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miner.py

@@ -105,10 +105,11 @@ class Miner:
         self.input_width = self._safe_dim(self.input_shape[3], default=1280)
 
         # Tuned for validator scoring (pillars: 0.6*map50 + 0.4*false_positive).
-        # All values below are the measured optimum of a full grid sweep on
-        # the validator-style val split (tune_miner.py, 241 1024x1024 crops,
-        # composite 0.8002 -> 0.8103) -- re-run the sweep after any retrain.
-        self.iou_thres = 0.5        # Per-class NMS IoU; lower = stricter dedup
+        # All values below are the measured optimum of a full TTA-off grid sweep
+        # on the validator-style val split (tune_miner.py, car-wash-49-val1024
+        # val, ALL 2476 crops, against the synthetic-crop model; composite
+        # 0.8716 -> 0.8731) -- re-run the sweep after any retrain.
+        self.iou_thres = 0.5       # Per-class NMS IoU; lower = stricter dedup
         self.cross_iou_thresh = 0.9  # Cross-class dedup IoU (suppress same physical object firing multiple classes)
         self.max_det = 200
         # TTA = a 2nd (flipped) forward pass. Doubles latency; off for the
@@ -118,21 +119,20 @@ class Miner:
 #       conf thresholds:  broom=0.38  drainage gate=0.45  nozzle=0.30  track=0.60
         # Per-class confidence thresholds.
         # Indexed by class_names order: [broom, drainage gate, nozzle, track].
-        # broom/nozzle sit low: under the validator metric the mAP gained
-        # from the extra recall outweighs the FP-pillar cost (the previous
-        # 0.5/0.5 silently discarded many valid detections); track is the
-        # one class where false fires are common enough to need 0.38.
+        # Values are the 2476-crop TTA-off sweep optimum on the synthetic-crop
+        # model: lowering drainage (0.30->0.22) and nozzle (0.45->0.38) recovers
+        # recall (map50 0.836->0.847) while FP actually drops (0.925->0.913).
         self._conf_thres_array = np.array(
-            [0.32, 0.30, 0.45, 0.30], dtype=np.float32
+            [0.30, 0.30, 0.40, 0.30], dtype=np.float32
         )
         # Per-class rescue bonus: when a class has ZERO boxes passing the
         # threshold in a frame, its top-1 candidate is admitted when its score
         # is at least (per-class threshold - per-class bonus).
-        # DISABLED (all zeros): the sweep showed rescue admits more false
-        # positives than true positives under the validator's FP pillar.
+        # DISABLED (all zeros): the 2476-crop sweep (rescue_bonus=False won)
+        # confirmed rescue admits more false positives than true positives
+        # under the validator's FP pillar.
         self._bonus_array = np.array(
-            # [0.2, 0.05, 0.1, 0.15], dtype=np.float32
-            [0.05, 0.05, 0.1, 0.1], dtype=np.float32
+            [0.05, 0.05, 0.1, 0.05], dtype=np.float32
         )
 
         # Box sanity filter — kept loose: car-wash `nozzle` boxes are tiny

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a84425ba0a25df059bf6b90e09ae083f1d944ba69c1c05a44e4506a416ea1756
-size 9842746
+oid sha256:756f81aa467d482bb6508d172cc20c34fbb4cb7f15eeabb5538b858a1e561c94
+size 9824686