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class_names.txt

@@ -0,0 +1,4 @@
+broom
+drainage gate
+nozzle
+track

miner.py

@@ -24,249 +24,141 @@ class TVFrameResult(BaseModel):
 
 
 class Miner:
-    def __init__(self,
-        path_hf_repo: Path
-    ) -> None:
-        model_path = self._resolve_model_path(path_hf_repo)
-        # car-wash element classes — cls_id order MUST match element `objects`
-        # (0=broom, 1=drainage gate, 2=nozzle, 3=track). This is the canonical
-        # order every downstream consumer (validator, BoundingBox.cls_id) sees.
-        self.class_names = ["broom", "drainage gate", "nozzle", "track"]
-        # FALLBACK model-emit order: the authoritative order is read from the
-        # ONNX `names` metadata after the session is created (embedded by
-        # Ultralytics at export, ships inside weights.onnx), so a retrained
-        # model with a different class order is remapped correctly without
-        # code changes. This list is used only when metadata is missing.
-        self._model_class_order = ["broom", "drainage gate", "nozzle", "track"]
+    """
+    YOLOv26 ONNX miner for car wash detection.
+    
+    Classes: broom, drainage gate, nozzle, track
+    
+    v26 is NMS-free — output shape: [1, 300, 6] (x1, y1, x2, y2, conf, cls_id).
+    
+    Features:
+      - Vectorized NMS + sanity filter + dedup + flip TTA
+      - Per-class rescue bonus (saves hard-to-detect classes at slightly lower conf)
+      - Confidence boost from same-class cluster (TTA consensus)
+      - Aggressive same-class overlap suppression
+      - Per-class IoU thresholds
+    """
+
+    class_names = ['broom', 'drainage gate', 'nozzle', 'track']
+    input_size = 1408
+    cross_iou_thresh = 0.8
+    max_det = 300
+    #overlap_suppress_threshold = 0.85
+    
+    # Per-class confidence thresholds
+    _conf_thres_array = np.array([0.35, 0.7, 0.4, 0.7], dtype=np.float32)
+    _extra_conf_thres_array = np.array([0.35, 0.35, 0.55, 0.35], dtype=np.float32)
+    
+    # Per-class IoU thresholds for same-class NMS
+    _iou_thres_array = np.array([0.6, 0.65, 0.5, 0.65], dtype=np.float32)
+    
+    # Per-class rescue bonus
+    _bonus_array = np.array([0.2, 0.2, 0.0, 0.2], dtype=np.float32)
+    
+    # Per-class minimum box area
+    # Indices: 0=broom, 1=drainage gate, 2=nozzle, 3=track
+    _min_box_area_array = np.array([144.0, 144.0, 4.0, 144.0], dtype=np.float32)
+    
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        self.path_hf_repo = path_hf_repo
+        
         print("ORT version:", ort.__version__)
-
+        
         try:
             ort.preload_dlls()
-            print("✅ onnxruntime.preload_dlls() success")
+            print("preload_dlls success")
         except Exception as e:
-            print(f"⚠️ preload_dlls failed: {e}")
-
+            print(f"preload_dlls failed: {e}")
+        
         print("ORT available providers BEFORE session:", ort.get_available_providers())
-
+        
         sess_options = ort.SessionOptions()
         sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-
-        try:
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-            )
-            print("✅ Created ORT session with preferred CUDA provider list")
-        except Exception as e:
-            print(f"⚠️ CUDA session creation failed, falling back to CPU: {e}")
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CPUExecutionProvider"],
-            )
-
-        print("ORT session providers:", self.session.get_providers())
-
-        # Build cls_remap: for each model-emit index i,
-        #   cls_remap[i] = self.class_names.index(model_class_order[i])
-        # The model-side order comes from the ONNX metadata when available,
-        # else falls back to the static _model_class_order.
-        model_class_order = self._read_model_class_order()
-        if model_class_order is None:
-            model_class_order = list(self._model_class_order)
-            print(f"cls order: no usable ONNX metadata, FALLBACK {model_class_order}")
-        else:
-            print(f"cls order: from ONNX metadata {model_class_order}")
-        self.cls_remap = np.array(
-            [self.class_names.index(n) for n in model_class_order], dtype=np.int32
+        
+        self.session = ort.InferenceSession(
+            str(path_hf_repo / "weights.onnx"),
+            sess_options=sess_options,
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
         )
-
-        for inp in self.session.get_inputs():
-            print("INPUT:", inp.name, inp.shape, inp.type)
-
-        for out in self.session.get_outputs():
-            print("OUTPUT:", out.name, out.shape, out.type)
-
+        print("Created ORT session with preferred CUDA provider list")
+        print("ORT session providers:", self.session.get_providers())
+        
         self.input_name = self.session.get_inputs()[0].name
         self.output_names = [output.name for output in self.session.get_outputs()]
-        self.input_shape = self.session.get_inputs()[0].shape
-
-        # Match the ONNX input dtype (this export is FP16 -> needs float16 input).
-        input_type = self.session.get_inputs()[0].type
-        self.np_dtype = np.float16 if "float16" in input_type else np.float32
-        print(f"✅ ONNX input dtype: {input_type} -> numpy {self.np_dtype}")
-
-        # ONNX is fixed-size 1408x1408 (v1 export); read actual shape to be safe.
-        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
-        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 = 0.45        # 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
-        self.use_tta = True
-
-#       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.
-        self._conf_thres_array = np.array(
-            [0.37, 0.23, 0.37, 0.45], 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.
-        self._bonus_array = np.array(
-            [0.05, 0.05, 0.0, 0.15], dtype=np.float32
-        )
-
-        # Box sanity filter — kept loose: car-wash `nozzle` boxes are tiny
-        # (GT median ~290 px², smallest ~32 px²). Fire's 14x14/min_side 8
-        # would delete valid nozzles, so thresholds are dropped here.
-        self.min_box_area = 4 * 4  # 16 px²
-        self.min_side = 3
-        self.max_aspect_ratio = 12.0
-
-        print(f"✅ ONNX model loaded from: {model_path}")
-        print(f"✅ ONNX providers: {self.session.get_providers()}")
-        print(f"✅ ONNX input: name={self.input_name}, shape={self.input_shape}")
+        input_shape = self.session.get_inputs()[0].shape
+        
+        self.input_h = self._safe_dim(input_shape[2], default=self.input_size)
+        self.input_w = self._safe_dim(input_shape[3], default=self.input_size)
 
     def __repr__(self) -> str:
-        return (
-            f"ONNXRuntime(session={type(self.session).__name__}, "
-            f"providers={self.session.get_providers()})"
-        )
+        return f"YOLOv26 Car Wash Miner classes={len(self.class_names)}"
 
     @staticmethod
     def _safe_dim(value, default: int) -> int:
         return value if isinstance(value, int) and value > 0 else default
 
-    @staticmethod
-    def _resolve_model_path(repo: Path) -> Path:
-        """Locate the ONNX model in the repo dir.
-
-        Prefers weights.onnx (FP16/FP32 export), then weights_int8.onnx (the
-        training script's INT8-quantized export -- works as-is: quantization
-        preserves the Ultralytics metadata and QDQ models take regular fp32
-        input), then any other .onnx file. INT8 is the fallback when the FP16
-        export exceeds the 30 MB deployment limit (e.g. yolo26m).
-        """
-        for name in ("weights.onnx", "weights_int8.onnx"):
-            p = repo / name
-            if p.exists():
-                if name != "weights.onnx":
-                    print(f"model: weights.onnx not found, using {name}")
-                return p
-        candidates = sorted(repo.glob("*.onnx"))
-        if candidates:
-            print(f"model: using {candidates[0].name}")
-            return candidates[0]
-        return repo / "weights.onnx"  # let session creation raise the error
-
-    def _read_model_class_order(self) -> list[str] | None:
-        """Read the model's class order from Ultralytics ONNX metadata.
-
-        Returns the class names ordered by model-emit index, or None when
-        metadata is missing/unparsable or doesn't match `class_names` as a
-        set (in which case the static _model_class_order fallback is used).
-        """
-        try:
-            import ast
-
-            meta = self.session.get_modelmeta().custom_metadata_map
-            names = ast.literal_eval(meta["names"])  # e.g. {0: 'broom', ...}
-            if isinstance(names, dict):
-                order = [str(names[i]) for i in sorted(names)]
-            else:
-                order = [str(n) for n in names]
-        except Exception as e:
-            print(f"cls order: could not read ONNX names metadata ({e})")
-            return None
-        if sorted(order) != sorted(self.class_names):
-            print(
-                f"cls order: ONNX names {order} do not match expected classes "
-                f"{self.class_names}; ignoring metadata"
-            )
-            return None
-        return order
-
+    # ─── Preprocessing ────────────────────────────────────────────
+    
     def _letterbox(
-        self,
-        image: ndarray,
-        new_shape: tuple[int, int],
-        color=(114, 114, 114),
-    ) -> tuple[ndarray, float, tuple[float, float]]:
-        """
-        Resize with unchanged aspect ratio and pad to target shape.
-        Returns:
-            padded_image,
-            ratio,
-            (pad_w, pad_h)  # half-padding
-        """
-        h, w = image.shape[:2]
-        new_w, new_h = new_shape
-
-        ratio = min(new_w / w, new_h / h)
-        resized_w = int(round(w * ratio))
-        resized_h = int(round(h * ratio))
-
-        if (resized_w, resized_h) != (w, h):
-            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
-
-        dw = new_w - resized_w
-        dh = new_h - resized_h
-        dw /= 2.0
-        dh /= 2.0
-
-        left = int(round(dw - 0.1))
-        right = int(round(dw + 0.1))
-        top = int(round(dh - 0.1))
-        bottom = int(round(dh + 0.1))
-
-        padded = cv2.copyMakeBorder(
-            image,
-            top,
-            bottom,
-            left,
-            right,
-            borderType=cv2.BORDER_CONSTANT,
-            value=color,
-        )
-        return padded, ratio, (dw, dh)
-
-    def _preprocess(
-        self, image: ndarray
-    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
-        """
-        Preprocess for fixed-size ONNX export:
-        - enhance image quality (CLAHE, denoise, sharpen)
-        - letterbox to model input size
-        - BGR -> RGB
-        - normalize to [0,1]
-        - HWC -> NCHW float32
-        """
+        self, image: ndarray, new_shape: tuple[int, int],
+        color: tuple[int, int, int] = (114, 114, 114),
+    ) -> tuple[ndarray, float, float, float]:
         orig_h, orig_w = image.shape[:2]
-
-        img, ratio, pad = self._letterbox(
-            image, (self.input_width, self.input_height)
+        target_w, target_h = new_shape
+        
+        r = min(target_w / orig_w, target_h / orig_h)
+        new_unpad_w = int(round(orig_w * r))
+        new_unpad_h = int(round(orig_h * r))
+        
+        resized = cv2.resize(image, (new_unpad_w, new_unpad_h), interpolation=cv2.INTER_LINEAR)
+        
+        dw = target_w - new_unpad_w
+        dh = target_h - new_unpad_h
+        pad_w = dw / 2.0
+        pad_h = dh / 2.0
+        
+        left = int(round(pad_w - 0.1))
+        right = int(round(pad_w + 0.1))
+        top = int(round(pad_h - 0.1))
+        bottom = int(round(pad_h + 0.1))
+        
+        out = cv2.copyMakeBorder(
+            resized, top, bottom, left, right,
+            cv2.BORDER_CONSTANT, value=color,
         )
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = (img.astype(np.float32) / 255.0)
-        img = np.transpose(img, (2, 0, 1))[None, ...]
-        img = np.ascontiguousarray(img, dtype=self.np_dtype)
-
-        return img, ratio, pad, (orig_w, orig_h)
-
+        return out, r, pad_w, pad_h
+
+    def _preprocess(self, image_bgr: np.ndarray,
+                   allow_pad: bool = True) -> tuple[np.ndarray, dict]:
+        orig_h, orig_w = image_bgr.shape[:2]
+        extra_left = 0
+        extra_right = 0
+        if allow_pad and orig_w == orig_h:          # only pad when allowed
+            target_w = int(orig_w * 1.05)
+            if target_w > orig_w:
+                total_extra = target_w - orig_w
+                extra_left = total_extra // 2
+                extra_right = total_extra - extra_left
+                image_bgr = cv2.copyMakeBorder(
+                    image_bgr, 0, 0, extra_left, extra_right,
+                    cv2.BORDER_CONSTANT, value=(114, 114, 114),
+                )
+        padded_h, padded_w = image_bgr.shape[:2]
+        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+        img, ratio, pad_w, pad_h = self._letterbox(rgb, (self.input_w, self.input_h))
+        x = img.astype(np.float32) / 255.0
+        x = np.transpose(x, (2, 0, 1))[None, ...]
+        x = np.ascontiguousarray(x)
+        return x, {
+            "orig_h": orig_h, "orig_w": orig_w,
+            "ratio": ratio, "pad_w": pad_w, "pad_h": pad_h,
+            "extra_left": extra_left, "extra_right": extra_right,
+            "padded_w": padded_w, "padded_h": padded_h,
+        }
+
+    # ─── Vectorized box operations ───────────────────────────────
+    
     @staticmethod
     def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
         w, h = image_size
@@ -277,200 +169,116 @@ class Miner:
         return boxes
 
     @staticmethod
-    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
-        out = np.empty_like(boxes)
-        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-        return out
-
-    def _soft_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        sigma: float = 0.5,
-        score_thresh: float = 0.01,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """
-        Soft-NMS: Gaussian decay of overlapping scores instead of hard removal.
-        Returns (kept_original_indices, updated_scores).
-        """
-        N = len(boxes)
-        if N == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-
-        boxes = boxes.astype(np.float32, copy=True)
-        scores = scores.astype(np.float32, copy=True)
-        order = np.arange(N)
-
-        for i in range(N):
-            max_pos = i + int(np.argmax(scores[i:]))
-            boxes[[i, max_pos]] = boxes[[max_pos, i]]
-            scores[[i, max_pos]] = scores[[max_pos, i]]
-            order[[i, max_pos]] = order[[max_pos, i]]
-
-            if i + 1 >= N:
+    def _hard_nms(boxes: np.ndarray, scores: np.ndarray,
+                  iou_thresh: float) -> np.ndarray:
+        """Vectorized NMS. Returns indices to keep."""
+        n = len(boxes)
+        if n == 0:
+            return np.array([], dtype=np.intp)
+        order = np.argsort(-scores)
+        keep = []
+        while len(order) > 0:
+            i = int(order[0])
+            keep.append(i)
+            if len(order) == 1:
                 break
-
-            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
-            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
-            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
-            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
+            rest = order[1:]
+            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
             inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-
-            area_i = max(0.0, float(
-                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
-            ))
-            areas_j = (
-                np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
-                * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1])
-            )
-            iou = inter / (area_i + areas_j - inter + 1e-7)
-            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
-
-        mask = scores > score_thresh
-        return order[mask], scores[mask]
-
-    @staticmethod
-    def _hard_nms(
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """
-        Standard NMS: keep one box per overlapping cluster (the one with highest score).
-        Returns indices of kept boxes (into the boxes/scores arrays).
-        """
-        N = len(boxes)
-        if N == 0:
-            return np.array([], dtype=np.intp)
-        boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
-        order = np.argsort(scores)[::-1]
-        keep: list[int] = []
-        suppressed = np.zeros(N, dtype=bool)
-        for i in range(N):
-            idx = order[i]
-            if suppressed[idx]:
-                continue
-            keep.append(idx)
-            bi = boxes[idx]
-            for k in range(i + 1, N):
-                jdx = order[k]
-                if suppressed[jdx]:
-                    continue
-                bj = boxes[jdx]
-                xx1 = max(bi[0], bj[0])
-                yy1 = max(bi[1], bj[1])
-                xx2 = min(bi[2], bj[2])
-                yy2 = min(bi[3], bj[3])
-                inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1)
-                area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
-                area_j = (bj[2] - bj[0]) * (bj[3] - bj[1])
-                iou = inter / (area_i + area_j - inter + 1e-7)
-                if iou > iou_thresh:
-                    suppressed[jdx] = True
-        return np.array(keep)
-
-    def _per_class_hard_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """Hard NMS applied independently per class."""
+            a_i = (max(0.0, boxes[i, 2] - boxes[i, 0]) *
+                   max(0.0, boxes[i, 3] - boxes[i, 1]))
+            a_r = (np.maximum(0.0, boxes[rest, 2] - boxes[rest, 0]) *
+                   np.maximum(0.0, boxes[rest, 3] - boxes[rest, 1]))
+            iou = inter / (a_i + a_r - inter + 1e-7)
+            order = rest[iou <= iou_thresh]
+        return np.array(keep, dtype=np.intp)
+
+    def _per_class_hard_nms(self, boxes: np.ndarray, scores: np.ndarray,
+                            cls_ids: np.ndarray) -> np.ndarray:
+        """Per-class NMS using per-class IoU thresholds."""
         if len(boxes) == 0:
             return np.array([], dtype=np.intp)
-        all_keep: list[int] = []
+        all_keep = []
         for c in np.unique(cls_ids):
             mask = cls_ids == c
             indices = np.where(mask)[0]
-            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
+            cls_iou = float(self._iou_thres_array[c])  # per-class IoU threshold
+            keep = self._hard_nms(boxes[mask], scores[mask], cls_iou)
             all_keep.extend(indices[keep].tolist())
         all_keep.sort()
         return np.array(all_keep, dtype=np.intp)
 
-    def _per_class_soft_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        sigma: float = 0.5,
-        score_thresh: float = 0.01,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Soft NMS applied independently per class."""
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        all_keep: list[int] = []
-        all_scores: list[float] = []
-        for c in np.unique(cls_ids):
-            mask = cls_ids == c
-            indices = np.where(mask)[0]
-            keep, updated = self._soft_nms(boxes[mask], scores[mask], sigma, score_thresh)
-            for k, s in zip(keep, updated):
-                all_keep.append(int(indices[k]))
-                all_scores.append(float(s))
-        if not all_keep:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        return np.array(all_keep, dtype=np.intp), np.array(all_scores, dtype=np.float32)
-
-    def _filter_sane_boxes(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        orig_size: tuple[int, int],
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Filter out tiny, degenerate, or implausible boxes (common FP)."""
-        if len(boxes) == 0:
+    def _cross_class_dedup_op(self, boxes: np.ndarray, scores: np.ndarray,
+                              cls_ids: np.ndarray, iou_thresh: float
+                              ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        n = len(boxes)
+        if n <= 1:
             return boxes, scores, cls_ids
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
+        boxes = np.asarray(boxes, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        cls_ids = np.asarray(cls_ids, dtype=np.int32)
+        areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
+                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
+        margins = scores - self._conf_thres_array[cls_ids]
+        order = np.lexsort((-areas, -margins))
+        suppressed = np.zeros(n, dtype=bool)
         keep = []
-        for i, box in enumerate(boxes):
-            x1, y1, x2, y2 = box.tolist()
-            bw = x2 - x1
-            bh = y2 - y1
-            if bw <= 0 or bh <= 0:
-                continue
-            if bw < self.min_side or bh < self.min_side:
-                continue
-            area = bw * bh
-            if area < self.min_box_area:
-                continue
-            if area > 0.95 * image_area:
-                continue
-            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
-            if ar > self.max_aspect_ratio:
+        for i in order:
+            if suppressed[i]:
                 continue
-            keep.append(i)
-        if not keep:
-            return (
-                np.empty((0, 4), dtype=np.float32),
-                np.empty((0,), dtype=np.float32),
-                np.empty((0,), dtype=np.int32),
-            )
-        k = np.array(keep, dtype=np.intp)
-        return boxes[k], scores[k], cls_ids[k]
+            keep.append(int(i))
+            bi = boxes[i]
+            xx1 = np.maximum(bi[0], boxes[:, 0])
+            yy1 = np.maximum(bi[1], boxes[:, 1])
+            xx2 = np.minimum(bi[2], boxes[:, 2])
+            yy2 = np.minimum(bi[3], boxes[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            a_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
+            iou = inter / (a_i + areas - inter + 1e-7)
+            dup = iou > iou_thresh
+            dup[i] = False
+            suppressed |= dup
+        keep_idx = np.array(keep, dtype=np.intp)
+        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
 
-    @staticmethod
-    def _max_score_per_cluster(
-        post_boxes: np.ndarray,
-        post_cls: np.ndarray,
-        full_boxes: np.ndarray,
-        full_scores: np.ndarray,
-        full_cls: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """For each kept (post-NMS) box, return the max score over the FULL
-        candidate set among SAME-CLASS boxes with IoU >= iou_thresh.
-
-        The previous version omitted the same-class constraint, which let a
-        confident broom raise the score of a coincident nozzle (or vice
-        versa) under TTA. That's a silent FP booster and is fixed here.
-        """
+    def _filter_sane_boxes(self, boxes: np.ndarray, scores: np.ndarray,
+                       cls_ids: np.ndarray, orig_size: tuple[int, int]
+                       ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Filter by per-class min area, max area ratio, and aspect ratio."""
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+        
+        orig_w, orig_h = orig_size
+        image_area = float(orig_w * orig_h)
+        bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0])
+        bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
+        area = bw * bh
+        
+        ar = np.where(
+            (bw > 0) & (bh > 0),
+            np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6)),
+            np.inf,
+        )
+        
+        # Per-class minimum area
+        class_min_area = self._min_box_area_array[cls_ids]
+        
+        keep = (
+            (area >= class_min_area) &
+            (area <= 0.95 * image_area)
+        )
+        return boxes[keep], scores[keep], cls_ids[keep]
+
+    def _max_score_per_cluster(self, post_boxes: np.ndarray,
+                               post_cls: np.ndarray,
+                               full_boxes: np.ndarray,
+                               full_scores: np.ndarray,
+                               full_cls: np.ndarray,
+                               iou_thresh: float) -> np.ndarray:
+        """For each kept box, set confidence to max score in its SAME-CLASS cluster."""
         n = len(post_boxes)
         if n == 0:
             return np.empty(0, dtype=np.float32)
@@ -490,16 +298,16 @@ class Miner:
             out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
         return out
 
-    def _conf_filter_mask(
-        self, scores: np.ndarray, cls_ids: np.ndarray
-    ) -> np.ndarray:
-        """Boolean keep-mask: score >= per-class threshold, with a per-class
-        rescue -- if a class has zero boxes passing, admit its top-1 candidate
-        when its score >= (per-class threshold - per-class bonus).
-        """
+    def _conf_filter_mask(self, scores: np.ndarray,
+                          cls_ids: np.ndarray, extra_left: int) -> np.ndarray:
+        """Per-class threshold with rescue bonus for missed classes."""
         if len(scores) == 0:
             return np.zeros(0, dtype=bool)
-        thr = self._conf_thres_array[cls_ids]
+        thr = 0
+        if extra_left > 0:
+            thr = self._extra_conf_thres_array[cls_ids]
+        else:
+            thr = self._conf_thres_array[cls_ids]
         keep = scores >= thr
         for c in np.unique(cls_ids):
             b = float(self._bonus_array[c])
@@ -514,60 +322,82 @@ class Miner:
                 keep[top] = True
         return keep
 
-    def _cross_class_dedup_op(
+    def _suppress_overlapping_same_class(
         self,
         boxes: np.ndarray,
         scores: np.ndarray,
         cls_ids: np.ndarray,
-        iou_thresh: float,
+        threshold: float,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Remove near-duplicate boxes across classes.
+        """
+        Drop a same-class box that is (almost) entirely *contained* inside a larger
+        same-class box — a duplicate detection of one object.
+
+        Containment is intersection / area_of_SMALLER_box (IoMin), NOT IoU.
+        A small box nested in a large one has tiny IoU, so plain NMS never removes
+        it; IoMin catches it.
 
-        Order candidates by (score - per_class_threshold) margin, then by area;
-        keep the highest, suppress every other box with IoU > iou_thresh. For
-        car-wash this kills the common failure where water spray makes the
-        model fire both `nozzle` and `track` on the same patch, or where a
-        broom handle overlaps a drainage-gate detection.
+            black (large) + green (fully inside black)  -> same gate, drop green
+            red   (sticks out of black)                 -> separate gate, keep
+
+        Survivor = the LARGER box, and its confidence is raised to the cluster max.
         """
         n = len(boxes)
         if n <= 1:
             return boxes, scores, cls_ids
+
         boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32).copy()
         cls_ids = np.asarray(cls_ids, dtype=np.int32)
+
         areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
-                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
-        margins = scores - self._conf_thres_array[cls_ids]
-        order = np.lexsort((-areas, -margins))
-        suppressed = np.zeros(n, dtype=bool)
-        keep: list[int] = []
-        for i in order:
-            if suppressed[i]:
+                np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
+
+        keep = np.ones(n, dtype=bool)
+
+        # Largest first, so the survivor of a containment chain is the biggest box.
+        order = np.argsort(-areas)
+
+        for idx_a in range(n):
+            a = order[idx_a]
+            if not keep[a]:
                 continue
-            keep.append(int(i))
-            bi = boxes[i]
-            xx1 = np.maximum(bi[0], boxes[:, 0])
-            yy1 = np.maximum(bi[1], boxes[:, 1])
-            xx2 = np.minimum(bi[2], boxes[:, 2])
-            yy2 = np.minimum(bi[3], boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + areas - inter + 1e-7)
-            dup = iou > iou_thresh
-            dup[i] = False
-            suppressed |= dup
-        keep_idx = np.array(keep, dtype=np.intp)
+            for idx_b in range(idx_a + 1, n):
+                b = order[idx_b]              # areas[b] <= areas[a]
+                if not keep[b]:
+                    continue
+                if cls_ids[a] != cls_ids[b]:
+                    continue
+
+                x1 = max(boxes[a, 0], boxes[b, 0])
+                y1 = max(boxes[a, 1], boxes[b, 1])
+                x2 = min(boxes[a, 2], boxes[b, 2])
+                y2 = min(boxes[a, 3], boxes[b, 3])
+                if x2 <= x1 or y2 <= y1:
+                    continue
+                inter = (x2 - x1) * (y2 - y1)
+
+                # How much of the SMALLER box (b) lies inside the larger (a):
+                containment_b = inter / max(areas[b], 1e-9)
+
+                if containment_b >= threshold:        # b is nested -> it's a duplicate
+                    scores[a] = max(scores[a], scores[b])   # keep the higher score
+                    keep[b] = False                          # drop the smaller (green)
+
+        keep_idx = np.where(keep)[0]
         return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
 
-    def _per_view_pipeline(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Per-view post-processing: per-class NMS -> cap -> cross-class dedup."""
+    def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray,
+                           cls_ids: np.ndarray, orig_size: tuple[int, int]
+                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Sanity filter + per-class NMS + cross-class dedup."""
+        boxes, scores, cls_ids = self._filter_sane_boxes(
+            boxes, scores, cls_ids, orig_size
+        )
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
         if len(boxes) > 1:
-            keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
+            keep = self._per_class_hard_nms(boxes, scores, cls_ids)
             boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
         if len(scores) > self.max_det:
             top = np.argsort(-scores)[: self.max_det]
@@ -578,295 +408,206 @@ class Miner:
             )
         return boxes, scores, cls_ids
 
-    def _decode_final_dets(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-        apply_optional_dedup: bool = False,
-    ) -> list[BoundingBox]:
-        """
-        Primary path:
-        expected output rows like [x1, y1, x2, y2, conf, cls_id]
-        in letterboxed input coordinates.
-        """
+    # ─── v26-specific decoding ────────────────────────────────────
+    
+    def _decode_v26_output(self, preds: np.ndarray, ratio: float,
+                           pad: tuple[float, float],
+                           orig_size: tuple[int, int],
+                           extra: tuple[int, int] = (0, 0)) -> list[BoundingBox]:  # NEW arg
+        """Decode YOLOv26 output (shape [1, 300, 6] or [300, 6])."""
         if preds.ndim == 3 and preds.shape[0] == 1:
             preds = preds[0]
 
         if preds.ndim != 2 or preds.shape[1] < 6:
-            raise ValueError(f"Unexpected ONNX final-det output shape: {preds.shape}")
+            print(f"Warning: Unexpected v26 output shape: {preds.shape}")
+            return []
 
         boxes = preds[:, :4].astype(np.float32)
         scores = preds[:, 4].astype(np.float32)
         cls_ids = preds[:, 5].astype(np.int32)
-        cls_ids = self.cls_remap[cls_ids]
 
-        # Per-class confidence filter with rescue (replaces scalar threshold)
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
+        n_cls = len(self.class_names)
+        valid = (cls_ids >= 0) & (cls_ids < n_cls)
+        boxes = boxes[valid]
+        scores = scores[valid]
+        cls_ids = cls_ids[valid]
 
         if len(boxes) == 0:
             return []
 
-        pad_w, pad_h = pad
-        orig_w, orig_h = orig_size
-
-        # reverse letterbox
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+        extra_left, _extra_right = extra
 
-        # Box sanity filter (reduces FP)
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            boxes, scores, cls_ids, orig_size
-        )
-        if len(boxes) == 0:
-            return []
-
-        if apply_optional_dedup and len(boxes) > 1:
-            # Soft-NMS path preserved as a tunable option; default below.
-            keep_idx, scores = self._per_class_soft_nms(boxes, scores, cls_ids)
-            boxes = boxes[keep_idx]
-            cls_ids = cls_ids[keep_idx]
-            if len(scores) > self.max_det:
-                top = np.argsort(-scores)[: self.max_det]
-                boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-            if len(boxes) > 1:
-                boxes, scores, cls_ids = self._cross_class_dedup_op(
-                    boxes, scores, cls_ids, self.cross_iou_thresh
-                )
-        else:
-            # Default: per-class hard NMS -> cap -> cross-class dedup
-            boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
-
-        results: list[BoundingBox] = []
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist()
-
-            if x2 <= x1 or y2 <= y1:
-                continue
-
-            results.append(
-                BoundingBox(
-                    x1=int(math.floor(x1)),
-                    y1=int(math.floor(y1)),
-                    x2=int(math.ceil(x2)),
-                    y2=int(math.ceil(y2)),
-                    cls_id=int(cls_id),
-                    conf=float(conf),
-                )
-            )
-
-        return results
-
-    def _decode_raw_yolo(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """
-        Fallback path for raw YOLO predictions.
-        Supports common layouts:
-        - [1, C, N]
-        - [1, N, C]
-        """
-        if preds.ndim != 3:
-            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
-
-        if preds.shape[0] != 1:
-            raise ValueError(f"Unexpected batch dimension in raw output: {preds.shape}")
-
-        preds = preds[0]
-
-        # Normalize to [N, C]
-        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
-            preds = preds.T
-
-        if preds.ndim != 2 or preds.shape[1] < 5:
-            raise ValueError(f"Unexpected normalized raw output shape: {preds.shape}")
-
-        boxes_xywh = preds[:, :4].astype(np.float32)
-        cls_part = preds[:, 4:].astype(np.float32)
-
-        if cls_part.shape[1] == 1:
-            scores = cls_part[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
-            scores = cls_part[np.arange(len(cls_part)), cls_ids]
-        cls_ids = self.cls_remap[cls_ids]
-
-        # Per-class confidence filter with rescue (replaces scalar threshold)
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes_xywh = boxes_xywh[keep]
+        keep = self._conf_filter_mask(scores, cls_ids, extra_left)
+        boxes = boxes[keep]
         scores = scores[keep]
         cls_ids = cls_ids[keep]
-        if len(boxes_xywh) == 0:
-            return []
 
-        boxes = self._xywh_to_xyxy(boxes_xywh)
+        if len(boxes) == 0:
+            return []
 
-        # Order matches fire001 / _decode_final_dets:
-        # unscale -> clip -> sanity filter -> per-view pipeline (NMS, cap, cross-class dedup).
+        # 1) undo letterbox -> coords in the PADDED (widened) image
         pad_w, pad_h = pad
-        orig_w, orig_h = orig_size
         boxes[:, [0, 2]] -= pad_w
         boxes[:, [1, 3]] -= pad_h
         boxes /= ratio
-        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
 
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            boxes, scores, cls_ids, (orig_w, orig_h)
+        # 2) NEW: undo the left/right pre-padding -> coords in the ORIGINAL image.
+        #    Only the LEFT pad shifts x; right pad adds width but no offset.
+        
+        if extra_left:
+            boxes[:, [0, 2]] -= extra_left
+        
+        # 2b) NEW: drop boxes that fall in the black padding region.
+        #     A real detection must have its CENTER inside the original image
+        #     width [0, orig_w]; boxes centered in the black bars are spurious.
+        if extra_left or _extra_right:
+            orig_w, orig_h = orig_size
+            cx = (boxes[:, 0] + boxes[:, 2]) * 0.5
+            inside = (cx >= 0) & (cx <= orig_w)
+            boxes = boxes[inside]
+            scores = scores[inside]
+            cls_ids = cls_ids[inside]
+            if len(boxes) == 0:
+                return []
+
+        # 3) clip to ORIGINAL image bounds (orig_size is the true original size)
+        boxes = self._clip_boxes(boxes, orig_size)
+
+        boxes, scores, cls_ids = self._per_view_pipeline(
+            boxes, scores, cls_ids, orig_size
         )
-        if len(boxes) == 0:
-            return []
 
-        boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
+        return self._build_results(boxes, scores, cls_ids, orig_size)
 
-        results: list[BoundingBox] = []
+    @staticmethod
+    def _build_results(boxes: np.ndarray, scores: np.ndarray,
+                       cls_ids: np.ndarray,
+                       orig_size: tuple[int, int]) -> list[BoundingBox]:
+        results = []
+        orig_w, orig_h = orig_size
         for box, conf, cls_id in zip(boxes, scores, cls_ids):
             x1, y1, x2, y2 = box.tolist()
-
             if x2 <= x1 or y2 <= y1:
                 continue
-
             results.append(
                 BoundingBox(
-                    x1=int(math.floor(x1)),
-                    y1=int(math.floor(y1)),
-                    x2=int(math.ceil(x2)),
-                    y2=int(math.ceil(y2)),
+                    x1=max(0, min(orig_w, int(math.floor(x1)))),
+                    y1=max(0, min(orig_h, int(math.floor(y1)))),
+                    x2=max(0, min(orig_w, int(math.ceil(x2)))),
+                    y2=max(0, min(orig_h, int(math.ceil(y2)))),
                     cls_id=int(cls_id),
-                    conf=float(conf),
+                    conf=float(max(0.0, min(1.0, conf))),
                 )
             )
-
         return results
 
-    def _postprocess(
-        self,
-        output: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """
-        Prefer final detections first.
-        Fallback to raw decode only if needed.
-        """
-        # final detections: [N,6]
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-
-        # final detections: [1,N,6]
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-
-        # fallback raw decode
-        return self._decode_raw_yolo(output, ratio, pad, orig_size)
-
-    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
-        if image is None:
-            raise ValueError("Input image is None")
-        if not isinstance(image, np.ndarray):
-            raise TypeError(f"Input is not numpy array: {type(image)}")
-        if image.ndim != 3:
-            raise ValueError(f"Expected HWC image, got shape={image.shape}")
-        if image.shape[0] <= 0 or image.shape[1] <= 0:
-            raise ValueError(f"Invalid image shape={image.shape}")
-        if image.shape[2] != 3:
-            raise ValueError(f"Expected 3 channels, got shape={image.shape}")
-
-        if image.dtype != np.uint8:
-            image = image.astype(np.uint8)
-
-        input_tensor, ratio, pad, orig_size = self._preprocess(image)
-
-        expected_shape = (1, 3, self.input_height, self.input_width)
-        if input_tensor.shape != expected_shape:
-            raise ValueError(
-                f"Bad input tensor shape={input_tensor.shape}, expected={expected_shape}"
-            )
-
-        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
-        det_output = outputs[0]
-        return self._postprocess(det_output, ratio, pad, orig_size)
-
-    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        """Horizontal-flip TTA.
-
-        Strategy (ported from fire001):
-          1. Predict on original and on flipped image.
-          2. Map flipped boxes back to original coordinates.
-          3. Per-class hard NMS on the union.
-          4. For each kept box, compute the max SAME-CLASS score across the
-             FULL union -- a high-confidence flipped detection raises a
-             borderline original one, but never one of a different class.
-          5. Cross-class dedup to suppress same-physical-object multi-class.
-        """
-        boxes_orig = self._predict_single(image)
-
-        flipped = cv2.flip(image, 1)
-        boxes_flip = self._predict_single(flipped)
-
-        w = image.shape[1]
+    # ─── Single-view inference ────────────────────────────────────
+    
+    def _predict_single(self, image_bgr: np.ndarray,
+                        allow_pad: bool = True) -> list[BoundingBox]:
+        if image_bgr is None or not isinstance(image_bgr, np.ndarray):
+            raise ValueError("Invalid image input")
+        if image_bgr.dtype != np.uint8:
+            image_bgr = image_bgr.astype(np.uint8)
+
+        inp, meta = self._preprocess(image_bgr, allow_pad=allow_pad)
+        outputs = self.session.run(None, {self.input_name: inp})
+
+        ratio = float(meta["ratio"])
+        pad = (float(meta["pad_w"]), float(meta["pad_h"]))
+        orig_size = (int(meta["orig_w"]), int(meta["orig_h"]))
+        extra = (int(meta["extra_left"]), int(meta["extra_right"]))
+
+        return self._decode_v26_output(outputs[0], ratio, pad, orig_size, extra)
+
+    # ─── TTA inference ────────────────────────────────────────────
+    
+    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
+        """3-view TTA: original (no pad) + flip (no pad) + original (L/R padded).
+        All three views return ORIGINAL-image coords, then pooled."""
+        orig_h, orig_w = image_bgr.shape[:2]
+
+        # View 1: original, NO left/right padding
+        boxes_orig = self._predict_single(image_bgr, allow_pad=True)
+        
+        # View 2: horizontal flip, NO left/right padding
+        flipped = cv2.flip(image_bgr, 1)
+        boxes_flip = self._predict_single(flipped, allow_pad=True)
+        w = image_bgr.shape[1]
         boxes_flip = [
-            BoundingBox(
-                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
-                cls_id=b.cls_id, conf=b.conf,
-            )
+            BoundingBox(x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
+                        cls_id=b.cls_id, conf=b.conf)
             for b in boxes_flip
         ]
-
-        all_boxes = boxes_orig + boxes_flip
-        if len(all_boxes) == 0:
+        
+        # View 3: original, WITH left/right padding (only meaningful if square)
+        # boxes_pad = self._predict_single(image_bgr, allow_pad=True)
+        # NOZZLE_CLS = self.class_names.index('nozzle')   # == 2
+        # boxes_pad = [b for b in boxes_pad if b.cls_id != NOZZLE_CLS]
+
+        # # View 4: flip, WITH left/right padding (only meaningful if square)
+        # flipped = cv2.flip(image_bgr, 1)
+        # boxes_pad_flip = self._predict_single(flipped, allow_pad=True)
+        # boxes_pad_flip = [
+        #     BoundingBox(x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
+        #                 cls_id=b.cls_id, conf=b.conf)
+        #     for b in boxes_pad_flip
+        # ]
+        # NOZZLE_CLS = self.class_names.index('nozzle')   # == 2
+        # boxes_pad_flip = [b for b in boxes_pad_flip if b.cls_id != NOZZLE_CLS]
+
+        all_boxes = boxes_orig + boxes_flip# + boxes_pad# + boxes_pad_flip
+        if not all_boxes:
             return []
 
-        coords = np.array(
-            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
-        )
+        coords = np.array([[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32)
         scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
         cls_ids = np.array([b.cls_id for b in all_boxes], dtype=np.int32)
 
-        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
+        # Per-class NMS (uses per-class IoU thresholds)
+        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids)
         if len(hard_keep) == 0:
             return []
+        
         if len(hard_keep) > self.max_det:
             top = np.argsort(-scores[hard_keep])[: self.max_det]
             hard_keep = hard_keep[top]
-
-        # Class-aware cluster-max score boost (fixes the silent cross-class
-        # leak in the previous _max_score_per_cluster).
+        
+        # For confidence boost, use average IoU threshold (or could use median)
+        avg_iou = float(np.mean(self._iou_thres_array))
         boosted = self._max_score_per_cluster(
             coords[hard_keep], cls_ids[hard_keep],
-            coords, scores, cls_ids, self.iou_thres,
+            coords, scores, cls_ids, avg_iou,
         )
 
         kept_coords = coords[hard_keep]
         kept_cls = cls_ids[hard_keep]
+        
+        # Cross-class dedup
         if len(kept_coords) > 1:
             kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
                 kept_coords, boosted, kept_cls, self.cross_iou_thresh
             )
 
-        return [
-            BoundingBox(
-                x1=int(math.floor(kept_coords[j, 0])),
-                y1=int(math.floor(kept_coords[j, 1])),
-                x2=int(math.ceil(kept_coords[j, 2])),
-                y2=int(math.ceil(kept_coords[j, 3])),
-                cls_id=int(kept_cls[j]),
-                conf=float(boosted[j]),
+        out_boxes = []
+        for j in range(len(kept_coords)):
+            x1, y1, x2, y2 = kept_coords[j].tolist()
+            if x2 <= x1 or y2 <= y1:
+                continue
+            out_boxes.append(
+                BoundingBox(
+                    x1=max(0, min(orig_w, int(math.floor(x1)))),
+                    y1=max(0, min(orig_h, int(math.floor(y1)))),
+                    x2=max(0, min(orig_w, int(math.ceil(x2)))),
+                    y2=max(0, min(orig_h, int(math.ceil(y2)))),
+                    cls_id=int(kept_cls[j]),
+                    conf=float(max(0.0, min(1.0, boosted[j]))),
+                )
             )
-            for j in range(len(kept_coords))
-        ]
+        return out_boxes
 
+    # ─── Public API ───────────────────────────────────────────────
+    
     def predict_batch(
         self,
         batch_images: list[ndarray],
@@ -874,23 +615,18 @@ class Miner:
         n_keypoints: int,
     ) -> list[TVFrameResult]:
         results: list[TVFrameResult] = []
-
-        for frame_number_in_batch, image in enumerate(batch_images):
+        for idx, image in enumerate(batch_images):
             try:
-                if self.use_tta:
-                    boxes = self._predict_tta(image)
-                else:
-                    boxes = self._predict_single(image)
+                boxes = self._infer_single(image)
             except Exception as e:
-                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
+                print(f"Inference failed for frame {offset + idx}: {e}")
                 boxes = []
-
+            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
             results.append(
                 TVFrameResult(
-                    frame_id=offset + frame_number_in_batch,
+                    frame_id=offset + idx,
                     boxes=boxes,
-                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
+                    keypoints=keypoints,
                 )
             )
-
         return results

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1f9c13ee4403ddbbccd9d9707a151b1474cbb33124a2ef2965fa57d1e821ed03
-size 19287011
+oid sha256:5eff40e23f79ec4d26d8639de704fdc432abab52f0fc44ec908037e9a2316824
+size 20833918