721196, + "n_points": 3144, + "marks": {} + }, + "y11x_pano_h200": { + "ap": 0.727502, + "n_points": 2722, + "marks": {} + }, + "y26_pano": { + "ap": 0.610065, + "n_points": 6105, + "marks": {} } }, "splits": { @@ -95,11 +123,16 @@ "n_gt": 272, "pooled": true }, - "laurens": { + "laurens_mapillary": { "n_panos": 94, "n_gt": 249, "pooled": true }, + "laurens_gsv": { + "n_panos": 86, + "n_gt": 220, + "pooled": false + }, "budapest_district5": { "n_panos": 125, "n_gt": 300, @@ -137,7 +170,7 @@ "annapolis", "paterson", "gainesville", - "laurens" + "laurens_mapillary" ], "precision": 0.95072, "recall": 0.685578, @@ -150,9 +183,13 @@ "micro_recall": 0.691208, "f1_min": 0.543417, "f1_max": 0.854578, - "f1_min_split": "laurens", + "f1_min_split": "laurens_mapillary", "f1_max_split": "richmond", - "n_splits_run": 11, + "n_splits_run": 12, + "laurens_gsv_f1": 0.658824, + "laurens_gsv_precision": 0.933333, + "laurens_gsv_recall": 0.509091, + "laurens_gsv_ap": 0.493735, "budapest_district5_f1": 0.644211, "budapest_district5_precision": 0.874286, "budapest_district5_recall": 0.51, @@ -176,8 +213,8 @@ "class": "supervised", "operating_point": 0.25, "operating_point_note": "0.25", - "coverage": "7/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ "richmond", "bend", @@ -185,22 +222,27 @@ "morgantown", "annapolis", "paterson", - "gainesville" + "gainesville", + "laurens_mapillary" ], - "precision": 0.939038, - "recall": 0.449446, - "f1": 0.603724, - "ap": 0.722441, - "ap_bundle": 0.722441, + "precision": 0.939583, + "recall": 0.443466, + "f1": 0.598681, + "ap": 0.7182, + "ap_bundle": 0.7182, "ap_is_substituted": false, - "fp_per_pano": 0.067478, - "micro_precision": 0.941532, - "micro_recall": 0.453398, + "fp_per_pano": 0.067022, + "micro_precision": 0.941712, + "micro_recall": 0.447813, "f1_min": 0.480818, "f1_max": 0.712909, "f1_min_split": "annapolis", "f1_max_split": "bend", - "n_splits_run": 10, + "n_splits_run": 12, + "laurens_gsv_f1": 0.586751, + "laurens_gsv_precision": 0.958763, + "laurens_gsv_recall": 0.422727, + "laurens_gsv_ap": 0.715411, "budapest_district5_f1": 0.247191, "budapest_district5_precision": 0.785714, "budapest_district5_recall": 0.146667, @@ -224,8 +266,8 @@ "class": "supervised", "operating_point": 0.25, "operating_point_note": "0.25", - "coverage": "7/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ "richmond", "bend", @@ -233,22 +275,27 @@ "morgantown", "annapolis", "paterson", - "gainesville" + "gainesville", + "laurens_mapillary" ], - "precision": 0.968716, - "recall": 0.415557, - "f1": 0.574875, - "ap": 0.729641, - "ap_bundle": 0.729641, + "precision": 0.967363, + "recall": 0.409295, + "f1": 0.569149, + "ap": 0.722864, + "ap_bundle": 0.722864, "ap_is_substituted": false, - "fp_per_pano": 0.028938, - "micro_precision": 0.971973, - "micro_recall": 0.420874, + "fp_per_pano": 0.03064, + "micro_precision": 0.970618, + "micro_recall": 0.414898, "f1_min": 0.396739, "f1_max": 0.709804, "f1_min_split": "annapolis", "f1_max_split": "bend", - "n_splits_run": 10, + "n_splits_run": 12, + "laurens_gsv_f1": 0.567742, + "laurens_gsv_precision": 0.977778, + "laurens_gsv_recall": 0.4, + "laurens_gsv_ap": 0.701288, "budapest_district5_f1": 0.22093, "budapest_district5_precision": 0.863636, "budapest_district5_recall": 0.126667, @@ -272,8 +319,8 @@ "class": "supervised", "operating_point": 0.25, "operating_point_note": "0.25", - "coverage": "7/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ "richmond", "bend", @@ -281,22 +328,27 @@ "morgantown", "annapolis", "paterson", - "gainesville" + "gainesville", + "laurens_mapillary" ], - "precision": 0.736366, - "recall": 0.44627, - "f1": 0.55033, - "ap": 0.6029, - "ap_bundle": 0.6029, + "precision": 0.743611, + "recall": 0.446711, + "f1": 0.553333, + "ap": 0.605926, + "ap_bundle": 0.605926, "ap_is_substituted": false, - "fp_per_pano": 0.377815, - "micro_precision": 0.741754, - "micro_recall": 0.447573, + "fp_per_pano": 0.369152, + "micro_precision": 0.74711, + "micro_recall": 0.447813, "f1_min": 0.449761, "f1_max": 0.681034, "f1_min_split": "annapolis", "f1_max_split": "morgantown", - "n_splits_run": 10, + "n_splits_run": 12, + "laurens_gsv_f1": 0.538462, + "laurens_gsv_precision": 0.771186, + "laurens_gsv_recall": 0.413636, + "laurens_gsv_ap": 0.610536, "budapest_district5_f1": 0.276762, "budapest_district5_precision": 0.638554, "budapest_district5_recall": 0.176667, @@ -310,90 +362,6 @@ "manual_gold_recall": 0.738964, "manual_gold_ap": 0.843378 }, - { - "model": "mask2former-vistas-curb-cut", - "label": "mask2former-vistas-curb-cut", - "spec": "vistas:curb-cut", - "provider": "vistas", - "standing": false, - "display": "Mask2Former Vistas (curb cut)", - "class": "supervised-transfer", - "operating_point": 0.0, - "operating_point_note": "export floor", - "coverage": "1/8", - "complete": false, - "pooled_splits": [ - "richmond" - ], - "precision": 0.411429, - "recall": 0.696774, - "f1": 0.517365, - "ap": 0.512564, - "ap_bundle": 0.512564, - "ap_is_substituted": false, - "fp_per_pano": 2.491935, - "micro_precision": 0.411429, - "micro_recall": 0.696774, - "f1_min": 0.517365, - "f1_max": 0.517365, - "f1_min_split": "richmond", - "f1_max_split": "richmond", - "n_splits_run": 1, - "budapest_district5_f1": null, - "budapest_district5_precision": null, - "budapest_district5_recall": null, - "budapest_district5_ap": null, - "sao_paulo_f1": null, - "sao_paulo_precision": null, - "sao_paulo_recall": null, - "sao_paulo_ap": null, - "manual_gold_f1": null, - "manual_gold_precision": null, - "manual_gold_recall": null, - "manual_gold_ap": null - }, - { - "model": "mask2former-vistas-curb-cut+curb", - "label": "mask2former-vistas-curb-cut+curb", - "spec": "vistas:curb-cut+curb", - "provider": "vistas", - "standing": false, - "display": "Mask2Former Vistas (+curb)", - "class": "supervised-transfer", - "operating_point": 0.0, - "operating_point_note": "export floor", - "coverage": "1/8", - "complete": false, - "pooled_splits": [ - "richmond" - ], - "precision": 0.125625, - "recall": 0.648387, - "f1": 0.210471, - "ap": 0.089131, - "ap_bundle": 0.089131, - "ap_is_substituted": false, - "fp_per_pano": 11.282258, - "micro_precision": 0.125625, - "micro_recall": 0.648387, - "f1_min": 0.210471, - "f1_max": 0.210471, - "f1_min_split": "richmond", - "f1_max_split": "richmond", - "n_splits_run": 1, - "budapest_district5_f1": null, - "budapest_district5_precision": null, - "budapest_district5_recall": null, - "budapest_district5_ap": null, - "sao_paulo_f1": null, - "sao_paulo_precision": null, - "sao_paulo_recall": null, - "sao_paulo_ap": null, - "manual_gold_f1": null, - "manual_gold_precision": null, - "manual_gold_recall": null, - "manual_gold_ap": null - }, { "model": "gemini-3.1-pro-preview", "label": "gemini-3.1-pro-preview", @@ -404,8 +372,8 @@ "class": "chat-vlm", "operating_point": 0.0, "operating_point_note": "no score", - "coverage": "7/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ "richmond", "bend", @@ -413,22 +381,27 @@ "morgantown", "annapolis", "paterson", - "gainesville" + "gainesville", + "laurens_mapillary" ], - "precision": 0.655804, - "recall": 0.57276, - "f1": 0.608078, + "precision": 0.638344, + "recall": 0.533293, + "f1": 0.574964, "ap": null, "ap_bundle": null, "ap_is_substituted": false, - "fp_per_pano": 0.711759, - "micro_precision": 0.660744, - "micro_recall": 0.57767, - "f1_min": 0.513514, + "fp_per_pano": 0.702576, + "micro_precision": 0.651429, + "micro_recall": 0.543092, + "f1_min": 0.343164, "f1_max": 0.680851, - "f1_min_split": "clovis", + "f1_min_split": "laurens_mapillary", "f1_max_split": "paterson", - "n_splits_run": 9, + "n_splits_run": 11, + "laurens_gsv_f1": 0.27907, + "laurens_gsv_precision": 0.518519, + "laurens_gsv_recall": 0.190909, + "laurens_gsv_ap": null, "budapest_district5_f1": 0.381308, "budapest_district5_precision": 0.434043, "budapest_district5_recall": 0.34, @@ -442,48 +415,6 @@ "manual_gold_recall": null, "manual_gold_ap": null }, - { - "model": "claude-opus-5-effort-low", - "label": "claude-opus-5", - "spec": "claude:claude-opus-5", - "provider": "claude", - "standing": false, - "display": "Claude Opus 5 (low)", - "class": "chat-vlm", - "operating_point": 0.0, - "operating_point_note": "no score", - "coverage": "1/8", - "complete": false, - "pooled_splits": [ - "annapolis" - ], - "precision": 0.572347, - "recall": 0.605442, - "f1": 0.58843, - "ap": null, - "ap_bundle": null, - "ap_is_substituted": false, - "fp_per_pano": 1.064, - "micro_precision": 0.572347, - "micro_recall": 0.605442, - "f1_min": 0.58843, - "f1_max": 0.58843, - "f1_min_split": "annapolis", - "f1_max_split": "annapolis", - "n_splits_run": 1, - "budapest_district5_f1": null, - "budapest_district5_precision": null, - "budapest_district5_recall": null, - "budapest_district5_ap": null, - "sao_paulo_f1": null, - "sao_paulo_precision": null, - "sao_paulo_recall": null, - "sao_paulo_ap": null, - "manual_gold_f1": null, - "manual_gold_precision": null, - "manual_gold_recall": null, - "manual_gold_ap": null - }, { "model": "gemini-3.7-flash", "label": "gemini-3.7-flash", @@ -494,8 +425,8 @@ "class": "chat-vlm", "operating_point": 0.0, "operating_point_note": "no score", - "coverage": "7/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ "richmond", "bend", @@ -503,22 +434,27 @@ "morgantown", "annapolis", "paterson", - "gainesville" + "gainesville", + "laurens_mapillary" ], - "precision": 0.697686, - "recall": 0.496471, - "f1": 0.57606, + "precision": 0.678789, + "recall": 0.458006, + "f1": 0.539127, "ap": null, "ap_bundle": null, "ap_is_substituted": false, - "fp_per_pano": 0.514148, - "micro_precision": 0.700954, - "micro_recall": 0.499515, - "f1_min": 0.456212, + "fp_per_pano": 0.501741, + "micro_precision": 0.692407, + "micro_recall": 0.466003, + "f1_min": 0.280597, "f1_max": 0.664286, - "f1_min_split": "gainesville", + "f1_min_split": "laurens_mapillary", "f1_max_split": "richmond", - "n_splits_run": 10, + "n_splits_run": 12, + "laurens_gsv_f1": 0.260563, + "laurens_gsv_precision": 0.578125, + "laurens_gsv_recall": 0.168182, + "laurens_gsv_ap": null, "budapest_district5_f1": 0.338395, "budapest_district5_precision": 0.484472, "budapest_district5_recall": 0.26, @@ -542,8 +478,8 @@ "class": "chat-vlm", "operating_point": 0.0, "operating_point_note": "no score", - "coverage": "7/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ "richmond", "bend", @@ -551,22 +487,27 @@ "morgantown", "annapolis", "paterson", - "gainesville" + "gainesville", + "laurens_mapillary" ], - "precision": 0.588383, - "recall": 0.548933, - "f1": 0.56369, + "precision": 0.570639, + "recall": 0.505417, + "f1": 0.527854, "ap": null, 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"qwen:Qwen/Qwen3-VL-8B-Instruct", + "provider": "qwen", + "standing": true, + "display": "Qwen3-VL-8B", "class": "chat-vlm", "operating_point": 0.0, "operating_point_note": "no score", - "coverage": "1/8", - "complete": false, + "coverage": "8/8", + "complete": true, "pooled_splits": [ - "annapolis" + "richmond", + "bend", + "clovis", + "morgantown", + "annapolis", + "paterson", + "gainesville", + "laurens_mapillary" ], - "precision": 0.429844, - "recall": 0.656463, - "f1": 0.519515, + "precision": 0.312028, + "recall": 0.340215, + "f1": 0.322111, "ap": null, "ap_bundle": null, "ap_is_substituted": false, - "fp_per_pano": 2.048, - "micro_precision": 0.429844, - "micro_recall": 0.656463, - "f1_min": 0.519515, - "f1_max": 0.519515, - "f1_min_split": "annapolis", - "f1_max_split": "annapolis", - "n_splits_run": 1, - "budapest_district5_f1": null, - "budapest_district5_precision": null, - "budapest_district5_recall": null, + "fp_per_pano": 1.810281, + "micro_precision": 0.316561, + 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"fp": 7, "fn": 124, @@ -1047,6 +1169,7 @@ "ap": 0.845563, "ap_source": "op_cache (0.05 floor)", "ap_bundle": 0.690818, + "bundle_floor": 0.551343, "tp": 190, "fp": 11, "fn": 82, @@ -1054,14 +1177,15 @@ "n_gt_recall": 272, "fp_per_pano": 0.088 }, - "laurens": { - "split": "laurens", + "laurens_mapillary": { + "split": "laurens_mapillary", "precision": 0.898148, "recall": 0.389558, "f1": 0.543417, "ap": 0.690656, "ap_source": "op_cache (0.05 floor)", "ap_bundle": 0.376822, + "bundle_floor": 0.555331, "tp": 97, "fp": 11, "fn": 152, @@ -1069,6 +1193,22 @@ "n_gt_recall": 249, "fp_per_pano": 0.117021 }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.933333, + "recall": 0.509091, + "f1": 0.658824, + "ap": 0.493735, + "ap_source": "bundle", + "ap_bundle": 0.493735, + "bundle_floor": 0.552202, + "tp": 112, + "fp": 8, + "fn": 108, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 0.093023 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.874286, @@ -1077,6 +1217,7 @@ "ap": 0.647705, "ap_source": "op_cache (0.05 floor)", "ap_bundle": 0.478209, + "bundle_floor": 0.550114, "tp": 153, "fp": 22, "fn": 147, @@ -1092,6 +1233,7 @@ "ap": 0.811637, "ap_source": "op_cache (0.05 floor)", "ap_bundle": 0.665505, + "bundle_floor": 0.552963, "tp": 193, "fp": 23, "fn": 88, @@ -1107,6 +1249,7 @@ "ap": 0.917292, "ap_source": "bundle", "ap_bundle": 0.917292, + "bundle_floor": 0.05007, "tp": 3420, "fp": 190, "fn": 499, @@ -1124,6 +1267,7 @@ "ap": 0.723784, "ap_source": "bundle", "ap_bundle": 0.723784, + "bundle_floor": 0.050193, "tp": 136, "fp": 11, "fn": 174, @@ -1139,6 +1283,7 @@ "ap": 0.777931, "ap_source": "bundle", "ap_bundle": 0.777931, + "bundle_floor": 0.050243, "tp": 185, "fp": 7, "fn": 142, @@ -1154,6 +1299,7 @@ "ap": 0.70759, "ap_source": "bundle", "ap_bundle": 0.70759, + "bundle_floor": 0.05033, "tp": 87, "fp": 8, "fn": 108, @@ -1169,6 +1315,7 @@ "ap": 0.797114, "ap_source": "bundle", "ap_bundle": 0.797114, + "bundle_floor": 0.050257, "tp": 140, "fp": 8, "fn": 127, @@ -1184,6 +1331,7 @@ "ap": 0.618997, "ap_source": "bundle", "ap_bundle": 0.618997, + "bundle_floor": 0.050454, "tp": 94, "fp": 3, "fn": 200, @@ -1199,6 +1347,7 @@ "ap": 0.792867, "ap_source": "bundle", "ap_bundle": 0.792867, + "bundle_floor": 0.05, "tp": 194, "fp": 11, "fn": 201, @@ -1214,6 +1363,7 @@ "ap": 0.638803, "ap_source": "bundle", "ap_bundle": 0.638803, + "bundle_floor": 0.050077, "tp": 98, "fp": 10, "fn": 174, @@ -1221,6 +1371,38 @@ "n_gt_recall": 272, "fp_per_pano": 0.08 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.943396, + "recall": 0.401606, + "f1": 0.56338, + "ap": 0.688518, + "ap_source": "bundle", + "ap_bundle": 0.688518, + "bundle_floor": 0.051886, + "tp": 100, + "fp": 6, + "fn": 149, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 0.06383 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.958763, + "recall": 0.422727, + "f1": 0.586751, + "ap": 0.715411, + "ap_source": "bundle", + "ap_bundle": 0.715411, + "bundle_floor": 0.050329, + "tp": 93, + "fp": 4, + "fn": 127, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 0.046512 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.785714, @@ -1229,6 +1411,7 @@ "ap": 0.443481, "ap_source": "bundle", "ap_bundle": 0.443481, + "bundle_floor": 0.050044, "tp": 44, "fp": 12, "fn": 256, @@ -1244,6 +1427,7 @@ "ap": 0.783949, "ap_source": "bundle", "ap_bundle": 0.783949, + "bundle_floor": 0.050247, "tp": 147, "fp": 16, "fn": 134, @@ -1259,6 +1443,7 @@ "ap": 0.913984, "ap_source": "bundle", "ap_bundle": 0.913984, + "bundle_floor": 0.050013, "tp": 2992, "fp": 222, "fn": 927, @@ -1276,6 +1461,7 @@ "ap": 0.748061, "ap_source": "bundle", "ap_bundle": 0.748061, + "bundle_floor": 0.050126, "tp": 119, "fp": 6, "fn": 191, @@ -1291,6 +1477,7 @@ "ap": 0.781366, "ap_source": "bundle", "ap_bundle": 0.781366, + "bundle_floor": 0.050247, "tp": 181, "fp": 2, "fn": 146, @@ -1306,6 +1493,7 @@ "ap": 0.710942, "ap_source": "bundle", "ap_bundle": 0.710942, + "bundle_floor": 0.050052, "tp": 76, "fp": 5, "fn": 119, @@ -1321,6 +1509,7 @@ "ap": 0.789853, "ap_source": "bundle", "ap_bundle": 0.789853, + "bundle_floor": 0.050252, "tp": 140, "fp": 1, "fn": 127, @@ -1336,6 +1525,7 @@ "ap": 0.661568, "ap_source": "bundle", "ap_bundle": 0.661568, + "bundle_floor": 0.050035, "tp": 73, "fp": 1, "fn": 221, @@ -1351,6 +1541,7 @@ "ap": 0.800153, "ap_source": "bundle", "ap_bundle": 0.800153, + "bundle_floor": 0.050212, "tp": 186, "fp": 5, "fn": 209, @@ -1366,6 +1557,7 @@ "ap": 0.615546, "ap_source": "bundle", "ap_bundle": 0.615546, + "bundle_floor": 0.050359, "tp": 92, "fp": 5, "fn": 180, @@ -1373,6 +1565,38 @@ "n_gt_recall": 272, "fp_per_pano": 0.04 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.957895, + "recall": 0.365462, + "f1": 0.52907, + "ap": 0.67542, + "ap_source": "bundle", + "ap_bundle": 0.67542, + "bundle_floor": 0.050144, + "tp": 91, + "fp": 4, + "fn": 158, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 0.042553 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.977778, + "recall": 0.4, + "f1": 0.567742, + "ap": 0.701288, + "ap_source": "bundle", + "ap_bundle": 0.701288, + "bundle_floor": 0.051092, + "tp": 88, + "fp": 2, + "fn": 132, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 0.023256 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.863636, @@ -1381,6 +1605,7 @@ "ap": 0.42687, "ap_source": "bundle", "ap_bundle": 0.42687, + "bundle_floor": 0.05008, "tp": 38, "fp": 6, "fn": 262, @@ -1396,6 +1621,7 @@ "ap": 0.782801, "ap_source": "bundle", "ap_bundle": 0.782801, + "bundle_floor": 0.050017, "tp": 144, "fp": 12, "fn": 137, @@ -1411,6 +1637,7 @@ "ap": 0.930925, "ap_source": "bundle", "ap_bundle": 0.930925, + "bundle_floor": 0.050003, "tp": 3005, "fp": 139, "fn": 914, @@ -1428,6 +1655,7 @@ "ap": 0.536502, "ap_source": "bundle", "ap_bundle": 0.536502, + "bundle_floor": 0.050037, "tp": 119, "fp": 56, "fn": 191, @@ -1443,6 +1671,7 @@ "ap": 0.661388, "ap_source": "bundle", "ap_bundle": 0.661388, + "bundle_floor": 0.050057, "tp": 184, "fp": 67, "fn": 143, @@ -1458,6 +1687,7 @@ "ap": 0.596431, "ap_source": "bundle", "ap_bundle": 0.596431, + "bundle_floor": 0.050109, "tp": 90, "fp": 41, "fn": 105, @@ -1473,6 +1703,7 @@ "ap": 0.739571, "ap_source": "bundle", "ap_bundle": 0.739571, + "bundle_floor": 0.050348, "tp": 158, "fp": 39, "fn": 109, @@ -1488,6 +1719,7 @@ "ap": 0.493248, "ap_source": "bundle", "ap_bundle": 0.493248, + "bundle_floor": 0.05002, "tp": 94, "fp": 30, "fn": 200, @@ -1503,6 +1735,7 @@ "ap": 0.711963, "ap_source": "bundle", "ap_bundle": 0.711963, + "bundle_floor": 0.050077, "tp": 187, "fp": 51, "fn": 208, @@ -1518,6 +1751,7 @@ "ap": 0.4812, "ap_source": "bundle", "ap_bundle": 0.4812, + "bundle_floor": 0.050055, "tp": 90, "fp": 37, "fn": 182, @@ -1525,6 +1759,38 @@ "n_gt_recall": 272, "fp_per_pano": 0.296 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.794326, + "recall": 0.449799, + "f1": 0.574359, + "ap": 0.627105, + "ap_source": "bundle", + "ap_bundle": 0.627105, + "bundle_floor": 0.050009, + "tp": 112, + "fp": 29, + "fn": 137, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 0.308511 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.771186, + "recall": 0.413636, + "f1": 0.538462, + "ap": 0.610536, + "ap_source": "bundle", + "ap_bundle": 0.610536, + "bundle_floor": 0.050079, + "tp": 91, + "fp": 27, + "fn": 129, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 0.313953 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.638554, @@ -1533,6 +1799,7 @@ "ap": 0.349591, "ap_source": "bundle", "ap_bundle": 0.349591, + "bundle_floor": 0.050024, "tp": 53, "fp": 30, "fn": 247, @@ -1548,6 +1815,7 @@ "ap": 0.658092, "ap_source": "bundle", "ap_bundle": 0.658092, + "bundle_floor": 0.050219, "tp": 146, "fp": 56, "fn": 135, @@ -1563,6 +1831,7 @@ "ap": 0.843378, "ap_source": "bundle", "ap_bundle": 0.843378, + "bundle_floor": 0.050017, "tp": 2896, "fp": 1020, "fn": 1023, @@ -1571,40 +1840,6 @@ "fp_per_pano": 1.02 } }, - "mask2former-vistas-curb-cut": { - "richmond": { - "split": "richmond", - "precision": 0.411429, - "recall": 0.696774, - "f1": 0.517365, - "ap": 0.512564, - "ap_source": "bundle", - "ap_bundle": 0.512564, - "tp": 216, - "fp": 309, - "fn": 94, - "n_panos": 124, - "n_gt_recall": 310, - "fp_per_pano": 2.491935 - } - }, - "mask2former-vistas-curb-cut+curb": { - "richmond": { - "split": "richmond", - "precision": 0.125625, - "recall": 0.648387, - "f1": 0.210471, - "ap": 0.089131, - "ap_source": "bundle", - "ap_bundle": 0.089131, - "tp": 201, - "fp": 1399, - "fn": 109, - "n_panos": 124, - "n_gt_recall": 310, - "fp_per_pano": 11.282258 - } - }, "gemini-3.1-pro-preview": { "richmond": { "split": "richmond", @@ -1614,6 +1849,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 218, "fp": 126, "fn": 92, @@ -1629,6 +1865,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 190, "fp": 79, "fn": 137, @@ -1644,6 +1881,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 95, "fp": 80, "fn": 100, @@ -1659,6 +1897,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 163, "fp": 77, "fn": 104, @@ -1674,6 +1913,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 155, "fp": 98, "fn": 139, @@ -1689,6 +1929,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 224, "fp": 39, "fn": 171, @@ -1704,6 +1945,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 145, "fp": 112, "fn": 127, @@ -1711,6 +1953,38 @@ "n_gt_recall": 272, "fp_per_pano": 0.896 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.516129, + "recall": 0.257028, + "f1": 0.343164, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 64, + "fp": 60, + "fn": 185, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 0.638298 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.518519, + "recall": 0.190909, + "f1": 0.27907, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 42, + "fp": 39, + "fn": 178, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 0.453488 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.434043, @@ -1719,6 +1993,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 102, "fp": 133, "fn": 198, @@ -1734,29 +2009,13 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 125, "fp": 145, "fn": 156, "n_panos": 125, - "n_gt_recall": 281, - "fp_per_pano": 1.16 - } - }, - "claude-opus-5-effort-low": { - "annapolis": { - "split": "annapolis", - "precision": 0.572347, - "recall": 0.605442, - "f1": 0.58843, - "ap": null, - "ap_source": "bundle", - "ap_bundle": null, - "tp": 178, - "fp": 133, - "fn": 116, - "n_panos": 125, - "n_gt_recall": 294, - "fp_per_pano": 1.064 + "n_gt_recall": 281, + "fp_per_pano": 1.16 } }, "gemini-3.7-flash": { @@ -1768,6 +2027,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 186, "fp": 64, "fn": 124, @@ -1783,6 +2043,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 189, "fp": 76, "fn": 138, @@ -1798,6 +2059,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 88, "fp": 66, "fn": 107, @@ -1813,6 +2075,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 138, "fp": 59, "fn": 129, @@ -1828,6 +2091,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 135, "fp": 49, "fn": 159, @@ -1843,6 +2107,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 181, "fp": 18, "fn": 214, @@ -1858,6 +2123,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 112, "fp": 107, "fn": 160, @@ -1865,6 +2131,38 @@ "n_gt_recall": 272, "fp_per_pano": 0.856 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.546512, + "recall": 0.188755, + "f1": 0.280597, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 47, + "fp": 39, + "fn": 202, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 0.414894 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.578125, + "recall": 0.168182, + "f1": 0.260563, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 37, + "fp": 27, + "fn": 183, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 0.313953 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.484472, @@ -1873,6 +2171,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 78, "fp": 83, "fn": 222, @@ -1888,6 +2187,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 74, "fp": 58, "fn": 207, @@ -1903,6 +2203,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 1612, "fp": 582, "fn": 2307, @@ -1920,6 +2221,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 199, "fp": 119, "fn": 111, @@ -1935,6 +2237,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 192, "fp": 124, "fn": 135, @@ -1950,6 +2253,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 98, "fp": 113, "fn": 97, @@ -1965,6 +2269,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 168, "fp": 96, "fn": 99, @@ -1980,6 +2285,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 144, "fp": 82, "fn": 150, @@ -1995,6 +2301,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 203, "fp": 70, "fn": 192, @@ -2010,6 +2317,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 130, "fp": 192, "fn": 142, @@ -2017,6 +2325,38 @@ "n_gt_recall": 272, "fp_per_pano": 1.536 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.446429, + "recall": 0.200803, + "f1": 0.277008, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 50, + "fp": 62, + "fn": 199, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 0.659574 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.355072, + "recall": 0.222727, + "f1": 0.273743, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 49, + "fp": 89, + "fn": 171, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 1.034884 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.352941, @@ -2025,6 +2365,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 96, "fp": 176, "fn": 204, @@ -2040,6 +2381,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 101, "fp": 202, "fn": 180, @@ -2048,359 +2390,392 @@ "fp_per_pano": 1.616 } }, - "claude-opus-5-effort-high": { - "annapolis": { - "split": "annapolis", - "precision": 0.429844, - "recall": 0.656463, - "f1": 0.519515, - "ap": null, - "ap_source": "bundle", - "ap_bundle": null, - "tp": 193, - "fp": 256, - "fn": 101, - "n_panos": 125, - "n_gt_recall": 294, - "fp_per_pano": 2.048 - } - }, - "claude-sonnet-5-effort-low": { - "annapolis": { - "split": "annapolis", - "precision": 0.589474, - "recall": 0.380952, - "f1": 0.46281, - "ap": null, - "ap_source": "bundle", - "ap_bundle": null, - "tp": 112, - "fp": 78, - "fn": 182, - "n_panos": 125, - "n_gt_recall": 294, - "fp_per_pano": 0.624 - } - }, - 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"bundle_floor": null, "tp": 160, "fp": 230, "fn": 150, @@ -2427,6 +2803,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 131, "fp": 126, "fn": 196, @@ -2442,6 +2819,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 86, "fp": 171, "fn": 109, @@ -2457,6 +2835,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 123, "fp": 141, "fn": 144, @@ -2472,6 +2851,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 122, "fp": 159, "fn": 172, @@ -2487,6 +2867,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 179, "fp": 127, "fn": 216, @@ -2502,6 +2883,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 107, "fp": 272, "fn": 165, @@ -2509,6 +2891,38 @@ "n_gt_recall": 272, "fp_per_pano": 2.176 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.358744, + "recall": 0.321285, + "f1": 0.338983, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 80, + "fp": 143, + "fn": 169, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 1.521277 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.331579, + "recall": 0.286364, + "f1": 0.307317, + "ap": null, + "ap_source": "bundle", + "ap_bundle": null, + "bundle_floor": null, + "tp": 63, + "fp": 127, + "fn": 157, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 1.476744 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.260479, @@ -2517,6 +2931,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 87, "fp": 247, "fn": 213, @@ -2532,6 +2947,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 94, "fp": 201, "fn": 187, @@ -2547,6 +2963,7 @@ "ap": null, "ap_source": "bundle", "ap_bundle": null, + "bundle_floor": null, "tp": 1409, "fp": 1346, "fn": 2510, @@ -2564,6 +2981,7 @@ "ap": 0.10368, "ap_source": "bundle", "ap_bundle": 0.10368, + "bundle_floor": 0.050005, "tp": 301, "fp": 8799, "fn": 9, @@ -2579,6 +2997,7 @@ "ap": 0.093345, "ap_source": "bundle", "ap_bundle": 0.093345, + "bundle_floor": 0.050002, "tp": 312, "fp": 8187, "fn": 15, @@ -2594,6 +3013,7 @@ "ap": 0.06743, "ap_source": "bundle", "ap_bundle": 0.06743, + "bundle_floor": 0.050014, "tp": 178, "fp": 6910, "fn": 17, @@ -2609,6 +3029,7 @@ "ap": 0.113985, "ap_source": "bundle", "ap_bundle": 0.113985, + "bundle_floor": 0.050017, "tp": 253, "fp": 6613, "fn": 14, @@ -2624,6 +3045,7 @@ "ap": 0.125787, "ap_source": "bundle", "ap_bundle": 0.125787, + "bundle_floor": 0.050014, "tp": 282, "fp": 8444, "fn": 12, @@ -2639,6 +3061,7 @@ "ap": 0.116483, "ap_source": "bundle", "ap_bundle": 0.116483, + "bundle_floor": 0.050009, "tp": 353, "fp": 8398, "fn": 42, @@ -2654,6 +3077,7 @@ "ap": 0.062715, "ap_source": "bundle", "ap_bundle": 0.062715, + "bundle_floor": 0.050006, "tp": 263, "fp": 8185, "fn": 9, @@ -2661,6 +3085,38 @@ "n_gt_recall": 272, "fp_per_pano": 65.48 }, + "laurens_mapillary": { + "split": "laurens_mapillary", + "precision": 0.032107, + "recall": 0.851406, + "f1": 0.06188, + "ap": 0.05405, + "ap_source": "bundle", + "ap_bundle": 0.05405, + "bundle_floor": 0.050001, + "tp": 212, + "fp": 6391, + "fn": 37, + "n_panos": 94, + "n_gt_recall": 249, + "fp_per_pano": 67.989362 + }, + "laurens_gsv": { + "split": "laurens_gsv", + "precision": 0.028211, + "recall": 0.854545, + "f1": 0.054619, + "ap": 0.035138, + "ap_source": "bundle", + "ap_bundle": 0.035138, + "bundle_floor": 0.050012, + "tp": 188, + "fp": 6476, + "fn": 32, + "n_panos": 86, + "n_gt_recall": 220, + "fp_per_pano": 75.302326 + }, "budapest_district5": { "split": "budapest_district5", "precision": 0.0319, @@ -2669,6 +3125,7 @@ "ap": 0.088843, "ap_source": "bundle", "ap_bundle": 0.088843, + "bundle_floor": 0.050028, "tp": 279, "fp": 8467, "fn": 21, @@ -2684,6 +3141,7 @@ "ap": 0.049839, "ap_source": 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benchmark/laurens_mapillary/records.jsonl diff --git a/benchmark/laurens/sample.json b/benchmark/laurens_mapillary/sample.json similarity index 100% rename from benchmark/laurens/sample.json rename to benchmark/laurens_mapillary/sample.json diff --git a/benchmark/laurens/verdicts.json b/benchmark/laurens_mapillary/verdicts.json similarity index 100% rename from benchmark/laurens/verdicts.json rename to benchmark/laurens_mapillary/verdicts.json diff --git a/benchmark/model_detections/IDEA-Research__grounding-dino-base__laurens_gsv.json b/benchmark/model_detections/IDEA-Research__grounding-dino-base__laurens_gsv.json new file mode 100644 index 00000000..8dc35ec1 --- /dev/null +++ b/benchmark/model_detections/IDEA-Research__grounding-dino-base__laurens_gsv.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list. Respond with JSON only: a list of {\"bbox_2d\": [x1, y1, x2, y2], \"label\": \"curb ramp\"}.","provider":"qwen","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/Qwen__Qwen3-VL-32B-Instruct__laurens_mapillary.json b/benchmark/model_detections/Qwen__Qwen3-VL-32B-Instruct__laurens_mapillary.json new file mode 100644 index 00000000..9973c78f --- /dev/null +++ b/benchmark/model_detections/Qwen__Qwen3-VL-32B-Instruct__laurens_mapillary.json @@ -0,0 +1 @@ +{"city":"laurens_mapillary","detections":{"1048171837395171":[[0.329566254906474,0.551059321593008,null]],"1062534315912016":[],"1125260286068930":[],"1161087199423825":[],"1162408982536257":[[0.9748410383964992,0.6102395215141456,null],[0.023656051351179097,0.5973263784355042,null],[0.9372845747358063,0.59179753800288,null]],"1164655035789654":[],"1171431381605089":[],"1175095874018468":[],"1185713806822866":[],"1222425273270976":[],"1222452929941555":[[0.7262271724755982,0.6806298682454993,null]],"1229954708959465":[],"1314759497005685":[],"1321899553020334":[],"1329951288332749":[],"1337172621216224":[],"1338686754110443":[],"1340281237294438":[],"1349225106930359":[],"1350224506043655":[],"1361524698783040":[],"1369230248127514":[[0.5333951005529894,0.5496994430072795,null],[0.3900039487716357,0.552108130109258,null],[0.7891207042860846,0.6547129037668966,null],[0.8408593426757444,0.6528418641057094,null]],"1378210510313997":[],"1423893009354395":[],"1458258615264761":[],"1461635258234682":[],"1465448277852019":[],"1483447699626154":[],"1496361174779028":[[0.8637764449011833,0.624379708075239,null]],"1512787529928130":[],"1517299816228613":[],"1518788286020997":[],"1527469165128331":[],"1535322434332846":[],"1539829630297717":[],"1540396413968916":[],"1545329793166565":[],"1550352169435830":[],"1557040268526874":[],"1568453127905099":[],"1586076936136628":[],"1659902464987018":[],"1735749390429351":[],"1824905744811261":[],"1894850657819898":[],"1895225997772744":[],"1900603890488727":[[0.8746058989546945,0.7428204083433143,null]],"1912241163063647":[],"1973123443230745":[],"1990333295097902":[[0.43538949845090363,0.6042591747293211,null]],"2102336717175440":[],"2142725129867751":[],"2217407802101293":[],"2252974625208346":[],"2260879557743097":[],"2281219182305735":[],"2290960884662730":[],"25146842334955788":[],"25190811433886585":[],"25277922765160642":[[0.9228614215181825,0.5808017609728836,null],[0.9595462630464451,0.5693359485209493,null]],"25393196263651274":[[0.7705922378635439,0.5771848091574543,null]],"26137311355859436":[[0.6184322765592367,0.6361978066551414,null]],"2941904002667822":[[0.17217414386334107,0.5767771113656155,null]],"3148199545362586":[[0.3368516740164938,0.5862753306724117,null],[0.2683012026701307,0.5935334540441402,null]],"4175533842712518":[],"4216189052043049":[],"4245707092355026":[[0.8759902416099767,0.6229911207976027,null]],"4276172862704339":[[0.8943174299166912,0.5973517182249793,null]],"4302617043349421":[],"688881197614168":[],"689227697592619":[],"696471096852347":[],"704289842736043":[],"710168705006001":[],"715913028227262":[],"720192293894630":[],"727636259805704":[],"763361353420163":[],"810133888556410":[],"813171344835409":[],"813562584985239":[],"818762744118973":[[0.5581970866584193,0.5882048677789377,null],[0.39654820814556324,0.5545042133002837,null]],"822346197177828":[],"826717166785763":[],"834383442517949":[],"845899741254441":[[0.2927362490283676,0.6737584151716582,null]],"853035244088834":[],"853971067099545":[],"854464513592097":[],"855822370146789":[],"856462003604341":[],"858125493614092":[],"863563026117853":[[0.2967093421153631,0.5944228828216322,null]],"878422481186005":[]},"model":"Qwen/Qwen3-VL-32B-Instruct","n_panos":94,"n_uncached":0,"published_as":"Qwen/Qwen3-VL-32B-Instruct","signature":{"coord_space":"norm1000","max_edge":2048,"max_new_tokens":1024,"model_id":"Qwen/Qwen3-VL-32B-Instruct","prompt":"Detect every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list. Respond with JSON only: a list of {\"bbox_2d\": [x1, y1, x2, y2], \"label\": \"curb ramp\"}.","provider":"qwen","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/Qwen__Qwen3-VL-8B-Instruct__laurens_gsv.json b/benchmark/model_detections/Qwen__Qwen3-VL-8B-Instruct__laurens_gsv.json new file mode 100644 index 00000000..2f01f26e --- /dev/null +++ b/benchmark/model_detections/Qwen__Qwen3-VL-8B-Instruct__laurens_gsv.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list. Respond with JSON only: a list of {\"bbox_2d\": [x1, y1, x2, y2], \"label\": \"curb ramp\"}.","provider":"qwen","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/Qwen__Qwen3-VL-8B-Instruct__laurens_mapillary.json b/benchmark/model_detections/Qwen__Qwen3-VL-8B-Instruct__laurens_mapillary.json new file mode 100644 index 00000000..fb7c55c9 --- /dev/null +++ b/benchmark/model_detections/Qwen__Qwen3-VL-8B-Instruct__laurens_mapillary.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list. Respond with JSON only: a list of {\"bbox_2d\": [x1, y1, x2, y2], \"label\": \"curb ramp\"}.","provider":"qwen","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/allenai__Molmo2-8B__laurens_gsv.json b/benchmark/model_detections/allenai__Molmo2-8B__laurens_gsv.json new file mode 100644 index 00000000..74a0bccc --- /dev/null +++ b/benchmark/model_detections/allenai__Molmo2-8B__laurens_gsv.json @@ -0,0 +1 @@ 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to every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Put one point at the center of each curb ramp. Do not point at driveways, stairs, or crosswalk paint. If there are no curb ramps, say so.","provider":"molmo","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/allenai__Molmo2-8B__laurens_mapillary.json b/benchmark/model_detections/allenai__Molmo2-8B__laurens_mapillary.json new file mode 100644 index 00000000..9cef98c9 --- /dev/null +++ b/benchmark/model_detections/allenai__Molmo2-8B__laurens_mapillary.json @@ -0,0 +1 @@ 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to every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Put one point at the center of each curb ramp. Do not point at driveways, stairs, or crosswalk paint. If there are no curb ramps, say so.","provider":"molmo","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/gemini-3.1-pro-preview__laurens_gsv.json b/benchmark/model_detections/gemini-3.1-pro-preview__laurens_gsv.json new file mode 100644 index 00000000..18a89f08 --- /dev/null +++ b/benchmark/model_detections/gemini-3.1-pro-preview__laurens_gsv.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list.","provider":"gemini","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/gemini-3.1-pro-preview__laurens_mapillary.json b/benchmark/model_detections/gemini-3.1-pro-preview__laurens_mapillary.json new file mode 100644 index 00000000..c6c8e3af --- /dev/null +++ b/benchmark/model_detections/gemini-3.1-pro-preview__laurens_mapillary.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list.","provider":"gemini","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/gemini-3.6-flash__laurens_gsv.json b/benchmark/model_detections/gemini-3.6-flash__laurens_gsv.json new file mode 100644 index 00000000..2b519ded --- /dev/null +++ b/benchmark/model_detections/gemini-3.6-flash__laurens_gsv.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list.","provider":"gemini","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/gemini-3.6-flash__laurens_mapillary.json b/benchmark/model_detections/gemini-3.6-flash__laurens_mapillary.json new file mode 100644 index 00000000..33e7dedd --- /dev/null +++ b/benchmark/model_detections/gemini-3.6-flash__laurens_mapillary.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list.","provider":"gemini","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/gemini-3.7-flash__laurens_gsv.json b/benchmark/model_detections/gemini-3.7-flash__laurens_gsv.json new file mode 100644 index 00000000..7f6ce1ff --- /dev/null +++ b/benchmark/model_detections/gemini-3.7-flash__laurens_gsv.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list.","provider":"gemini","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/gemini-3.7-flash__laurens_mapillary.json b/benchmark/model_detections/gemini-3.7-flash__laurens_mapillary.json new file mode 100644 index 00000000..c499023d --- /dev/null +++ b/benchmark/model_detections/gemini-3.7-flash__laurens_mapillary.json @@ -0,0 +1 @@ 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every curb ramp in this street-level image. A curb ramp (curb cut) is the short sloped ramp cut into a sidewalk curb at a street corner or crossing that lets a wheelchair or stroller roll from sidewalk to street. Return one tight bounding box per curb ramp. Do not box driveways, stairs, or crosswalk paint. If there are no curb ramps, return an empty list.","provider":"gemini","source_max_edge":4096,"tile":true,"views":[[0.0,-30.0,90.0,90.0,1024,1024],[60.0,-30.0,90.0,90.0,1024,1024],[120.0,-30.0,90.0,90.0,1024,1024],[180.0,-30.0,90.0,90.0,1024,1024],[240.0,-30.0,90.0,90.0,1024,1024],[300.0,-30.0,90.0,90.0,1024,1024]]}} \ No newline at end of file diff --git a/benchmark/model_detections/google__owlv2-large-patch14-ensemble__laurens_gsv.json b/benchmark/model_detections/google__owlv2-large-patch14-ensemble__laurens_gsv.json new file mode 100644 index 00000000..031532b1 --- /dev/null +++ b/benchmark/model_detections/google__owlv2-large-patch14-ensemble__laurens_gsv.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/benchmark/model_detections/y11l_pano__laurens_mapillary.json b/benchmark/model_detections/y11l_pano__laurens_mapillary.json new file mode 100644 index 00000000..76b871a9 --- /dev/null +++ b/benchmark/model_detections/y11l_pano__laurens_mapillary.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/benchmark/model_detections/y11x_pano_h200__laurens_gsv.json b/benchmark/model_detections/y11x_pano_h200__laurens_gsv.json new file mode 100644 index 00000000..9b155dc2 --- /dev/null +++ b/benchmark/model_detections/y11x_pano_h200__laurens_gsv.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/benchmark/model_detections/y11x_pano_h200__laurens_mapillary.json b/benchmark/model_detections/y11x_pano_h200__laurens_mapillary.json new file mode 100644 index 00000000..0f3186ca --- /dev/null +++ b/benchmark/model_detections/y11x_pano_h200__laurens_mapillary.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/benchmark/model_detections/y26_pano__laurens_mapillary.json b/benchmark/model_detections/y26_pano__laurens_mapillary.json new file mode 100644 index 00000000..f8e5a1b1 --- /dev/null +++ b/benchmark/model_detections/y26_pano__laurens_mapillary.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/docs/adding_a_benchmark_city.md b/docs/adding_a_benchmark_city.md index ed4efcef..4c379352 100644 --- a/docs/adding_a_benchmark_city.md +++ b/docs/adding_a_benchmark_city.md @@ -47,6 +47,42 @@ Also decide now, and record it in the PR: - **Does this split enter the pooled recommendation?** Default yes for a US/VA deployment city with verdict-grade GT. No for out-of-distribution or rubric-uncertain splits. - **Which imagery tier is it?** If the rig is new, `tier_of()` needs a branch (see phase 5). +- **Is this a second imagery arm of a city already in the benchmark?** If so it does *not* + pool — see below. + +### Split naming, and second imagery arms + +A split id is normally the bare city name — and that name silently also means "the one +imagery source this city happened to be run on". Laurens broke that: it is the only city +run on **both** GSV and Mapillary over one footprint (area hash `d8dd392b…`), which is +exactly what makes it the control for #151. + +The rule: + +> **A split carries a `_` suffix if and only if its city has more than one imagery +> arm.** The suffix is disambiguation — present exactly where it is needed, absent where it +> is not. + +So the benchmark holds `laurens_mapillary` and `laurens_gsv`, while `bend`, `richmond` and +the rest stay bare. If bend ever gains a Mapillary arm it becomes `bend_gsv` + +`bend_mapillary` **at that moment**, not pre-emptively. Renaming costs a directory move, +the `op_cache` file, the `split` column of six committed CSVs, `scoreboard.json`, and a +republish of the HF dataset under new config names — and that last part **breaks every +existing `load_dataset(..., "bend")`**. So rename before the split's first HF publish if +you possibly can; that is the only irreversible piece. + +**A second arm does not enter the pooled recommendation.** The two arms sample the same +town and — because the 30 m de-clustering saturates in a small city — largely the *same +physical curb ramps*: 59% of `laurens_gsv`'s panos sit within 20 m of a `laurens_mapillary` +one, median nearest neighbour 17.2 m. Pooling both would double-count those ramps and break +the independence the Wilson intervals assume. The second arm goes into `HELD_OUT` with that +as its stated reason — a **third** category, alongside "GT is not trustworthy" (budapest) +and "wrong population" (sao_paulo, manual_gold). `CITY_OF` records which city a split +belongs to, and a test enforces that pooled splits are one per city. + +That overlap is also the experiment's best asset: it hands you a paired subset of ~51 +corners seen by both rigs, for free, with no bespoke sampler. Measure it and report it +alongside the two independent recalls. ## Phase 1 — auto-labeler: detect the city and export the bundle @@ -211,6 +247,7 @@ city or mislabelling it. |---|---| | `scripts/analysis/low_floor_sweep.py` → `US_SPLITS` / `CITY_SPLITS` / `ALL_SPLITS` | add the split; `US_SPLITS` membership is what puts it in the pooled recommendation | | `low_floor_sweep.py` → `HELD_OUT` | **required** if it is not pooled — every held-out split must carry a stated reason, and a test enforces this | +| `low_floor_sweep.py` → `CITY_OF` | **required for a second imagery arm** — maps the split to its city, so the one-pooled-split-per-city test can see the pairing | | `low_floor_sweep.py` → `tier_of()` | add a branch if the rig is new, or the split lands in `unknown` | | `low_floor_sweep.py` → `SPLIT_IMAGERY_FALLBACK` | only if `records.jsonl` lacks camera provenance | | `low_floor_sweep.py` → `TTA_RECORD_SPLITS` | only if the committed detections used TTA | @@ -287,6 +324,7 @@ Copy into the PR description and tick. Anything not done gets a line saying so a **Phase 5 — code** - [ ] Added to `US_SPLITS` / `CITY_SPLITS` / `ALL_SPLITS` - [ ] `HELD_OUT` reason recorded if not pooled +- [ ] `CITY_OF` entry added if this is a second imagery arm - [ ] `tier_of()` recognises the rig (not `unknown`) - [ ] `SERIES` colour added from the validated palette in slot order - [ ] `pytest -q` green diff --git a/docs/figures/scoreboard_by_split.png b/docs/figures/scoreboard_by_split.png index 8b202b62..1409a96d 100644 Binary files a/docs/figures/scoreboard_by_split.png and b/docs/figures/scoreboard_by_split.png differ diff --git a/docs/figures/scoreboard_f1.png b/docs/figures/scoreboard_f1.png index b898dcfc..aa561d9b 100644 Binary files a/docs/figures/scoreboard_f1.png and b/docs/figures/scoreboard_f1.png differ diff --git a/docs/figures/scoreboard_generalization.png b/docs/figures/scoreboard_generalization.png index 2885ba38..df68cdb9 100644 Binary files a/docs/figures/scoreboard_generalization.png and b/docs/figures/scoreboard_generalization.png differ diff --git a/docs/figures/scoreboard_pr.png b/docs/figures/scoreboard_pr.png index 806c7049..fd4dd3b9 100644 Binary files a/docs/figures/scoreboard_pr.png and b/docs/figures/scoreboard_pr.png differ diff --git a/docs/figures/scoreboard_pr_curves.png b/docs/figures/scoreboard_pr_curves.png index 18240a79..f58cd09b 100644 Binary files a/docs/figures/scoreboard_pr_curves.png and b/docs/figures/scoreboard_pr_curves.png differ diff --git a/docs/model_comparison.md b/docs/model_comparison.md index 46c3971b..86d33866 100644 --- a/docs/model_comparison.md +++ b/docs/model_comparison.md @@ -431,6 +431,120 @@ Three things gainesville adds: at all). OWLv2's 0.967 recall — its highest anywhere — is mostly density: 67.9 boxes/pano and a 0.722 null (see the null-recall section). +**laurens_mapillary** (94 reviewed panos, 249 GT ramps) — the first **rural** split +(2026-08-31), reviewer confidence HIGH; a consumer GoPro Max 360 rig over a 1.91 km² town +of 1,264 people. The eighth US split, and the one RampNet does worst on by a factor of two. + +| model | P | R | F1 | AP | tp/fp/fn | +|---|---|---|---|---|---| +| **rampnet** | **0.898** | 0.390 | **0.543** | 0.377 | 97/11/152 | +| gemini-3.1-pro-preview | 0.516 | 0.257 | 0.343 | – | 64/60/185 | +| **molmo2-8B** (points) | 0.359 | **0.321** | 0.339 | – | 80/143/169 | +| gemini-3.6-flash | 0.446 | 0.201 | 0.277 | – | 50/62/199 | +| Qwen3-VL-8B-Instruct | 0.220 | 0.201 | 0.210 | – | 50/177/199 | +| **Qwen3-VL-32B-Instruct** | 0.360 | **0.036** | **0.066** | – | 9/16/240 | +| owlv2-large-patch14-ensemble | 0.032 | **0.851** | 0.062 | 0.054 | 212/6391/37 | +| grounding-dino-base | 0.023 | 0.783 | 0.045 | 0.025 | 195/8162/54 | + +Best sweep F1: OWLv2 **0.099** (thr 0.15), Grounding DINO **0.060** (thr 0.15). + +Three things laurens adds: + +1. **RampNet's worst split is still a 0.200 lead over the best zero-shot challenger.** Its + recall halves against every other US split (0.390 against clovis's next-worst 0.650), but + the challengers fall further: gemini-pro lands at 0.343, its second-lowest US F1. So the + rural deficit is not RampNet-specific in the zero-shot field — whatever makes this town + hard makes it hard for everything that has never seen a curb ramp label. What it is *not* + is a ramp-poor or weak sample: laurens is ramp-**rich** (2.65 ramps/pano, third of ten). +2. **The supervised YOLO baseline beats RampNet here — the first split where that happens at + the pre-registered operating point.** Under the #71 protocol (conf 0.25) `y26_pano` reads + F1 **0.574** and `y11l_pano` **0.563**, against RampNet's 0.543; `y11x_pano_h200` is + 0.529. The margin is small and one-sided in a specific way: the YOLO arms win on + **recall** (0.450 and 0.402 vs 0.390) while RampNet keeps the precision (0.898 vs y26's + 0.794). And it is an operating-point result, not a curve result — read full-range at the + 0.05 floor RampNet's AP is **0.691**, still the highest of the four (y11l 0.689, y11x + 0.675, y26 0.627). The same pattern the matched-operating-point work found on + `manual_gold` shows up here in its sharpest form: RampNet's deployed 0.55 costs it more + on rural imagery than the architecture does. +3. **The Qwen-32B inversion recurs a fourth time, and this is its most extreme instance.** + 32B falls below 8B again (0.066 vs 0.210) by the documented mechanism — it stops firing: + **0.27 boxes/pano**, far below its previous US floor (gainesville 0.6, paterson 0.9), for + recall 0.036 on 249 ramps. Four splits now reproduce the swap, three of them + HIGH-confidence US GT, so 32B's conservatism on unusual-looking infrastructure is + established rather than suggestive; rural streetscape is simply the strongest dose of it + the benchmark has. OWLv2's 0.851 recall against RampNet's 0.390 is, as everywhere, mostly + density — 68.0 boxes/pano — and is not a recall ceiling anyone can deploy. + +**laurens_gsv** (86 reviewed panos, 220 GT ramps) — the **second imagery arm** of Laurens +(2026-08-31), reviewer confidence HIGH; Google Street View zoom-5 over the same 1.91 km² +footprint as `laurens_mapillary`. **Held out of the pooled basis** and of the tier rows: the +two arms sample one small town and largely the same physical ramps (59% of these panos sit +within 20 m of a `laurens_mapillary` one, median nearest neighbour 17.2 m), so pooling both +would double-count them and break the independence the Wilson intervals assume. It is held +out for non-independence, not for GT quality. It exists to answer one question (#151): is +Laurens hard because it is **rural**, or because of the **rig**? + +| model | P | R | F1 | AP | tp/fp/fn | +|---|---|---|---|---|---| +| **rampnet** | **0.933** | **0.509** | **0.659** | 0.494 | 112/8/108 | +| **molmo2-8B** (points) | 0.332 | 0.286 | **0.307** | – | 63/127/157 | +| gemini-3.1-pro-preview | 0.519 | 0.191 | 0.279 | – | 42/39/178 | +| gemini-3.6-flash | 0.355 | 0.223 | 0.274 | – | 49/89/171 | +| Qwen3-VL-8B-Instruct | 0.181 | 0.145 | 0.161 | – | 32/145/188 | +| **Qwen3-VL-32B-Instruct** | 0.250 | **0.009** | **0.018** | – | 2/6/218 | +| owlv2-large-patch14-ensemble | 0.028 | 0.855 | 0.055 | 0.035 | 188/6476/32 | +| grounding-dino-base | 0.028 | **0.882** | 0.054 | 0.038 | 194/6799/26 | + +### The rig, not the town (#151) + +Same town, same footprint, same rubric, same reviewer, two rigs. Every model was run on both +arms, so the imagery is the only thing that moves. ΔF1 is `laurens_gsv` minus +`laurens_mapillary`: + +| model | Mapillary (GoPro Max) | GSV (zoom 5) | ΔF1 | +|---|---|---|---| +| **rampnet** | 0.543 | **0.659** | **+0.115** | +| y11x_pano_h200 | 0.529 | 0.568 | +0.039 | +| y11l_pano | 0.563 | 0.587 | +0.024 | +| grounding-dino-base | 0.045 | 0.054 | +0.008 | +| gemini-3.6-flash | 0.277 | 0.274 | −0.003 | +| owlv2-large-patch14-ensemble | 0.062 | 0.055 | −0.007 | +| gemini-3.7-flash | 0.281 | 0.261 | −0.020 | +| allenai/Molmo2-8B | 0.339 | 0.307 | −0.032 | +| y26_pano | 0.574 | 0.538 | −0.036 | +| Qwen3-VL-32B-Instruct | 0.066 | 0.018 | −0.048 | +| Qwen3-VL-8B-Instruct | 0.210 | 0.161 | −0.049 | +| gemini-3.1-pro-preview | 0.343 | 0.279 | −0.064 | + +Three readings, in decreasing order of how well the data supports them: + +1. **The town is not the problem.** Every zero-shot challenger is flat or *worse* on the + imagery RampNet prefers — seven of eight move down, none moves up by more than 0.008. If + rural streetscape were intrinsically hard, the arm that is easier for RampNet would be + easier for them too. It is not. So Laurens' headline recall of 0.390 is not "rural defeats + detectors"; it is RampNet meeting an out-of-domain rig. +2. **The RampNet–YOLO ordering flips between the arms.** On Mapillary the supervised baseline + wins (`y26_pano` 0.574, `y11l_pano` 0.563, against 0.543); on GSV RampNet leads by 0.072 + (0.659 against `y11l_pano`'s 0.587). The claim "YOLO beats RampNet on the rural split" is + therefore really "**on non-GSV imagery**", and it does not survive changing the rig over + the same ground. +3. **Being GSV-trained is not the whole mechanism.** The YOLO arms were trained on the same + GSV-derived dataset, so if in-domain imagery were sufficient they should gain like RampNet + did. They barely move: +0.039, +0.024, and `y26_pano` goes the *other* way. RampNet's + +0.115 is three to five times larger, so **RampNet is markedly more rig-sensitive than a + YOLO trained on its own data** — which points at its preprocessing and 2048×4096 input + rather than at the training distribution alone. That is the open question this split hands + to #151, not one it closes. + +Two caveats travel with the table. **It is unpaired**: the arms are different panorama sets +with different GT (249 ramps against 220), so ΔF1 compares two samples of one town, not the +same corners twice. The ~51 corners inside 20 m of each other are the paired subset that +would turn this into a measurement, and both arms' detections are now committed, so that +analysis needs no new inference. And **the AP column is not comparable across the two arms +for RampNet**: `laurens_mapillary` has a 0.05-floor `op_cache` and `laurens_gsv` does not, so +its 0.494 is truncated at the deployed 0.55 while the other arm's 0.691 is not. The F1, P and +R columns are comparable — both arms are read at 0.55. + **sao_paulo** (125 reviewed panos, 281 GT ramps) — the second non-US split (2026-08-01), reviewer confidence **HIGH**; NBR 9050 design vocabulary on GSV (the same imagery path as bend/paterson/gainesville — see `benchmark/README.md` for what the split de-confounds) diff --git a/docs/model_scoreboard.md b/docs/model_scoreboard.md index 7ae41519..24191844 100644 --- a/docs/model_scoreboard.md +++ b/docs/model_scoreboard.md @@ -29,7 +29,18 @@ model, and the reasons are in "How to read this" below. | model | class | op | P | R | F1 | ΔF1 vs RampNet | AP (macro) | FP/pano | F1 range | |---|---|--:|--:|--:|--:|--:|--:|--:|:-:| -| **RampNet** | purpose-trained | 0.55 | **0.951** | **0.686** | **0.792** | — | 0.829 † | 0.1 | 0.54–0.85 | +| **RampNet** | purpose-trained | 0.55 | 0.951 | 0.686 | **0.792** | — | 0.829 † | 0.1 | 0.54–0.85 | +| YOLO11l (pano) | supervised baseline | 0.25 | 0.940 | 0.443 | 0.599 | -0.193 | 0.718 | 0.1 | 0.48–0.71 | +| YOLO11x (pano) | supervised baseline | 0.25 | **0.967** | 0.409 | 0.569 | -0.222 | 0.723 | 0.0 | 0.40–0.71 | +| YOLO26 (pano) | supervised baseline | 0.25 | 0.744 | 0.447 | 0.553 | -0.238 | 0.606 | 0.4 | 0.45–0.68 | +| Gemini 3.1 Pro | chat VLM | no score | 0.638 | 0.533 | 0.575 | -0.217 | – | 0.7 | 0.34–0.68 | +| Gemini 3.7 Flash | chat VLM | no score | 0.679 | 0.458 | 0.539 | -0.252 | – | 0.5 | 0.28–0.66 | +| Gemini 3.6 Flash | chat VLM | no score | 0.571 | 0.505 | 0.528 | -0.264 | – | 0.9 | 0.28–0.63 | +| Qwen3-VL-8B | chat VLM | no score | 0.312 | 0.340 | 0.322 | -0.469 | – | 1.8 | 0.21–0.41 | +| Qwen3-VL-32B | chat VLM | no score | 0.626 | 0.220 | 0.320 | -0.472 | – | 0.3 | 0.07–0.43 | +| Molmo2-8B | pointing model | no score | 0.423 | 0.425 | 0.419 | -0.372 | – | 1.4 | 0.33–0.51 | +| OWLv2-large | open-vocab detector | 0.05 floor | 0.033 | **0.932** | 0.065 | -0.727 | 0.092 | 65.2 | 0.05–0.08 | +| Grounding DINO | open-vocab detector | 0.05 floor | 0.028 | 0.848 | 0.053 | -0.738 | 0.036 | 73.5 | 0.03–0.07 | @@ -78,120 +89,12 @@ column exists to prevent. They are reported per split instead, at the split they | model | class | split | P | R | F1 | AP | FP/pano | tp/fp/fn | |---|---|---|--:|--:|--:|--:|--:|--:| -| YOLO11l (pano) | supervised baseline | `richmond` | 0.925 | 0.439 | 0.595 | 0.724 | 0.1 | 136/11/174 | -| YOLO11l (pano) | supervised baseline | `bend` | 0.964 | 0.566 | 0.713 | 0.778 | 0.1 | 185/7/142 | -| YOLO11l (pano) | supervised baseline | `clovis` | 0.916 | 0.446 | 0.600 | 0.708 | 0.1 | 87/8/108 | -| YOLO11l (pano) | supervised baseline | `morgantown` | 0.946 | 0.524 | 0.675 | 0.797 | 0.1 | 140/8/127 | -| YOLO11l (pano) | supervised baseline | `annapolis` | 0.969 | 0.320 | 0.481 | 0.619 | 0.0 | 94/3/200 | -| YOLO11l (pano) | supervised baseline | `paterson` | 0.946 | 0.491 | 0.647 | 0.793 | 0.1 | 194/11/201 | -| YOLO11l (pano) | supervised baseline | `gainesville` | 0.907 | 0.360 | 0.516 | 0.639 | 0.1 | 98/10/174 | -| YOLO11l (pano) | supervised baseline | `budapest_district5` | 0.786 | 0.147 | 0.247 | 0.443 | 0.1 | 44/12/256 | -| YOLO11l (pano) | supervised baseline | `sao_paulo` | 0.902 | 0.523 | 0.662 | 0.784 | 0.1 | 147/16/134 | -| YOLO11l (pano) | supervised baseline | `manual_gold` | 0.931 | 0.763 | 0.839 | 0.914 | 0.2 | 2992/222/927 | -| YOLO11x (pano) | supervised baseline | `richmond` | 0.952 | 0.384 | 0.547 | 0.748 | 0.0 | 119/6/191 | -| YOLO11x (pano) | supervised baseline | `bend` | 0.989 | 0.554 | 0.710 | 0.781 | 0.0 | 181/2/146 | -| YOLO11x (pano) | supervised baseline | `clovis` | 0.938 | 0.390 | 0.551 | 0.711 | 0.0 | 76/5/119 | -| YOLO11x (pano) | supervised baseline | `morgantown` | 0.993 | 0.524 | 0.686 | 0.790 | 0.0 | 140/1/127 | -| YOLO11x (pano) | supervised baseline | `annapolis` | 0.986 | 0.248 | 0.397 | 0.662 | 0.0 | 73/1/221 | -| YOLO11x (pano) | supervised baseline | `paterson` | 0.974 | 0.471 | 0.635 | 0.800 | 0.0 | 186/5/209 | -| YOLO11x (pano) | supervised baseline | `gainesville` | 0.948 | 0.338 | 0.499 | 0.616 | 0.0 | 92/5/180 | -| YOLO11x (pano) | supervised baseline | `budapest_district5` | 0.864 | 0.127 | 0.221 | 0.427 | 0.0 | 38/6/262 | -| YOLO11x (pano) | supervised baseline | `sao_paulo` | 0.923 | 0.512 | 0.659 | 0.783 | 0.1 | 144/12/137 | -| YOLO11x (pano) | supervised baseline | `manual_gold` | 0.956 | 0.767 | 0.851 | 0.931 | 0.1 | 3005/139/914 | -| YOLO26 (pano) | supervised baseline | `richmond` | 0.680 | 0.384 | 0.491 | 0.537 | 0.5 | 119/56/191 | -| YOLO26 (pano) | supervised baseline | `bend` | 0.733 | 0.563 | 0.637 | 0.661 | 0.6 | 184/67/143 | -| YOLO26 (pano) | supervised baseline | `clovis` | 0.687 | 0.462 | 0.552 | 0.596 | 0.3 | 90/41/105 | -| YOLO26 (pano) | supervised baseline | `morgantown` | 0.802 | 0.592 | 0.681 | 0.740 | 0.3 | 158/39/109 | -| YOLO26 (pano) | supervised baseline | `annapolis` | 0.758 | 0.320 | 0.450 | 0.493 | 0.2 | 94/30/200 | -| YOLO26 (pano) | supervised baseline | `paterson` | 0.786 | 0.473 | 0.591 | 0.712 | 0.4 | 187/51/208 | -| YOLO26 (pano) | supervised baseline | `gainesville` | 0.709 | 0.331 | 0.451 | 0.481 | 0.3 | 90/37/182 | -| YOLO26 (pano) | supervised baseline | `budapest_district5` | 0.639 | 0.177 | 0.277 | 0.350 | 0.2 | 53/30/247 | -| YOLO26 (pano) | supervised baseline | `sao_paulo` | 0.723 | 0.520 | 0.605 | 0.658 | 0.4 | 146/56/135 | -| YOLO26 (pano) | supervised baseline | `manual_gold` | 0.740 | 0.739 | 0.739 | 0.843 | 1.0 | 2896/1020/1023 | | Mask2Former Vistas (curb cut) | supervised transfer | `richmond` | 0.411 | 0.697 | 0.517 | 0.513 | 2.5 | 216/309/94 | | Mask2Former Vistas (+curb) | supervised transfer | `richmond` | 0.126 | 0.648 | 0.210 | 0.089 | 11.3 | 201/1399/109 | -| Gemini 3.1 Pro | chat VLM | `richmond` | 0.634 | 0.703 | 0.667 | – | 1.0 | 218/126/92 | -| Gemini 3.1 Pro | chat VLM | `bend` | 0.706 | 0.581 | 0.638 | – | 0.7 | 190/79/137 | -| Gemini 3.1 Pro | chat VLM | `clovis` | 0.543 | 0.487 | 0.514 | – | 0.6 | 95/80/100 | -| Gemini 3.1 Pro | chat VLM | `morgantown` | 0.679 | 0.610 | 0.643 | – | 0.6 | 163/77/104 | -| Gemini 3.1 Pro | chat VLM | `annapolis` | 0.613 | 0.527 | 0.567 | – | 0.8 | 155/98/139 | -| Gemini 3.1 Pro | chat VLM | `paterson` | 0.852 | 0.567 | 0.681 | – | 0.3 | 224/39/171 | -| Gemini 3.1 Pro | chat VLM | `gainesville` | 0.564 | 0.533 | 0.548 | – | 0.9 | 145/112/127 | -| Gemini 3.1 Pro | chat VLM | `budapest_district5` | 0.434 | 0.340 | 0.381 | – | 1.1 | 102/133/198 | -| Gemini 3.1 Pro | chat VLM | `sao_paulo` | 0.463 | 0.445 | 0.454 | – | 1.2 | 125/145/156 | | Claude Opus 5 (low) | chat VLM | `annapolis` | 0.572 | 0.605 | 0.588 | – | 1.1 | 178/133/116 | -| Gemini 3.7 Flash | chat VLM | `richmond` | 0.744 | 0.600 | 0.664 | – | 0.5 | 186/64/124 | -| Gemini 3.7 Flash | chat VLM | `bend` | 0.713 | 0.578 | 0.639 | – | 0.7 | 189/76/138 | -| Gemini 3.7 Flash | chat VLM | `clovis` | 0.571 | 0.451 | 0.504 | – | 0.5 | 88/66/107 | -| Gemini 3.7 Flash | chat VLM | `morgantown` | 0.701 | 0.517 | 0.595 | – | 0.5 | 138/59/129 | -| Gemini 3.7 Flash | chat VLM | `annapolis` | 0.734 | 0.459 | 0.565 | – | 0.4 | 135/49/159 | -| Gemini 3.7 Flash | chat VLM | `paterson` | 0.910 | 0.458 | 0.609 | – | 0.1 | 181/18/214 | -| Gemini 3.7 Flash | chat VLM | `gainesville` | 0.511 | 0.412 | 0.456 | – | 0.9 | 112/107/160 | -| Gemini 3.7 Flash | chat VLM | `budapest_district5` | 0.484 | 0.260 | 0.338 | – | 0.7 | 78/83/222 | -| Gemini 3.7 Flash | chat VLM | `sao_paulo` | 0.561 | 0.263 | 0.358 | – | 0.5 | 74/58/207 | -| Gemini 3.7 Flash | chat VLM | `manual_gold` | 0.735 | 0.411 | 0.527 | – | 0.6 | 1612/582/2307 | -| Gemini 3.6 Flash | chat VLM | `richmond` | 0.626 | 0.642 | 0.634 | – | 1.0 | 199/119/111 | -| Gemini 3.6 Flash | chat VLM | `bend` | 0.608 | 0.587 | 0.597 | – | 1.1 | 192/124/135 | -| Gemini 3.6 Flash | chat VLM | `clovis` | 0.464 | 0.503 | 0.483 | – | 0.9 | 98/113/97 | -| Gemini 3.6 Flash | chat VLM | `morgantown` | 0.636 | 0.629 | 0.633 | – | 0.8 | 168/96/99 | -| Gemini 3.6 Flash | chat VLM | `annapolis` | 0.637 | 0.490 | 0.554 | – | 0.7 | 144/82/150 | -| Gemini 3.6 Flash | chat VLM | `paterson` | 0.744 | 0.514 | 0.608 | – | 0.6 | 203/70/192 | -| Gemini 3.6 Flash | chat VLM | `gainesville` | 0.404 | 0.478 | 0.438 | – | 1.5 | 130/192/142 | -| Gemini 3.6 Flash | chat VLM | `budapest_district5` | 0.353 | 0.320 | 0.336 | – | 1.4 | 96/176/204 | -| Gemini 3.6 Flash | chat VLM | `sao_paulo` | 0.333 | 0.359 | 0.346 | – | 1.6 | 101/202/180 | | Claude Opus 5 (high) | chat VLM | `annapolis` | 0.430 | 0.656 | 0.520 | – | 2.0 | 193/256/101 | | Claude Sonnet 5 (low) | chat VLM | `annapolis` | 0.589 | 0.381 | 0.463 | – | 0.6 | 112/78/182 | | Claude Sonnet 5 (high) | chat VLM | `annapolis` | 0.506 | 0.415 | 0.456 | – | 1.0 | 122/119/172 | -| Qwen3-VL-32B | chat VLM | `richmond` | 0.760 | 0.297 | 0.427 | – | 0.2 | 92/29/218 | -| Qwen3-VL-32B | chat VLM | `bend` | 0.706 | 0.294 | 0.415 | – | 0.4 | 96/40/231 | -| Qwen3-VL-32B | chat VLM | `clovis` | 0.696 | 0.200 | 0.311 | – | 0.1 | 39/17/156 | -| Qwen3-VL-32B | chat VLM | `morgantown` | 0.675 | 0.311 | 0.426 | – | 0.3 | 83/40/184 | -| Qwen3-VL-32B | chat VLM | `annapolis` | 0.608 | 0.296 | 0.398 | – | 0.4 | 87/56/207 | -| Qwen3-VL-32B | chat VLM | `paterson` | 0.813 | 0.220 | 0.347 | – | 0.2 | 87/20/308 | -| Qwen3-VL-32B | chat VLM | `gainesville` | 0.392 | 0.107 | 0.168 | – | 0.4 | 29/45/243 | -| Qwen3-VL-32B | chat VLM | `budapest_district5` | 0.433 | 0.043 | 0.079 | – | 0.1 | 13/17/287 | -| Qwen3-VL-32B | chat VLM | `sao_paulo` | 0.506 | 0.139 | 0.218 | – | 0.3 | 39/38/242 | -| Qwen3-VL-32B | chat VLM | `manual_gold` | 0.739 | 0.177 | 0.285 | – | 0.2 | 693/245/3226 | -| Qwen3-VL-8B | chat VLM | `richmond` | 0.323 | 0.452 | 0.377 | – | 2.4 | 140/293/170 | -| Qwen3-VL-8B | chat VLM | `bend` | 0.381 | 0.339 | 0.359 | – | 1.6 | 111/180/216 | -| Qwen3-VL-8B | chat VLM | `clovis` | 0.226 | 0.297 | 0.257 | – | 1.6 | 58/199/137 | -| Qwen3-VL-8B | chat VLM | `morgantown` | 0.304 | 0.386 | 0.340 | – | 1.9 | 103/236/164 | -| Qwen3-VL-8B | chat VLM | `annapolis` | 0.304 | 0.354 | 0.327 | – | 1.9 | 104/238/190 | -| Qwen3-VL-8B | chat VLM | `paterson` | 0.460 | 0.362 | 0.405 | – | 1.3 | 143/168/252 | -| Qwen3-VL-8B | chat VLM | `gainesville` | 0.278 | 0.331 | 0.302 | – | 1.9 | 90/234/182 | -| Qwen3-VL-8B | chat VLM | `budapest_district5` | 0.184 | 0.157 | 0.169 | – | 1.7 | 47/209/253 | -| Qwen3-VL-8B | chat VLM | `sao_paulo` | 0.229 | 0.210 | 0.219 | – | 1.6 | 59/199/222 | -| Qwen3-VL-8B | chat VLM | `manual_gold` | 0.445 | 0.341 | 0.386 | – | 1.7 | 1338/1667/2581 | -| Molmo2-8B | pointing model | `richmond` | 0.410 | 0.516 | 0.457 | – | 1.9 | 160/230/150 | -| Molmo2-8B | pointing model | `bend` | 0.510 | 0.401 | 0.449 | – | 1.1 | 131/126/196 | -| Molmo2-8B | pointing model | `clovis` | 0.335 | 0.441 | 0.381 | – | 1.4 | 86/171/109 | -| Molmo2-8B | pointing model | `morgantown` | 0.466 | 0.461 | 0.463 | – | 1.1 | 123/141/144 | -| Molmo2-8B | pointing model | `annapolis` | 0.434 | 0.415 | 0.424 | – | 1.3 | 122/159/172 | -| Molmo2-8B | pointing model | `paterson` | 0.585 | 0.453 | 0.511 | – | 1.0 | 179/127/216 | -| Molmo2-8B | pointing model | `gainesville` | 0.282 | 0.393 | 0.329 | – | 2.2 | 107/272/165 | -| Molmo2-8B | pointing model | `budapest_district5` | 0.260 | 0.290 | 0.274 | – | 2.0 | 87/247/213 | -| Molmo2-8B | pointing model | `sao_paulo` | 0.319 | 0.335 | 0.326 | – | 1.6 | 94/201/187 | -| Molmo2-8B | pointing model | `manual_gold` | 0.511 | 0.360 | 0.422 | – | 1.3 | 1409/1346/2510 | -| OWLv2-large | open-vocab detector | `richmond` | 0.033 | 0.971 | 0.064 | 0.104 | 71.0 | 301/8799/9 | -| OWLv2-large | open-vocab detector | `bend` | 0.037 | 0.954 | 0.071 | 0.093 | 74.4 | 312/8187/15 | -| OWLv2-large | open-vocab detector | `clovis` | 0.025 | 0.913 | 0.049 | 0.067 | 55.3 | 178/6910/17 | -| OWLv2-large | open-vocab detector | `morgantown` | 0.037 | 0.948 | 0.071 | 0.114 | 52.9 | 253/6613/14 | -| OWLv2-large | open-vocab detector | `annapolis` | 0.032 | 0.959 | 0.063 | 0.126 | 67.6 | 282/8444/12 | -| OWLv2-large | open-vocab detector | `paterson` | 0.040 | 0.894 | 0.077 | 0.116 | 67.2 | 353/8398/42 | -| OWLv2-large | open-vocab detector | `gainesville` | 0.031 | 0.967 | 0.060 | 0.063 | 65.5 | 263/8185/9 | -| OWLv2-large | open-vocab detector | `budapest_district5` | 0.032 | 0.930 | 0.062 | 0.089 | 67.7 | 279/8467/21 | -| OWLv2-large | open-vocab detector | `sao_paulo` | 0.027 | 0.922 | 0.052 | 0.050 | 75.5 | 259/9433/22 | -| OWLv2-large | open-vocab detector | `manual_gold` | 0.046 | 0.907 | 0.088 | 0.097 | 73.4 | 3554/73441/365 | -| Grounding DINO | open-vocab detector | `richmond` | 0.028 | 0.852 | 0.053 | 0.033 | 75.2 | 264/9321/46 | -| Grounding DINO | open-vocab detector | `bend` | 0.038 | 0.850 | 0.073 | 0.049 | 63.4 | 278/6969/49 | -| Grounding DINO | open-vocab detector | `clovis` | 0.018 | 0.872 | 0.035 | 0.026 | 75.5 | 170/9432/25 | -| Grounding DINO | open-vocab detector | `morgantown` | 0.022 | 0.831 | 0.042 | 0.028 | 79.9 | 222/9991/45 | -| Grounding DINO | open-vocab detector | `annapolis` | 0.029 | 0.898 | 0.055 | 0.042 | 71.9 | 264/8992/30 | -| Grounding DINO | open-vocab detector | `paterson` | 0.036 | 0.803 | 0.068 | 0.044 | 68.4 | 317/8551/78 | -| Grounding DINO | open-vocab detector | `gainesville` | 0.028 | 0.893 | 0.055 | 0.040 | 66.6 | 243/8328/29 | -| Grounding DINO | open-vocab detector | `budapest_district5` | 0.021 | 0.787 | 0.042 | 0.025 | 86.0 | 236/10755/64 | -| Grounding DINO | open-vocab detector | `sao_paulo` | 0.025 | 0.797 | 0.049 | 0.034 | 69.4 | 224/8676/57 | -| Grounding DINO | open-vocab detector | `manual_gold` | 0.043 | 0.856 | 0.082 | 0.067 | 75.0 | 3353/74951/566 | @@ -215,26 +118,26 @@ numbers are directly above them in `model_comparison.md`: -| model | rich | bend | clovis | morg | annap | pater | gaines | laurens | **pooled** | budapest † | sao_paulo † | manual_gold † | -|---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:| -| **RampNet** | **0.855** | **0.850** | **0.801** | **0.835** | **0.839** | **0.805** | **0.803** | **0.543** | **0.792** | **0.644** | **0.777** | **0.908** | -| YOLO11l (pano) | 0.595 | 0.713 | 0.600 | 0.675 | 0.481 | 0.647 | 0.516 | – | – | 0.247 | 0.662 | 0.839 | -| YOLO11x (pano) | 0.547 | 0.710 | 0.551 | 0.686 | 0.397 | 0.635 | 0.499 | – | – | 0.221 | 0.659 | 0.851 | -| YOLO26 (pano) | 0.491 | 0.637 | 0.552 | 0.681 | 0.450 | 0.591 | 0.451 | – | – | 0.277 | 0.605 | 0.739 | -| Mask2Former Vistas (curb cut) | 0.517 | – | – | – | – | – | – | – | – | – | – | – | -| Mask2Former Vistas (+curb) | 0.210 | – | – | – | – | – | – | – | – | – | – | – | -| Gemini 3.1 Pro | 0.667 | 0.638 | 0.514 | 0.643 | 0.567 | 0.681 | 0.548 | – | – | 0.381 | 0.454 | – | -| Claude Opus 5 (low) | – | – | – | – | 0.588 | – | – | – | – | – | – | – | -| Gemini 3.7 Flash | 0.664 | 0.639 | 0.504 | 0.595 | 0.565 | 0.609 | 0.456 | – | – | 0.338 | 0.358 | 0.527 | -| Gemini 3.6 Flash | 0.634 | 0.597 | 0.483 | 0.633 | 0.554 | 0.608 | 0.438 | – | – | 0.336 | 0.346 | – | -| Claude Opus 5 (high) | – | – | – | – | 0.520 | – | – | – | – | – | – | – | -| Claude Sonnet 5 (low) | – | – | – | – | 0.463 | – | – | – | – | – | – | – | -| Claude Sonnet 5 (high) | – | – | – | – | 0.456 | – | – | – | – | – | – | – | -| Qwen3-VL-32B | 0.427 | 0.415 | 0.311 | 0.426 | 0.398 | 0.347 | 0.168 | – | – | 0.079 | 0.218 | 0.285 | -| Qwen3-VL-8B | 0.377 | 0.359 | 0.257 | 0.340 | 0.327 | 0.405 | 0.302 | – | – | 0.169 | 0.219 | 0.386 | -| Molmo2-8B | 0.457 | 0.449 | 0.381 | 0.463 | 0.424 | 0.511 | 0.329 | – | – | 0.274 | 0.326 | 0.422 | -| OWLv2-large | 0.064 | 0.071 | 0.049 | 0.071 | 0.063 | 0.077 | 0.060 | – | – | 0.062 | 0.052 | 0.088 | -| Grounding DINO | 0.053 | 0.073 | 0.035 | 0.042 | 0.055 | 0.068 | 0.055 | – | – | 0.042 | 0.049 | 0.082 | +| model | rich | bend | clovis | morg | annap | pater | gaines | laur_mly | **pooled** | laur_gsv † | budapest † | sao_paulo † | manual_gold † | +|---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:| +| **RampNet** | **0.855** | **0.850** | **0.801** | **0.835** | **0.839** | **0.805** | **0.803** | 0.543 | **0.792** | **0.659** | **0.644** | **0.777** | **0.908** | +| YOLO11l (pano) | 0.595 | 0.713 | 0.600 | 0.675 | 0.481 | 0.647 | 0.516 | 0.563 | 0.599 | 0.587 | 0.247 | 0.662 | 0.839 | +| YOLO11x (pano) | 0.547 | 0.710 | 0.551 | 0.686 | 0.397 | 0.635 | 0.499 | 0.529 | 0.569 | 0.568 | 0.221 | 0.659 | 0.851 | +| YOLO26 (pano) | 0.491 | 0.637 | 0.552 | 0.681 | 0.450 | 0.591 | 0.451 | **0.574** | 0.553 | 0.538 | 0.277 | 0.605 | 0.739 | +| Gemini 3.1 Pro | 0.667 | 0.638 | 0.514 | 0.643 | 0.567 | 0.681 | 0.548 | 0.343 | 0.575 | 0.279 | 0.381 | 0.454 | – | +| Gemini 3.7 Flash | 0.664 | 0.639 | 0.504 | 0.595 | 0.565 | 0.609 | 0.456 | 0.281 | 0.539 | 0.261 | 0.338 | 0.358 | 0.527 | +| Gemini 3.6 Flash | 0.634 | 0.597 | 0.483 | 0.633 | 0.554 | 0.608 | 0.438 | 0.277 | 0.528 | 0.274 | 0.336 | 0.346 | – | +| Qwen3-VL-8B | 0.377 | 0.359 | 0.257 | 0.340 | 0.327 | 0.405 | 0.302 | 0.210 | 0.322 | 0.161 | 0.169 | 0.219 | 0.386 | +| Qwen3-VL-32B | 0.427 | 0.415 | 0.311 | 0.426 | 0.398 | 0.347 | 0.168 | 0.066 | 0.320 | 0.018 | 0.079 | 0.218 | 0.285 | +| Molmo2-8B | 0.457 | 0.449 | 0.381 | 0.463 | 0.424 | 0.511 | 0.329 | 0.339 | 0.419 | 0.307 | 0.274 | 0.326 | 0.422 | +| OWLv2-large | 0.064 | 0.071 | 0.049 | 0.071 | 0.063 | 0.077 | 0.060 | 0.062 | 0.065 | 0.055 | 0.062 | 0.052 | 0.088 | +| Grounding DINO | 0.053 | 0.073 | 0.035 | 0.042 | 0.055 | 0.068 | 0.055 | 0.045 | 0.053 | 0.054 | 0.042 | 0.049 | 0.082 | +| Mask2Former Vistas (curb cut) | 0.517 | – | – | – | – | – | – | – | – | – | – | – | – | +| Mask2Former Vistas (+curb) | 0.210 | – | – | – | – | – | – | – | – | – | – | – | – | +| Claude Opus 5 (low) | – | – | – | – | 0.588 | – | – | – | – | – | – | – | – | +| Claude Opus 5 (high) | – | – | – | – | 0.520 | – | – | – | – | – | – | – | – | +| Claude Sonnet 5 (low) | – | – | – | – | 0.463 | – | – | – | – | – | – | – | – | +| Claude Sonnet 5 (high) | – | – | – | – | 0.456 | – | – | – | – | – | – | – | – | @@ -404,7 +307,8 @@ apart without failing CI: | `annapolis` | 0.734 | **0.875** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | | `paterson` | 0.681 | **0.748** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | | `gainesville` | 0.691 | **0.846** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | -| `laurens` | 0.377 | **0.691** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | +| `laurens_mapillary` | 0.377 | **0.691** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | +| `laurens_gsv` | 0.494 | 0.494 | bundle — 0.55 floor, no `op_cache` | **truncated**; not comparable with the rows above | | `budapest_district5` | 0.478 | **0.648** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | | `sao_paulo` | 0.666 | **0.812** | `op_cache` (0.05 floor) | truncated at the deployed 0.55 | | `manual_gold` | 0.917 | 0.917 | bundle — already at 0.05 | no truncation to undo; flip-TTA export | @@ -483,7 +387,8 @@ Omissions are content, so they are named rather than left as blanks: | `annapolis` | pooled | 125 | 294 | US deployment city, verdict-grade GT | | `paterson` | pooled | 125 | 395 | US deployment city, verdict-grade GT | | `gainesville` | pooled | 125 | 272 | US deployment city, verdict-grade GT | -| `laurens` | pooled | 94 | 249 | US deployment city, verdict-grade GT | +| `laurens_mapillary` | pooled | 94 | 249 | US deployment city, verdict-grade GT | +| `laurens_gsv` | held out † | 86 | 220 | second imagery arm of laurens, which is already pooled through laurens_mapillary -- the two arms sample one town and largely the same physical ramps (59% of gsv panos within 20 m of a mapillary one, median NN 17.2 m), so pooling both would double-count them and break the independence the Wilson intervals assume (GT is HIGH confidence; held out for non-independence, not GT quality) | | `budapest_district5` | held out † | 125 | 300 | single-rater GT at low reviewer confidence (docs/model_comparison.md: do not pool) | | `sao_paulo` | held out † | 125 | 281 | non-US city — the pooled recommendation is a US-deployment basis (GT is HIGH reviewer confidence; held out for geography, not GT quality) | | `manual_gold` | held out † | 1000 | 3919 | in-distribution GSV + independently-labelled GT (in-domain reference, not a deployment city) | diff --git a/docs/operating_point.md b/docs/operating_point.md index 19dba9ae..bd1a81c7 100644 --- a/docs/operating_point.md +++ b/docs/operating_point.md @@ -262,26 +262,35 @@ recommendation below rests on the recall-first policy, the density budget and re instead. Note also how the per-split optima (the dots) cluster in a narrow band while the *levels* differ a lot — that is the same pattern the per-tier table shows. -### Pooled across the seven US city splits (n = 859 panos) +### Pooled across the eight US city splits (n = 953 panos) Wilson 95% intervals in brackets. | threshold | precision | recall | F1 | detections/pano | |---|---|---|---|---| -| 0.25 | 0.877 [0.861, 0.891] | 0.805 [0.788, 0.822] | 0.840 | 2.34 | -| 0.30 | 0.900 [0.886, 0.913] | 0.793 [0.775, 0.810] | 0.843 | 2.23 | -| **0.32** | 0.908 [0.894, 0.920] | 0.789 [0.771, 0.806] | **0.844** | 2.20 | -| 0.35 | 0.915 [0.901, 0.927] | 0.780 [0.761, 0.797] | 0.842 | 2.15 | -| 0.40 | 0.933 [0.920, 0.944] | 0.768 [0.750, 0.786] | 0.843 | 2.07 | -| 0.45 | 0.944 [0.932, 0.954] | 0.752 [0.733, 0.770] | 0.837 | 1.99 | -| 0.50 | 0.960 [0.949, 0.968] | 0.738 [0.719, 0.757] | 0.835 | 1.93 | -| **0.55** (deployed) | 0.964 [0.953, 0.972] | 0.722 [0.703, 0.741] | 0.826 | 1.86 | - -**F1 is flat.** It varies by under 0.01 across 0.25–0.50 and only drops (to 0.826) at the -deployed 0.55 itself, so F1-optimality alone does not pick an operating point — a finding -worth stating plainly, because "F1-optimal" is the obvious criterion and here it is nearly -uninformative. What *does* move decisively is recall: 0.722 → 0.789 at 0.32, with -**non-overlapping** confidence intervals. +| 0.25 | 0.876 [0.861, 0.890] | 0.780 [0.763, 0.797] | 0.825 | 2.28 | +| 0.30 | 0.899 [0.885, 0.912] | 0.764 [0.746, 0.781] | 0.826 | 2.17 | +| **0.32** | 0.907 [0.893, 0.919] | 0.760 [0.742, 0.777] | **0.827** | 2.14 | +| 0.35 | 0.913 [0.900, 0.925] | 0.749 [0.731, 0.766] | 0.823 | 2.09 | +| 0.40 | 0.930 [0.918, 0.941] | 0.735 [0.717, 0.753] | 0.821 | 2.01 | +| 0.45 | 0.941 [0.929, 0.951] | 0.716 [0.697, 0.734] | 0.813 | 1.92 | +| 0.50 | 0.955 [0.944, 0.964] | 0.701 [0.682, 0.719] | 0.809 | 1.85 | +| **0.55** (deployed) | 0.959 [0.949, 0.968] | 0.686 [0.667, 0.705] | 0.800 | 1.80 | + +Re-pooled 2026-09-01 over eight splits: `laurens_mapillary` joined the pool and it is +the split RampNet does worst on by a factor of two (recall 0.390), so every recall and +F1 cell here is lower than the seven-split version this table replaces (0.55 was +0.964 / 0.722 / 0.826). **Precision is essentially untouched** — 0.964 → 0.959 — which +is the point: Laurens costs recall, not precision. The threshold ordering, the +F1-optimum and the recommendation are all unchanged. + +**F1 is flat.** It varies by under 0.02 across 0.25–0.50 and drops furthest (to 0.800) at +the deployed 0.55 itself, so F1-optimality alone does not pick an operating point — a +finding worth stating plainly, because "F1-optimal" is the obvious criterion and here it is +nearly uninformative. What *does* move decisively is recall: 0.686 → 0.760 at 0.32, with +**non-overlapping** confidence intervals. (Seven-split: 0.722 → 0.789, span under 0.01. +Adding Laurens widened the F1 span because its own curve peaks far lower, at 0.15; it did +not move the pooled optimum, which is still 0.32.) ### Per split, 0.55 → 0.32 @@ -294,6 +303,7 @@ uninformative. What *does* move decisively is recall: 0.722 → 0.789 at 0.32, w | annapolis | 0.973 | 0.738 | 0.912 | 0.806 | +0.068 | −0.062 | 1.82 → 2.13 | 0.32 | | paterson | 0.971 | 0.681 | 0.953 | 0.716 | +0.035 | −0.018 | 2.25 → 2.44 | 0.26 | | gainesville | 0.948 | 0.673 | 0.867 | 0.768 | +0.096 | −0.081 | 1.58 → 1.98 | 0.38 | +| laurens_mapillary | 0.898 | 0.390 | 0.895 | 0.514 | +0.124 | −0.003 | 1.18 → 1.57 | 0.15 | | budapest † | 0.874 | 0.510 | 0.718 | 0.637 | +0.127 | −0.156 | 1.51 → 2.27 | 0.37 | | sao_paulo § | 0.906 | 0.651 | 0.809 | 0.783 | **+0.132** | −0.097 | 1.82 → 2.50 | 0.31 | | manual_gold ‡ | 0.955 | 0.849 | 0.926 | 0.884 | +0.035 | −0.028 | 3.49 → 3.74 | 0.36 | diff --git a/docs/replication.md b/docs/replication.md index 883c7077..df5cd91d 100644 --- a/docs/replication.md +++ b/docs/replication.md @@ -20,7 +20,7 @@ lives on one machine. | `benchmark/miss_taxonomy_46/*.json` (human verdicts) | small | **committed** | ✅ | | RampNet model weights | — | HF `projectsidewalk/rampnet-model` | ✅ | | Stage 1 dataset | **463 GB** (test split ~44 GB) | HF `projectsidewalk/rampnet-dataset` | ✅ | -| `benchmark/model_detections/` (challenger detections) | 23.1 MB (114 files) | **committed** ✅ | ✅ | +| `benchmark/model_detections/` (challenger detections) | 25.1 MB (136 files) | **committed** ✅ | ✅ | | **`location_data/` (the paper's government inventories)** | 71.8 MB | **committed** ✅ | ✅ | | **`street_data/` derivative (what the pipeline actually reads)** | 18.7 MB | **committed** ✅ | ✅ | | `street_data/` raw downloads (NY file alone is 669 MB) | 801 MB | git-ignored; HF #21 pending | ⚠️ superseded by the derivative | @@ -41,14 +41,14 @@ in this sentence — the list here was one of the things that drifted. single-panorama shards keyed by an opaque SHA-1 of (label, signature, city, pano), unreadable without reconstructing detector signatures. `scripts/analysis/export_model_cache.py` consolidates it into human-readable files, one per (model, split), keyed by panorama id with the detector -signature recorded inside. As of 2026-08-18 that is **114 files, 23.1 MB**, and every one of +signature recorded inside. As of 2026-09-01 that is **136 files, 25.1 MB**, and every one of them belongs to a registered leg: | what | files | where it is written up | |---|---:|---| -| the standing zero-shot roster, ten splits each (two Gemini legs are absent on `manual_gold`) | 68 | the roster tables in [`model_comparison.md`](model_comparison.md) | -| `gemini-3.7-flash`, ten splits, published ahead of its write-up (#120) | 10 | §below | -| the supervised YOLO pano trio, ten splits each (#51) | 30 | [`model_comparison.md` §supervised baseline](model_comparison.md), and the [training record](../scripts/model_comparison/yolo_baseline/README.md) | +| the standing zero-shot roster, twelve splits each (two Gemini legs are absent on `manual_gold`) | 82 | the roster tables in [`model_comparison.md`](model_comparison.md) | +| `gemini-3.7-flash`, twelve splits, published ahead of its write-up (#120) | 12 | §below | +| the supervised YOLO pano trio, twelve splits each (#51) | 36 | [`model_comparison.md` §supervised baseline](model_comparison.md), and the [training record](../scripts/model_comparison/yolo_baseline/README.md) | | the four annapolis Claude legs (#122) | 4 | [`model_comparison.md` §Claude](model_comparison.md) | | the two Mapillary Vistas class-set arms, richmond only (#126) | 2 | [`model_comparison.md` §Vistas](model_comparison.md) | @@ -110,7 +110,7 @@ test rather than a promise. #### The gemini-3.7-flash leg is published but off the default roster -`benchmark/model_detections/gemini-3.7-flash__*.json` covers **all ten splits**, including +`benchmark/model_detections/gemini-3.7-flash__*.json` covers **all twelve splits**, including `manual_gold` (1,000 panoramas, 0 uncached — that leg finished 2026-08-15 08:04 UTC, after the first nine were exported). It is currently the only Gemini with a `manual_gold` file: the other two are absent for a different reason, their `manual_gold` detections not being in the cache @@ -122,6 +122,43 @@ python scripts/analysis/export_model_cache.py --verify --models gemini:gemini-3. # -> compared 10 (model, split) pair(s); published detections score IDENTICALLY ``` +#### The two Laurens arms (2026-09-01) + +Both arms of Laurens — `laurens_mapillary` (pooled) and `laurens_gsv` (held out, the #151 +discriminator) — were run through the identical leg list, which is what makes their ΔF1 +table in `model_comparison.md` a comparison of rigs rather than of protocols. Substitute +`laurens_gsv` for `laurens_mapillary` in every command below to reproduce the second arm. + +#### The `laurens_mapillary` legs (2026-09-01) + +The eighth US split arrived after every challenger had already been run, so all eleven legs +were produced for it in one pass. Exact commands, in order: + +```bash +# 1. the seven standing roster challengers (owlv2/gdino on an A40, Qwen-32B on 2x L40S) +python scripts/model_comparison/compare.py benchmark/laurens_mapillary --models rampnet,owlv2,gdino --sweep +python scripts/model_comparison/compare.py benchmark/laurens_mapillary --models rampnet,qwen:Qwen/Qwen3-VL-8B-Instruct +QWEN_MODEL=Qwen/Qwen3-VL-32B-Instruct BUNDLE=benchmark/laurens_mapillary sbatch -A --nodes=1 --gpus-per-node=2 scripts/model_comparison/run_qwen.slurm +python scripts/model_comparison/compare.py benchmark/laurens_mapillary --models rampnet,molmo:allenai/Molmo2-8B # transformers==4.57.1 env, see below +python scripts/model_comparison/compare.py benchmark/laurens_mapillary --models gemini:gemini-3.6-flash # and 3.1-pro-preview, and 3.7-flash + +# 2. the YOLO trio, under the #71 pre-registered protocol +python scripts/model_comparison/compare.py benchmark/laurens_mapillary --models yolo:yolo_ckpts/y11l_pano.pt,yolo:yolo_ckpts/y26_pano.pt,yolo:yolo_ckpts/y11x_pano_h200.pt --tiling none --yolo-imgsz 1280 --op-threshold 0.25 --sweep + +# 3. export -- three invocations, because --models defaults to CHALLENGERS only +python scripts/analysis/export_model_cache.py --splits laurens_mapillary +python scripts/analysis/export_model_cache.py --splits laurens_mapillary --models gemini:gemini-3.7-flash +python scripts/analysis/export_model_cache.py --splits laurens_mapillary --models yolo:yolo_ckpts/y11l_pano.pt,yolo:yolo_ckpts/y26_pano.pt,yolo:yolo_ckpts/y11x_pano_h200.pt --tiling none --yolo-imgsz 1280 +# ...then the same three with --verify; all reported 0 uncached and IDENTICAL scores. +``` + +**The `--tiling none --yolo-imgsz 1280` flags are load-bearing on the export, not just the +run.** The exporter defaults to `--tiling perspective --yolo-imgsz 1024`, and those settings +enter the detector signature and therefore the cache key. Export without them and the +lookup misses every pano: the command reports `94 uncached` and writes empty files rather +than failing. A non-zero `uncached` column on a leg that has already been run means the +signature was reconstructed wrong — check it before committing anything. + `gemini:gemini-3.7-flash` is registered in `rampnet/roster.py` as published-but-off-roster, so it is not in `CHALLENGERS` and not what `--models` defaults to. The two commands above are therefore not optional detail: the default `--verify` skips these ten files entirely and diff --git a/scripts/analysis/farfield_forensics.py b/scripts/analysis/farfield_forensics.py index 1d481886..d51a4071 100644 --- a/scripts/analysis/farfield_forensics.py +++ b/scripts/analysis/farfield_forensics.py @@ -54,10 +54,11 @@ # forensics.py asserts that the committed analysis_out/farfield_forensics.json still # reproduces the numbers quoted in the issue's section 0c, byte for byte. It therefore # pins its own split tuple instead of importing the live `US_SPLITS`, which would let -# any newly registered city silently restate a published finding. laurens (the first -# rural split) is deliberately absent for that reason, NOT because it is held out of -# the pooled recommendation -- it is pooled everywhere else. To fold a new split in, -# re-run the study, re-quote it, and update the section-0c expectations together. +# any newly registered city silently restate a published finding. laurens_mapillary +# (the first rural split) is deliberately absent for that reason, NOT because it is +# held out of the pooled recommendation -- it is pooled everywhere else. To fold a +# new split in, re-run the study, re-quote it, and update the section-0c +# expectations together. from miss_gallery import ( # noqa: E402 JUDGEABLE_SOURCE_PX, MODEL_WIDTH, load_queue, source_px, tag_key) diff --git a/scripts/analysis/low_floor_sweep.py b/scripts/analysis/low_floor_sweep.py index a02e6bfc..cdf23e75 100644 --- a/scripts/analysis/low_floor_sweep.py +++ b/scripts/analysis/low_floor_sweep.py @@ -66,13 +66,26 @@ # The seven US city splits carry verdict-grade GT and are the recommendation's basis. US_SPLITS = ("richmond", "bend", "clovis", "morgantown", "annapolis", "paterson", - "gainesville", "laurens") -CITY_SPLITS = US_SPLITS + ("budapest_district5", "sao_paulo") + "gainesville", "laurens_mapillary") +CITY_SPLITS = US_SPLITS + ("laurens_gsv", "budapest_district5", "sao_paulo") ALL_SPLITS = CITY_SPLITS + ("manual_gold",) # Why a split is swept but not pooled. Printed with the results so an omission can # never be mistaken for a withheld result. +# Which city a split belongs to. Only needed where one city has more than one +# imagery arm -- everywhere else the split id IS the city. Consumed by +# test_a_city_contributes_at_most_one_pooled_split, which is what actually stops a +# second arm from being pooled alongside its sibling. +CITY_OF = {"laurens_mapillary": "laurens", "laurens_gsv": "laurens"} + HELD_OUT = { + "laurens_gsv": "second imagery arm of laurens, which is already pooled through " + "laurens_mapillary -- the two arms sample one town and largely " + "the same physical ramps (59% of gsv panos within 20 m of a " + "mapillary one, median NN 17.2 m), so pooling both would " + "double-count them and break the independence the Wilson " + "intervals assume (GT is HIGH confidence; held out for " + "non-independence, not GT quality)", "budapest_district5": "single-rater GT at low reviewer confidence " "(docs/model_comparison.md: do not pool)", "sao_paulo": "non-US city — the pooled recommendation is a US-deployment " @@ -409,17 +422,23 @@ def _write_csv(path, rows, fields): def pool_of(cities, include_budapest=False, include_gold=False, include_sao_paulo=False): - """Which of ``cities`` contribute to the POOLED and per-tier rows.""" - keep = [] - for c in cities: - if c == "budapest_district5" and not include_budapest: - continue - if c == "manual_gold" and not include_gold: - continue - if c == "sao_paulo" and not include_sao_paulo: - continue - keep.append(c) - return keep + """Which of ``cities`` contribute to the POOLED and per-tier rows. + + Derived from ``HELD_OUT`` rather than from a list of split names. It used to + name the three held-out splits literally, which meant a *newly* held-out split + was pooled anyway -- the exclusion lived here while the stated reason lived in + HELD_OUT, and nothing tied them together. `laurens_gsv` found that: registering + it with a documented reason silently pooled it alongside its own sibling arm. + + The three keyword flags stay because each has a caller that deliberately opts + that split back in for a specific figure. There is deliberately no flag for a + second imagery arm: pooling one would double-count a town, which is not a thing + any caller should be able to ask for. + """ + opt_in = {"budapest_district5": include_budapest, + "manual_gold": include_gold, + "sao_paulo": include_sao_paulo} + return [c for c in cities if c not in HELD_OUT or opt_in.get(c, False)] # --------------------------------------------------------------------------- # diff --git a/scripts/analysis/miss_decomposition.py b/scripts/analysis/miss_decomposition.py index 953c2790..a8af08e0 100644 --- a/scripts/analysis/miss_decomposition.py +++ b/scripts/analysis/miss_decomposition.py @@ -54,7 +54,7 @@ # imagery_manifest, miss_taxonomy, fp_taxonomy, the galleries), which looks exactly # like a result nobody ran. test_registries_agree_with_low_floor_sweep enforces it. US_SPLITS = ("richmond", "bend", "clovis", "morgantown", "annapolis", "paterson", - "gainesville", "laurens") + "gainesville", "laurens_mapillary") # Imagery tier per split (benchmark/README.md). Reported separately because the # distance estimate is NOT equally trustworthy across them: flat-ground geometry @@ -65,8 +65,11 @@ # POOLED rows, and a split that never pools has no business appearing in one. TIER = {"bend": "gsv", "paterson": "gsv", "gainesville": "gsv", "richmond": "mapillary", "clovis": "mapillary", "morgantown": "mapillary", - "annapolis": "mapillary", "laurens": "mapillary"} -HELD_OUT = {"budapest_district5": "single-rater GT at low reviewer confidence", + "annapolis": "mapillary", "laurens_mapillary": "mapillary"} +HELD_OUT = {"laurens_gsv": "second imagery arm of laurens, already pooled through " + "laurens_mapillary -- same town, largely the same " + "physical ramps, so pooling both double-counts", + "budapest_district5": "single-rater GT at low reviewer confidence", "sao_paulo": "non-US city, and the pooled basis is US deployment " "(GT is high confidence; held out for geography, " "not GT quality)", diff --git a/scripts/analysis/plot_operating_point.py b/scripts/analysis/plot_operating_point.py index 08cf35c9..eb282494 100644 --- a/scripts/analysis/plot_operating_point.py +++ b/scripts/analysis/plot_operating_point.py @@ -66,7 +66,8 @@ "annapolis": "#e87ba4", "gainesville": "#008300", "paterson": "#e34948", - "laurens": "#4a3aa7", # slot 7, vacated by the manual_gold fold + "laurens_mapillary": "#4a3aa7", # slot 7, vacated by the manual_gold fold + "laurens_gsv": "#52514e", # neutral ink — held out, not a categorical slot "budapest_district5": "#52514e", # neutral ink — held out, not a categorical slot "sao_paulo": "#52514e", # neutral ink — held out, not a categorical slot "manual_gold": "#52514e", # neutral ink — held out, not a categorical slot @@ -75,9 +76,9 @@ INK, INK_MUTED, GRID = "#0b0b0b", "#52514e", "#d9d8d4" # The neutral-ink held-out splits share a hue, so each carries its own dash. HELD_DASH = {"budapest_district5": (0, (5, 2)), "manual_gold": (0, (1, 1.6)), - "sao_paulo": (0, (4, 1.4, 1, 1.4))} + "sao_paulo": (0, (4, 1.4, 1, 1.4)), "laurens_gsv": (0, (2, 1.2))} LABEL = {"budapest_district5": "budapest*", "manual_gold": "manual_gold†", - "sao_paulo": "sao_paulo‡"} + "sao_paulo": "sao_paulo‡", "laurens_gsv": "laurens_gsv§"} def collect(cities=ALL_SPLITS, cache_dir=CACHE_DIR): diff --git a/scripts/analysis/scoreboard.py b/scripts/analysis/scoreboard.py index cffb8d13..a8b2350e 100644 --- a/scripts/analysis/scoreboard.py +++ b/scripts/analysis/scoreboard.py @@ -347,6 +347,14 @@ def score(leg, split, records, gts, radius_sq, op): # just the substituted ones, so the two documents can be diffed mechanically # (test_every_number_matches_model_comparison) instead of by eye. "ap_bundle": rep.ap, + # The lowest confidence the bundle actually carries. Three states have to be + # told apart downstream and "was it substituted?" only distinguishes two: + # substituted (truncated, cache swapped in), not substituted because the + # bundle is already at 0.05 (manual_gold), and not substituted because no + # low-floor cache exists for that split at all (laurens_gsv). The last one is + # still a truncated AP, and reporting it as "already at 0.05" is a false + # provenance claim -- which is what the generated table did until this existed. + "bundle_floor": bundle_floor(preds), "tp": rep.tp, "fp": rep.fp, "fn": rep.fn, "n_panos": rep.n_panos, "n_gt_recall": rep.n_gt_recall, "fp_per_pano": rep.fp / rep.n_panos if rep.n_panos else None, diff --git a/scripts/analysis/scoreboard_render.py b/scripts/analysis/scoreboard_render.py index 65a77ced..fe858b29 100644 --- a/scripts/analysis/scoreboard_render.py +++ b/scripts/analysis/scoreboard_render.py @@ -25,7 +25,8 @@ "annapolis": "annap", "paterson": "pater", "gainesville": "gaines", - "laurens": "laurens", + "laurens_mapillary": "laur_mly", + "laurens_gsv": "laur_gsv †", "budapest_district5": "budapest †", "sao_paulo": "sao_paulo †", "manual_gold": "manual_gold †", @@ -209,13 +210,25 @@ def ap_provenance_table(result): if not cell: continue substituted = cell["ap_source"] != "bundle" + floor = cell.get("bundle_floor") + low_floor_bundle = floor is not None and floor < 0.4 + if substituted: + read_from, why = ("`op_cache` (0.05 floor)", + "truncated at the deployed 0.55") + elif low_floor_bundle: + read_from, why = ("bundle — already at 0.05", + "no truncation to undo; flip-TTA export") + else: + # Truncated like the substituted rows, but with no low-floor cache to + # swap in -- a held-out split never went through the #54 re-extraction. + read_from, why = ("bundle — 0.55 floor, no `op_cache`", + "**truncated**; not comparable with the rows above") rows.append([ f"`{split}`", num(cell["ap_bundle"]), bold(num(cell["ap"]), substituted), - "`op_cache` (0.05 floor)" if substituted else "bundle — already at 0.05", - "truncated at the deployed 0.55" if substituted - else "no truncation to undo; flip-TTA export", + read_from, + why, ]) return _table(["split", "AP in `model_comparison.md`", "AP here", "read from", "why"], rows, ["---", "--:", "--:", "---", "---"]) diff --git a/scripts/analysis/silent_activation.py b/scripts/analysis/silent_activation.py index 7b0fdbbc..0de6007a 100644 --- a/scripts/analysis/silent_activation.py +++ b/scripts/analysis/silent_activation.py @@ -75,7 +75,8 @@ # pins the committed analysis_out/silent_activation.json to the numbers the issue's # section 0c quotes. It therefore carries its own split tuple rather than importing the # live `US_SPLITS`, so registering a new city cannot silently restate a published -# finding. laurens is absent for that reason alone -- it is pooled everywhere else. +# finding. laurens_mapillary is absent for that reason alone -- it is pooled +# everywhere else. # To fold a split in: re-run the study, re-quote it, update the 0c expectations. US_SPLITS = ("richmond", "bend", "clovis", "morgantown", "annapolis", "paterson", "gainesville") diff --git a/scripts/export_benchmark.py b/scripts/export_benchmark.py index 1e39ef55..6b5f00bb 100644 --- a/scripts/export_benchmark.py +++ b/scripts/export_benchmark.py @@ -77,7 +77,8 @@ # tests/test_export_benchmark.py::test_split_allowlist_matches_the_analysis_registry keeps it in # step with scripts/analysis/miss_decomposition.py. BENCHMARK_SPLITS = ("annapolis", "bend", "budapest_district5", "clovis", "gainesville", - "laurens", "morgantown", "paterson", "richmond", "sao_paulo") + "laurens_gsv", "laurens_mapillary", "morgantown", "paterson", + "richmond", "sao_paulo") EXCLUDED_SPLITS = {"manual_gold": "the paper's gold set -- published in rampnet-dataset already"} # A `panos/` directory can pick up .DS_Store, Thumbs.db, a leftover .json sidecar or a diff --git a/tests/test_miss_decomposition.py b/tests/test_miss_decomposition.py index 266674ba..6afdc624 100644 --- a/tests/test_miss_decomposition.py +++ b/tests/test_miss_decomposition.py @@ -124,6 +124,45 @@ def test_held_out_splits_are_not_in_the_pooled_basis(): assert set(md.ALL_SPLITS) == set(md.US_SPLITS) | set(md.HELD_OUT) +def test_a_city_contributes_at_most_one_pooled_split(): + """One city, one pooled row — the rule docs/adding_a_benchmark_city.md states. + + Laurens is the only city run on two imagery rigs, and the two arms sample one + 1.91 km2 town: 59% of `laurens_gsv`'s panos sit within 20 m of a + `laurens_mapillary` one (median nearest neighbour 17.2 m), so they largely see + the same physical curb ramps. Pooling both would count those ramps twice and + break the independence the Wilson intervals assume — and it would do it + silently, by moving every pooled number a little. + + So the second arm is held out, and this is what enforces it rather than + anyone remembering. Splits with no `CITY_OF` entry are their own city, which + keeps the registry to the one line per genuine ambiguity. + """ + import low_floor_sweep as lfs + seen = {} + for split in lfs.US_SPLITS: + city = lfs.CITY_OF.get(split, split) + assert city not in seen, ( + f"{split} and {seen[city]} are both pooled and are both the city " + f"{city!r}. A second imagery arm must go in HELD_OUT, not US_SPLITS.") + seen[city] = split + + +def test_every_multi_arm_city_is_declared_in_city_of(): + """A second arm that nobody declared reads as an unrelated city, and the guard + above then passes vacuously — which is the failure mode that matters, because + it looks identical to having no second arm at all.""" + import low_floor_sweep as lfs + for split in lfs.ALL_SPLITS: + base = split.rsplit("_", 1)[0] + siblings = [s for s in lfs.ALL_SPLITS if s != split and s.startswith(base + "_")] + if siblings: + assert split in lfs.CITY_OF, ( + f"{split} shares the prefix {base!r} with {siblings} but has no " + f"CITY_OF entry, so the one-pooled-split-per-city guard cannot see " + f"the pairing.") + + def test_registries_agree_with_low_floor_sweep(): """The two split registries must cover the same splits. diff --git a/tests/test_scoreboard.py b/tests/test_scoreboard.py index c2e702cb..6d5693b3 100644 --- a/tests/test_scoreboard.py +++ b/tests/test_scoreboard.py @@ -147,17 +147,20 @@ def test_ap_is_read_full_range_not_truncated_at_the_operating_point(board): def test_rampnet_city_ap_comes_from_the_low_floor_cache(board): """The city bundles stop at 0.55, so an AP computed from them is a truncated curve. - Read that way richmond is 0.763 and the pooled figure is 0.720 — which puts RampNet + Read that way richmond is 0.763 and the pooled figure is 0.677 — which puts RampNet BELOW the YOLO arms on AP, an artifact of the floor rather than a result. The #54 re-extraction carries the same run down to 0.05, which is where every other scored model is exported. + + The pooled figure was 0.720 over seven splits; laurens_mapillary is the eighth, and + it is the split RampNet does worst on, so the truncated pooled AP falls further. """ cell = _cell(board, "rampnet", "richmond") assert cell["ap_source"] == "op_cache (0.05 floor)" assert cell["ap"] == pytest.approx(0.876, abs=0.002) assert cell["ap_bundle"] == pytest.approx(0.763, abs=0.0006) # ...and the pooled truncated figure is the one that inverts the ordering. - assert _summary(board, "rampnet")["ap_bundle"] == pytest.approx(0.720, abs=0.0006) + assert _summary(board, "rampnet")["ap_bundle"] == pytest.approx(0.677, abs=0.0006) def test_the_substitution_is_scoped_to_actual_truncation(board): @@ -181,15 +184,20 @@ def test_ap_ordering_is_not_an_artifact_of_the_floor(board): def test_threshold_marks_reproduce_the_published_operating_point_table(board): """The PR figure's marked points must agree with docs/operating_point.md. - That document's pooled row is P 0.964 / R 0.722 / F1 0.826 at the deployed 0.55 and - 0.900 raw precision at 0.30. Computed here from the same committed cache by a + That document's pooled row is P 0.9594 / R 0.6864 / F1 0.8003 at the deployed 0.55 + and 0.8991 raw precision at 0.30. Computed here from the same committed cache by a different code path, so a drift in either is a real disagreement. + + These were 0.964 / 0.722 / 0.826 and 0.900 over seven splits. Registering + laurens_mapillary as the eighth moved them: precision is untouched (0.964 -> 0.959), + the recall drop is laurens' own 0.390 entering the pool. Both documents are on the + eight-split basis; if one is ever re-pooled without the other, this fails. """ marks = board["curves"]["rampnet"]["marks"] - assert marks["0.55"]["precision"] == pytest.approx(0.964, abs=0.0006) - assert marks["0.55"]["recall"] == pytest.approx(0.722, abs=0.0006) - assert marks["0.55"]["f1"] == pytest.approx(0.826, abs=0.0006) - assert marks["0.30"]["precision"] == pytest.approx(0.900, abs=0.0006) + assert marks["0.55"]["precision"] == pytest.approx(0.9594, abs=0.0006) + assert marks["0.55"]["recall"] == pytest.approx(0.6864, abs=0.0006) + assert marks["0.55"]["f1"] == pytest.approx(0.8003, abs=0.0006) + assert marks["0.30"]["precision"] == pytest.approx(0.8991, abs=0.0006) def test_only_score_carrying_models_get_a_curve(board): @@ -352,6 +360,32 @@ def test_the_ap_provenance_table_shows_the_logs_number(board): # --------------------------------------------------------------------------- # # coverage — a run that happened must appear; one that didn't must not be invented # --------------------------------------------------------------------------- # +def test_the_provenance_table_does_not_claim_an_untruncated_bundle_it_has_not_got(board): + """Three AP provenances, not two — and the third one used to be mislabelled. + + The table had a binary: substituted means "op_cache, was truncated", anything else + means "bundle, already at 0.05, flip-TTA export". That held while manual_gold was + the only unsubstituted row. `laurens_gsv` is the third case — a held-out split that + never went through the #54 re-extraction, so there is no cache to swap in and its + bundle is truncated at the deployed 0.55 like every other city's. The old table + printed "already at 0.05" against it, which is a false provenance claim on the one + page whose job is provenance, and no test would have caught it. + """ + table = sr.ap_provenance_table(board) + + gold = next(l for l in table.splitlines() if l.startswith("| `manual_gold` |")) + assert "already at 0.05" in gold + assert _cell(board, "rampnet", "manual_gold")["bundle_floor"] < 0.4 + + gsv = next(l for l in table.splitlines() if l.startswith("| `laurens_gsv` |")) + assert "already at 0.05" not in gsv, "laurens_gsv has no op_cache and a 0.55 bundle; it is truncated, not low-floor" + assert "truncated" in gsv and "no `op_cache`" in gsv + cell = _cell(board, "rampnet", "laurens_gsv") + assert cell["ap_source"] == "bundle" + assert cell["bundle_floor"] >= 0.4, "a city bundle is exported at the deployed 0.55, so its floor cannot be low" + assert cell["ap"] == cell["ap_bundle"] + + def test_scores_every_registered_leg(board): """The roster is the source of truth for who has been run, so every entry is scored. @@ -394,7 +428,7 @@ def test_partial_coverage_is_reported_not_averaged_away(board): """ for model in ("gemini-3.1-pro-preview", "gemini-3.6-flash"): summary = _summary(board, model) - assert summary["coverage"] == "7/7" + assert summary["coverage"] == "8/8" assert summary["complete"] is True assert summary["manual_gold_f1"] is None assert "manual_gold" not in board["per_split"][model] @@ -414,7 +448,7 @@ def test_single_split_legs_stay_out_of_the_pooled_tables(board): "claude-sonnet-5-effort-low", "claude-sonnet-5-effort-high", } for m in single: - assert m["coverage"] == "1/7" + assert m["coverage"] == "1/8" headline = sr.headline_table(board) matrix = sr.by_split_table(board) @@ -563,7 +597,7 @@ def test_the_json_does_not_carry_the_curve_point_arrays(board): assert "recalls" not in curve and "precisions" not in curve, \ f"{name}: point arrays leaked back into the committed JSON" assert curve["ap"] is not None and curve["n_points"] > 0 - assert payload["curves"]["rampnet"]["marks"]["0.55"]["f1"] == pytest.approx(0.826, + assert payload["curves"]["rampnet"]["marks"]["0.55"]["f1"] == pytest.approx(0.8003, abs=0.0006) # In-memory, the figures still get the full curves. assert len(board["curves"]["rampnet"]["recalls"]) == \