subnet_bridge: copy winning miner repo into library

README.md

@@ -10,9 +10,18 @@ tags:
 manako:
   source: winner_fetch
   manifest_element_name: manak0/Detect-fire
-  winner_repo_id: SuperBitDev/fire4
-  winner_revision: 0679fa90aae80dacbcaa1235755c2791da2f7cc0
-  note: E=0.03082304 (map50=0.600000, size_mb=19.465956)
+  winner_repo_id: SuperBitDev/cw1
+  winner_revision: 20a49423c3135551fe19c0d78df3d0a15b0b4917
+  note: E=0.03105072 (map50=0.600000, size_mb=19.323222)
 ---
 
-## YOLO26 ONNX detector
+# Detect-fire-winner
+
+Published winning miner converted into a library element.
+
+- Source winner repo: `SuperBitDev/cw1`
+- Source revision: `20a49423c3135551fe19c0d78df3d0a15b0b4917`
+- Manifest element: `manak0/Detect-fire`
+- Element type: `Detect`
+- Objects: fire, smoke, fire extinguisher
+- Runtime model type: `ultralytics-yolo`

chute_config.yml

@@ -2,13 +2,12 @@ Image:
   from_base: parachutes/python:3.12
   run_command:
     - pip install --upgrade setuptools wheel
-    - pip install 'numpy>=1.23' 'onnxruntime-gpu[cuda,cudnn]>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
-    - pip install torch torchvision
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
+    - pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
 
 NodeSelector:
   gpu_count: 1
   min_vram_gb_per_gpu: 16
-  max_hourly_price_per_gpu: 2
   include:
     - pro_6000
 

class_names.txt

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

miner.py

@@ -1,977 +1,896 @@
-from pathlib import Path
-import math
-
-import cv2
-import numpy as np
-import onnxruntime as ort
-from numpy import ndarray
-from pydantic import BaseModel
-
-
-class BoundingBox(BaseModel):
-    x1: int
-    y1: int
-    x2: int
-    y2: int
-    cls_id: int
-    conf: float
-
-
-class TVFrameResult(BaseModel):
-    frame_id: int
-    boxes: list[BoundingBox]
-    keypoints: list[tuple[int, int]]
-
-
-class Miner:
-    """ONNX Runtime miner for fire / smoke / fire_extinguisher detection.
-
-    Strategy (ported from offense miner):
-      - per-class confidence threshold with per-class rescue bonus
-      - per-class hard NMS, then cross-class dedup
-      - horizontal-flip TTA with full-set cluster score boost
-    Plus fire001 specifics: class remap, sanity-box filter, TTA toggle.
-    """
-
-    class_names = ["fire", "smoke", "fire extinguisher"]
-    # FALLBACK order the model emits classes in -- remapped to `class_names`
-    # index by `self.cls_remap` (built in __init__). The authoritative order
-    # is read from the ONNX `names` metadata that Ultralytics embeds at
-    # export time (ships inside weights.onnx), so a retrained model with a
-    # different class order is remapped correctly without code changes.
-    # Used only when that metadata is missing or unparsable.
-    _model_class_order = ["fire", "fire extinguisher", "smoke"]
-
-    iou_thres = 0.45
-    cross_iou_thresh = 0.8
-    max_det = 150
-
-    # Per-class confidence thresholds. Higher = fewer FP for that class.
-    # Indexed by class_names order: [fire, smoke, fire_extinguisher].
-    _conf_thres_array = np.array(
-        [0.42, 0.2, 0.15], dtype=np.float32
-    )
-    # Per-class rescue bonus. If a class has ZERO boxes passing the threshold
-    # in a frame, its top-1 candidate is admitted when its score is at least
-    # (threshold - bonus). Fire and smoke get a small bonus (variable
-    # appearance); fire extinguisher does not (distinctive object, leave FP
-    # control strict).
-    _bonus_array = np.array(
-        [0.28, 0.05, 0.05], dtype=np.float32
-    )
-
-    # Box sanity filter (fire001-specific FP reduction): drop tiny / degenerate
-    # / image-spanning / extreme aspect ratio boxes.
-    min_box_area = 14 * 14
-    min_side = 8
-    max_aspect_ratio = 8.0
-
-    # Smoke merge: two smoke boxes whose intersection covers at least this
-    # fraction of the SMALLER box are treated as the same smoke object and
-    # replaced by their union. Catches nested boxes (IoU below NMS threshold)
-    # and fragmented plume detections.
-    smoke_merge_overlap = 0.8
-
-    # ── Low-confidence color-prior FP filters ───────────────────────────────
-    # Ported from the firedetect1007 miner's color checks, but applied ONLY to
-    # the borderline confidence band (just above each per-class threshold) and
-    # ONLY on color frames. A fire/extinguisher detection there is dropped when
-    # its pixels clearly do not match the expected appearance: warm/bright for
-    # fire, red for extinguisher. High-confidence detections are never touched.
-    #
-    # The reference miner ran these unconditionally -- a BUG on this validator,
-    # which feeds some frames as grayscale (a true red extinguisher is gray
-    # there, so a red test would wrongly delete it). We skip the filter when the
-    # ROI is near-grayscale, so it never fires on those frames.
-    #
-    # Tunable: set a max-conf gate to 0.0 to disable that filter. After a model
-    # retrain, re-validate these with tune_miner.py (the gates are relative to
-    # the per-class thresholds, so they move when those move).
-    fire_color_filter_max_conf = 0.45      # only fire boxes in (thresh, 0.45]
-    fire_ext_color_filter_max_conf = 0.40  # only ext boxes in (thresh, 0.40]
-    color_filter_min_saturation = 0.06     # skip filter if ROI is near-grayscale
-
-    # ── Corroboration FP filters (optional; OFF by default) ─────────────────
-    # Ported in spirit from firedetect1007. Both REMOVE borderline boxes that
-    # lack support -- a precision play for the validator's FP pillar. OFF by
-    # default because, unlike the color priors, they can also drop true
-    # positives; enable + sweep with verify_filters.py and keep only the
-    # settings that raise the measured composite. A max-conf gate of 0.0
-    # disables the corresponding filter.
-    #   edge filter: drop boxes touching the frame border in a low-conf band
-    #     (the validator scales/crops, so border-hugging boxes are often the
-    #     truncated remains of an object whose body is off-frame).
-    #   tta view filter: drop low-conf boxes that appear in only ONE of the two
-    #     horizontal-flip TTA views (a real object is usually seen in both).
-    use_edge_filter = False
-    edge_filter_max_conf = 0.0      # drop edge-touching boxes with conf <= this
-    edge_tol = 2.0                  # px from the border counted as "on edge"
-    use_tta_view_filter = False
-    tta_view_filter_max_conf = 0.0  # drop single-view boxes with conf <= this
-    tta_view_iou_thresh = 0.5       # IoU for "same object seen in both views"
-
-    def __init__(self, path_hf_repo: Path) -> None:
-        model_path = path_hf_repo / "weights.onnx"
-        print("ORT version:", ort.__version__)
-
-        try:
-            ort.preload_dlls()
-            print("✅ onnxruntime.preload_dlls() success")
-        except Exception as 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])
-        # i.e. converts a model-side class id into the canonical class id
-        # that downstream code (BoundingBox.cls_id, validator) expects.
-        # 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,
-        )
-
-        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)
-
-        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
-
-        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
-        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
-
-        self.use_tta = True
-
-        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}")
-        print("per-class conf: " + ", ".join(
-            f"{n}={t:.3f}" for n, t in zip(
-                self.class_names, self._conf_thres_array.tolist()
-            )
-        ))
-
-    def __repr__(self) -> str:
-        return (
-            f"ONNXRuntime(session={type(self.session).__name__}, "
-            f"providers={self.session.get_providers()})"
-        )
-
-    @staticmethod
-    def _safe_dim(value, default: int) -> int:
-        return value if isinstance(value, int) and value > 0 else default
-
-    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: 'fire', ...}
-            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
-
-    def _letterbox(
-        self,
-        image: ndarray,
-        new_shape: tuple[int, int],
-        color=(114, 114, 114),
-    ) -> tuple[ndarray, float, tuple[float, float]]:
-        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) / 2.0
-        dh = (new_h - resized_h) / 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]]:
-        orig_h, orig_w = image.shape[:2]
-        img, ratio, pad = self._letterbox(
-            image, (self.input_width, self.input_height)
-        )
-        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=np.float32)
-        return img, ratio, pad, (orig_w, orig_h)
-
-    @staticmethod
-    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
-        w, h = image_size
-        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
-        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
-        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
-        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
-        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
-
-    @staticmethod
-    def _hard_nms(
-        boxes: np.ndarray, scores: np.ndarray, iou_thresh: float
-    ) -> np.ndarray:
-        n = len(boxes)
-        if n == 0:
-            return np.array([], dtype=np.intp)
-        order = np.argsort(-scores)
-        keep: list[int] = []
-        while len(order) > 0:
-            i = int(order[0])
-            keep.append(i)
-            if len(order) == 1:
-                break
-            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)
-            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,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp)
-        all_keep: list[int] = []
-        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)
-            all_keep.extend(indices[keep].tolist())
-        all_keep.sort()
-        return np.array(all_keep, dtype=np.intp)
-
-    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]:
-        """Remove near-duplicate boxes across classes.
-
-        Order candidates by (score - per_class_threshold) margin, then by area;
-        keep the highest, suppress every other box with IoU > iou_thresh.
-        This suppresses the case where the same physical object is detected
-        as multiple classes (e.g. fire vs smoke on the same flames).
-        """
-        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)
-        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]:
-                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)
-        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
-
-    def _merge_smoke_boxes(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Merge multiple detections of the same smoke object into one box.
-
-        Smoke has diffuse boundaries, so one plume often yields several
-        overlapping / nested boxes that per-class NMS does not collapse
-        (nested boxes can have IoU below `iou_thres`). Any two smoke boxes
-        whose intersection covers >= `smoke_merge_overlap` of the smaller box
-        are merged into their union with the max confidence of the pair.
-        Repeats until no pair merges, so chains of fragments collapse too.
-        """
-        smoke_cls = self.class_names.index("smoke")
-        smoke_idx = np.where(cls_ids == smoke_cls)[0]
-        if len(smoke_idx) <= 1:
-            return boxes, scores, cls_ids
-
-        sb = boxes[smoke_idx].astype(np.float32).tolist()
-        ss = scores[smoke_idx].astype(np.float32).tolist()
-        merged_any = True
-        while merged_any and len(sb) > 1:
-            merged_any = False
-            for i in range(len(sb)):
-                for j in range(i + 1, len(sb)):
-                    a, b = sb[i], sb[j]
-                    ix1 = max(a[0], b[0])
-                    iy1 = max(a[1], b[1])
-                    ix2 = min(a[2], b[2])
-                    iy2 = min(a[3], b[3])
-                    inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
-                    area_a = max(0.0, a[2] - a[0]) * max(0.0, a[3] - a[1])
-                    area_b = max(0.0, b[2] - b[0]) * max(0.0, b[3] - b[1])
-                    smaller = min(area_a, area_b)
-                    if inter / (smaller + 1e-7) >= self.smoke_merge_overlap:
-                        sb[i] = [
-                            min(a[0], b[0]), min(a[1], b[1]),
-                            max(a[2], b[2]), max(a[3], b[3]),
-                        ]
-                        ss[i] = max(ss[i], ss[j])
-                        del sb[j]
-                        del ss[j]
-                        merged_any = True
-                        break
-                if merged_any:
-                    break
-
-        other = cls_ids != smoke_cls
-        new_boxes = np.concatenate(
-            [boxes[other].astype(np.float32),
-             np.array(sb, dtype=np.float32).reshape(-1, 4)]
-        )
-        new_scores = np.concatenate(
-            [scores[other].astype(np.float32),
-             np.array(ss, dtype=np.float32)]
-        )
-        new_cls = np.concatenate(
-            [cls_ids[other].astype(np.int32),
-             np.full(len(sb), smoke_cls, dtype=np.int32)]
-        )
-        return new_boxes, new_scores, new_cls
-
-    @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.
-
-        Used after horizontal-flip TTA: a high-confidence flipped detection
-        can raise the score of the corresponding original detection.
-        """
-        n = len(post_boxes)
-        if n == 0:
-            return np.empty(0, dtype=np.float32)
-        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
-                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
-        out = np.empty(n, dtype=np.float32)
-        for i in range(n):
-            bi = post_boxes[i]
-            xx1 = np.maximum(bi[0], full_boxes[:, 0])
-            yy1 = np.maximum(bi[1], full_boxes[:, 1])
-            xx2 = np.minimum(bi[2], full_boxes[:, 2])
-            yy2 = np.minimum(bi[3], full_boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + full_areas - inter + 1e-7)
-            cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
-            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)."""
-        if len(scores) == 0:
-            return np.zeros(0, dtype=bool)
-        thr = self._conf_thres_array[cls_ids]
-        keep = scores >= thr
-        for c in np.unique(cls_ids):
-            b = float(self._bonus_array[c])
-            if b <= 0.0:
-                continue
-            cm = cls_ids == c
-            if keep[cm].any():
-                continue
-            idx = np.where(cm)[0]
-            top = int(idx[int(np.argmax(scores[idx]))])
-            if scores[top] >= self._conf_thres_array[c] - b:
-                keep[top] = True
-        return keep
-
-    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]:
-        """Drop tiny / degenerate / image-spanning / extreme-AR boxes (FP)."""
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
-        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:
-                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]
-
-    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 pipeline: per-class NMS -> cap -> cross-class dedup -> smoke merge."""
-        if len(boxes) > 1:
-            keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
-            boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
-        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
-            )
-        if len(boxes) > 1:
-            boxes, scores, cls_ids = self._merge_smoke_boxes(boxes, scores, cls_ids)
-        return boxes, scores, cls_ids
-
-    @staticmethod
-    def _roi_for_box(image: np.ndarray, box: BoundingBox) -> np.ndarray | None:
-        """Clip a BoundingBox to the image and return its BGR pixel ROI."""
-        h, w = image.shape[:2]
-        x1 = max(0, int(math.floor(box.x1)))
-        y1 = max(0, int(math.floor(box.y1)))
-        x2 = min(w, int(math.ceil(box.x2)))
-        y2 = min(h, int(math.ceil(box.y2)))
-        if x2 <= x1 or y2 <= y1:
-            return None
-        roi = image[y1:y2, x1:x2]
-        return roi if roi.size else None
-
-    def _roi_is_near_grayscale(self, roi: np.ndarray) -> bool:
-        """True if the ROI carries almost no color (validator grayscale frame).
-        On such ROIs the color priors are skipped so they can't delete valid
-        red/warm objects that have been stripped of color."""
-        mx = roi.max(axis=2).astype(np.float32)
-        mn = roi.min(axis=2).astype(np.float32)
-        sat = (mx - mn) / (mx + 1e-6)
-        return float(sat.mean()) < self.color_filter_min_saturation
-
-    @staticmethod
-    def _passes_fire_color(roi: np.ndarray) -> bool:
-        """Fire is warm and/or has a bright hotspot. ROI is BGR."""
-        blue = roi[:, :, 0].astype(np.float32)
-        green = roi[:, :, 1].astype(np.float32)
-        red = roi[:, :, 2].astype(np.float32)
-        mean_r = float(np.mean(red))
-        max_rgb = float(max(np.max(red), np.max(green), np.max(blue)))
-        bright_frac = float(np.mean(np.max(roi, axis=2) >= 150))
-        # A bright hotspot is fire-like even with little hue (also covers the
-        # near-white core of an intense flame).
-        if max_rgb >= 200.0 and bright_frac >= 0.01:
-            return True
-        warm = (red > green + 10.0) & (red > blue + 10.0)
-        warm_frac = float(np.mean(warm))
-        r_minus_g = mean_r - float(np.mean(green))
-        if warm_frac >= 0.05 and (
-            max_rgb >= 120.0 or mean_r >= 120.0 or warm_frac >= 0.15
-        ):
-            return True
-        if bright_frac >= 0.12 and r_minus_g >= 2.0:
-            return True
-        return False
-
-    @staticmethod
-    def _passes_fire_ext_red_color(roi: np.ndarray) -> bool:
-        """Fire extinguishers are red. ROI is BGR. Lenient: only clearly
-        cool/green/blue or very dark regions fail."""
-        blue = roi[:, :, 0].astype(np.float32)
-        green = roi[:, :, 1].astype(np.float32)
-        red = roi[:, :, 2].astype(np.float32)
-        red_dom = float(np.mean((red > green + 10.0) & (red > blue + 10.0)))
-        if red_dom >= 0.03:
-            return True
-        if (float(np.mean(red)) - float(np.mean(green))) >= 0.0 and \
-                float(np.mean(red)) >= 50.0:
-            return True
-        return False
-
-    def _remove_edge_low_conf(
-        self, results: list[BoundingBox], orig_size: tuple[int, int]
-    ) -> list[BoundingBox]:
-        """Drop border-hugging boxes in the low-confidence band."""
-        if (
-            not self.use_edge_filter
-            or self.edge_filter_max_conf <= 0.0
-            or not results
-        ):
-            return results
-        w, h = orig_size
-        tol = self.edge_tol
-        out: list[BoundingBox] = []
-        for b in results:
-            on_edge = (
-                b.x1 <= tol
-                or b.y1 <= tol
-                or b.x2 >= w - 1 - tol
-                or b.y2 >= h - 1 - tol
-            )
-            if on_edge and b.conf <= self.edge_filter_max_conf:
-                continue
-            out.append(b)
-        return out
-
-    def _views_corroborated(
-        self,
-        post_boxes: np.ndarray,
-        post_cls: np.ndarray,
-        full_boxes: np.ndarray,
-        full_cls: np.ndarray,
-        full_views: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """For each post-NMS box, True if same-class detections from >= 2
-        distinct TTA views overlap it (IoU >= iou_thresh) in the full union."""
-        n = len(post_boxes)
-        if n == 0:
-            return np.zeros(0, dtype=bool)
-        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
-                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
-        out = np.zeros(n, dtype=bool)
-        for i in range(n):
-            bi = post_boxes[i]
-            xx1 = np.maximum(bi[0], full_boxes[:, 0])
-            yy1 = np.maximum(bi[1], full_boxes[:, 1])
-            xx2 = np.minimum(bi[2], full_boxes[:, 2])
-            yy2 = np.minimum(bi[3], full_boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + full_areas - inter + 1e-7)
-            mask = (iou >= iou_thresh) & (full_cls == post_cls[i])
-            if np.any(mask):
-                out[i] = len(np.unique(full_views[mask])) >= 2
-        return out
-
-    def _filter_low_conf_by_color(
-        self, image: np.ndarray, results: list[BoundingBox]
-    ) -> list[BoundingBox]:
-        """Drop borderline fire / extinguisher detections whose pixels clearly
-        contradict the class's expected color. No-op on near-grayscale ROIs and
-        on detections above the per-class color-filter conf gate."""
-        if not results:
-            return results
-        cls_fire = self.class_names.index("fire")
-        cls_ext = self.class_names.index("fire extinguisher")
-        out: list[BoundingBox] = []
-        for box in results:
-            check_fire = (
-                box.cls_id == cls_fire
-                and box.conf <= self.fire_color_filter_max_conf
-            )
-            check_ext = (
-                box.cls_id == cls_ext
-                and box.conf <= self.fire_ext_color_filter_max_conf
-            )
-            if not check_fire and not check_ext:
-                out.append(box)
-                continue
-            roi = self._roi_for_box(image, box)
-            if roi is None or self._roi_is_near_grayscale(roi):
-                out.append(box)
-                continue
-            if check_fire and not self._passes_fire_color(roi):
-                continue
-            if check_ext and not self._passes_fire_ext_red_color(roi):
-                continue
-            out.append(box)
-        return out
-
-    @staticmethod
-    def _build_results(
-        boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray
-    ) -> list[BoundingBox]:
-        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_final_dets(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """Final-detection output path: rows shaped [x1, y1, x2, y2, conf, cls_id]."""
-        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}")
-
-        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]
-
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-        if len(boxes) == 0:
-            return []
-
-        pad_w, pad_h = pad
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            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)
-
-    def _decode_raw_yolo(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """Fallback raw-YOLO output path: per-anchor class logits."""
-        if preds.ndim != 3 or preds.shape[0] != 1:
-            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
-        preds = preds[0]
-        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 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]
-
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes_xywh = boxes_xywh[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-        if len(boxes_xywh) == 0:
-            return []
-        boxes = self._xywh_to_xyxy(boxes_xywh)
-
-        pad_w, pad_h = pad
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            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)
-
-    def _postprocess(
-        self,
-        output: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-        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 = (1, 3, self.input_height, self.input_width)
-        if input_tensor.shape != expected:
-            raise ValueError(
-                f"Bad input tensor shape={input_tensor.shape}, expected={expected}"
-            )
-
-        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
-        return self._postprocess(outputs[0], ratio, pad, orig_size)
-
-    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        """Horizontal-flip TTA.
-
-        Strategy:
-          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 (not just the post-NMS subset) -- this lets a high-
-             confidence flipped detection raise a borderline original one.
-          5. Cross-class dedup to suppress same-physical-object multi-class.
-          6. Smoke merge: overlapping / nested smoke boxes collapse into
-             their union (one box per smoke object).
-        """
-        boxes_orig = self._predict_single(image)
-        flipped = cv2.flip(image, 1)
-        boxes_flip = self._predict_single(flipped)
-        w = image.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,
-            )
-            for b in boxes_flip
-        ]
-        all_boxes = boxes_orig + boxes_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
-        )
-        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)
-        # view_id 0 = original, 1 = horizontal flip (mapped back to orig coords)
-        view_ids = np.array(
-            [0] * len(boxes_orig) + [1] * len(boxes_flip), dtype=np.int32
-        )
-
-        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
-        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]
-
-        boosted = self._max_score_per_cluster(
-            coords[hard_keep], cls_ids[hard_keep],
-            coords, scores, cls_ids, self.iou_thres,
-        )
-
-        kept_coords = coords[hard_keep]
-        kept_cls = cls_ids[hard_keep]
-
-        # Optional: drop low-conf detections seen in only one TTA view.
-        if (
-            self.use_tta_view_filter
-            and self.tta_view_filter_max_conf > 0.0
-            and len(kept_coords) > 0
-        ):
-            corrob = self._views_corroborated(
-                kept_coords, kept_cls, coords, cls_ids, view_ids,
-                self.tta_view_iou_thresh,
-            )
-            keep = ~((boosted <= self.tta_view_filter_max_conf) & (~corrob))
-            kept_coords = kept_coords[keep]
-            boosted = boosted[keep]
-            kept_cls = kept_cls[keep]
-
-        if len(kept_coords) > 1:
-            kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
-                kept_coords, boosted, kept_cls, self.cross_iou_thresh
-            )
-        if len(kept_coords) > 1:
-            kept_coords, boosted, kept_cls = self._merge_smoke_boxes(
-                kept_coords, boosted, kept_cls
-            )
-
-        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]),
-            )
-            for j in range(len(kept_coords))
-        ]
-
-    def predict_batch(
-        self,
-        batch_images: list[ndarray],
-        offset: int,
-        n_keypoints: int,
-    ) -> list[TVFrameResult]:
-        results: list[TVFrameResult] = []
-        for frame_number_in_batch, image in enumerate(batch_images):
-            try:
-                if self.use_tta:
-                    boxes = self._predict_tta(image)
-                else:
-                    boxes = self._predict_single(image)
-                # Color-prior + edge FP filters on the merged result, in
-                # original-image coords. Single insertion point so they run once
-                # per frame for both the TTA and non-TTA paths.
-                if isinstance(image, np.ndarray) and image.ndim == 3:
-                    boxes = self._filter_low_conf_by_color(image, boxes)
-                    boxes = self._remove_edge_low_conf(
-                        boxes, (image.shape[1], image.shape[0])
-                    )
-            except Exception as e:
-                print(
-                    f"⚠️ Inference failed for frame "
-                    f"{offset + frame_number_in_batch}: {e}"
-                )
-                boxes = []
-            results.append(
-                TVFrameResult(
-                    frame_id=offset + frame_number_in_batch,
-                    boxes=boxes,
-                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
-                )
-            )
-        return results
+from pathlib import Path
+import math
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from numpy import ndarray
+from pydantic import BaseModel
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+
+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"]
+        print("ORT version:", ort.__version__)
+
+        try:
+            ort.preload_dlls()
+            print("✅ onnxruntime.preload_dlls() success")
+        except Exception as 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
+        )
+
+        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)
+
+        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}")
+
+    def __repr__(self) -> str:
+        return (
+            f"ONNXRuntime(session={type(self.session).__name__}, "
+            f"providers={self.session.get_providers()})"
+        )
+
+    @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
+
+    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
+        """
+        orig_h, orig_w = image.shape[:2]
+
+        img, ratio, pad = self._letterbox(
+            image, (self.input_width, self.input_height)
+        )
+        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)
+
+    @staticmethod
+    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
+        w, h = image_size
+        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
+        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
+        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
+        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
+        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:
+                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])
+            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."""
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+        all_keep: list[int] = []
+        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)
+            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:
+            return boxes, scores, cls_ids
+        orig_w, orig_h = orig_size
+        image_area = float(orig_w * orig_h)
+        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:
+                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]
+
+    @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.
+        """
+        n = len(post_boxes)
+        if n == 0:
+            return np.empty(0, dtype=np.float32)
+        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
+                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
+        out = np.empty(n, dtype=np.float32)
+        for i in range(n):
+            bi = post_boxes[i]
+            xx1 = np.maximum(bi[0], full_boxes[:, 0])
+            yy1 = np.maximum(bi[1], full_boxes[:, 1])
+            xx2 = np.minimum(bi[2], full_boxes[:, 2])
+            yy2 = np.minimum(bi[3], full_boxes[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
+            iou = inter / (a_i + full_areas - inter + 1e-7)
+            cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
+            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).
+        """
+        if len(scores) == 0:
+            return np.zeros(0, dtype=bool)
+        thr = self._conf_thres_array[cls_ids]
+        keep = scores >= thr
+        for c in np.unique(cls_ids):
+            b = float(self._bonus_array[c])
+            if b <= 0.0:
+                continue
+            cm = cls_ids == c
+            if keep[cm].any():
+                continue
+            idx = np.where(cm)[0]
+            top = int(idx[int(np.argmax(scores[idx]))])
+            if scores[top] >= self._conf_thres_array[c] - b:
+                keep[top] = True
+        return keep
+
+    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]:
+        """Remove near-duplicate boxes across classes.
+
+        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.
+        """
+        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)
+        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]:
+                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)
+        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."""
+        if len(boxes) > 1:
+            keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
+            boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
+        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
+            )
+        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.
+        """
+        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}")
+
+        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]
+
+        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))
+
+        # 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]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+        if len(boxes_xywh) == 0:
+            return []
+
+        boxes = self._xywh_to_xyxy(boxes_xywh)
+
+        # Order matches fire001 / _decode_final_dets:
+        # unscale -> clip -> sanity filter -> per-view pipeline (NMS, cap, cross-class dedup).
+        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)
+        )
+        if len(boxes) == 0:
+            return []
+
+        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 _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]
+        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,
+            )
+            for b in boxes_flip
+        ]
+
+        all_boxes = boxes_orig + boxes_flip
+        if len(all_boxes) == 0:
+            return []
+
+        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)
+        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).
+        boosted = self._max_score_per_cluster(
+            coords[hard_keep], cls_ids[hard_keep],
+            coords, scores, cls_ids, self.iou_thres,
+        )
+
+        kept_coords = coords[hard_keep]
+        kept_cls = cls_ids[hard_keep]
+        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]),
+            )
+            for j in range(len(kept_coords))
+        ]
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        results: list[TVFrameResult] = []
+
+        for frame_number_in_batch, image in enumerate(batch_images):
+            try:
+                if self.use_tta:
+                    boxes = self._predict_tta(image)
+                else:
+                    boxes = self._predict_single(image)
+            except Exception as e:
+                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
+                boxes = []
+
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + frame_number_in_batch,
+                    boxes=boxes,
+                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
+                )
+            )
+
+        return results

my_chute.py

@@ -1,440 +0,0 @@
-#!/usr/bin/env python3
-
-import sys
-from importlib.machinery import PathFinder
-from importlib.util import module_from_spec, spec_from_file_location
-from inspect import signature
-from pathlib import Path
-from site import getsitepackages, getusersitepackages
-from sysconfig import get_paths
-from tempfile import NamedTemporaryFile
-from typing import Any, Generator
-from urllib.parse import quote
-
-import torch
-from aiohttp import ClientSession
-from chutes.chute import Chute, NodeSelector
-from chutes.image import Image
-from base64 import b64decode
-from cv2 import CAP_PROP_FRAME_COUNT, VideoCapture, imdecode, IMREAD_COLOR
-from huggingface_hub import snapshot_download
-from numpy import ndarray, frombuffer, uint8
-from pydantic import BaseModel
-from yaml import safe_load
-
-##============VARIABLES=======================
-HF_REPO_NAME = "yevheniiapopova/ScoreVisionBeverage"
-HF_REPO_REVISION = "main"
-CHUTES_USERNAME = "dragonsn44miner"
-CHUTE_NAME = "dragonsn44miner"
-FILENAME = "miner.py"
-CLASSNAME = "Miner"
-CHUTE_CONFIG_FILENAME = "chute_config.yml"
-MAX_LOADED_MODEL_SIZE_GB = 5.0
-
-
-##=========DATA CLASSES================
-class TVFrame(BaseModel):
-    frame_id: int
-    url: str | None = None
-    data: str | None = None
-
-
-class TVPredictInput(BaseModel):
-    url: str | None = None
-    frames: list[TVFrame] | None = None
-    meta: dict[str, Any] = {}
-
-
-class TVPredictOutput(BaseModel):
-    success: bool
-    predictions: dict[str, list[dict]] | None = None
-    error: str | None = None
-
-
-##=========UTILITY FUNCTIONS=================
-def get_whitelisted_paths() -> set[Path]:
-    STDLIB_ROOT = Path(get_paths()["stdlib"]).resolve()
-    SITE_PACKAGES_ROOTS = {Path(p).resolve() for p in getsitepackages()} 
-    SITE_PACKAGES_ROOTS.add(Path(getusersitepackages()).resolve())
-
-    return {STDLIB_ROOT, *SITE_PACKAGES_ROOTS}
-
-def post_check_files_against_whitelist(*, before: set[str], miner_path: Path) -> None:
-    miner_path = miner_path.resolve()
-    WHITELIST = get_whitelisted_paths()
-    after = set(sys.modules.keys()) - before
-    offenders = []
-    for module_loaded_name in sorted(after):
-        module_loaded = sys.modules.get(module_loaded_name)
-        if module_loaded is None:
-            continue
-        module_loaded_file = getattr(module_loaded, "__file__", None)
-        if not module_loaded_file:
-            continue  
-        module_loaded_filepath = Path(module_loaded_file).resolve()
-        if module_loaded_filepath == miner_path:
-            continue
-        if any(module_loaded_filepath.is_relative_to(root) for root in WHITELIST):
-            continue
-        offenders.append(module_loaded_name)
-
-    if offenders:
-        raise ImportError(
-            f"Miner loaded modules outside the whitelist: {', '.join(offenders)}"
-        )
-
-
-def verify_cuda_version() -> str:
-    min_version = (12, 8)
-    if not torch.cuda.is_available():
-        return "❌ CUDA is not available. Make sure you have a CUDA-capable GPU and correct drivers installed"
-    device_count = torch.cuda.device_count()
-    if device_count < 1:
-        return "❌ No CUDA devices detected"
-    cuda_version_str = torch.version.cuda
-    if cuda_version_str is None:
-        return "❌ PyTorch was not compiled with CUDA support"
-    try:
-        cuda_version = tuple(map(int, cuda_version_str.split(".")))
-    except Exception as e:
-        return f"❌ Unable to parse CUDA version string: {e}"
-    if cuda_version < min_version:
-        return f"❌ CUDA version {cuda_version_str} is too old. Requires >= {min_version[0]}.{min_version[1]}"
-    return f"CUDA {cuda_version_str} detected_with {device_count} device(s)"
-
-
-def load_chute_config_from_hf_repo(config_path: Path) -> dict:
-    try:
-        if not config_path.exists():
-            raise ValueError("No config file found in repo")
-
-        with config_path.open() as configuration_file:
-            config = safe_load(configuration_file)
-            print("✅ Loaded Chutes Configuration")
-            return config or {}
-    except Exception as e:
-        print(f"⚠️ Failed to load Chutes Configuration. Using defaults: {e}")
-        return {}
-
-
-def safe_instantiate(cls, config: dict):
-    params = {k: v for k, v in config.items() if k in signature(cls).parameters}
-    print(
-        f"The following parameters will be applied when instantiating {cls.__name__}: {params}"
-    )
-    obj = cls(**params)
-    for function_name, value in config.items():
-        if not hasattr(obj, function_name) or not callable(getattr(obj, function_name)):
-            continue
-        print(f"applying .{function_name}({value})")
-        method = getattr(obj, function_name)
-        if isinstance(value, list):
-            for v in value:
-                if isinstance(v, (tuple, list)):
-                    method(*v)
-                else:
-                    method(v)
-        else:
-            method(value)
-    return obj
-
-
-def load_chute(
-    hf_repo: str,
-    hf_revision: str,
-    config_filename: str,
-    chutes_username: str,
-    chute_name: str,
-) -> Chute:
-    hf_repo_path = Path(snapshot_download(hf_repo, revision=hf_revision))
-    print("✅ Huggingface Hub repo downloaded")
-
-    config = load_chute_config_from_hf_repo(config_path=hf_repo_path / config_filename)
-    print("✅ Config file loaded")
-
-    image_config = config.get("Image", {})
-    image_config.update(
-        {
-            "username": chutes_username,
-            "name": chute_name,
-            "tag":"latest",
-            "readme": "README.md",
-        }
-    )
-    chute_image = safe_instantiate(cls=Image, config=image_config)
-    print("✅ Image instantiated")
-
-    machine_specs = safe_instantiate(
-        cls=NodeSelector, config=config.get("NodeSelector", {})
-    )
-    print("✅ NodeSelector instantiated")
-
-    chute_config = config.get("Chute", {})
-    chute_config.update(
-        {
-            "username": chutes_username,
-            "name": chute_name,
-            "image": chute_image,
-            "node_selector": machine_specs,
-            "allow_external_egress":False,
-            "readme": "README.md",
-        }
-    )
-    chute = safe_instantiate(cls=Chute, config=chute_config)
-    print("✅ Chute instantiated")
-    return chute
-
-
-def load_miner_from_hf_repo(path_hf_repo: Path, filename: str, classname: str):
-    path_hf_repo = path_hf_repo.resolve()
-    path_miner = path_hf_repo / filename
-    if not path_miner.is_file():
-        raise FileNotFoundError(
-            f"❌ Required file '{filename}' not in repo {path_hf_repo}"
-        )
-
-    WHITELIST = get_whitelisted_paths()
-
-    class HFRepoImportBlocker(PathFinder):
-        @classmethod
-        def find_spec(cls, fullname, path=None, target=None):
-            spec = super().find_spec(fullname, path, target)
-            if spec is None or spec.origin is None:
-                return spec
-            origin_path = Path(spec.origin).resolve()
-            if any(origin_path.is_relative_to(root) for root in WHITELIST):
-                return spec
-            raise ImportError(
-                f"❌ Import blocked: '{fullname}' (file: {origin_path}) is not in the whitelist. All code related to the miner MUST be inside {filename}"
-            )
-
-    try:
-        before_exec = set(sys.modules.keys())
-        sys.meta_path.insert(0, HFRepoImportBlocker)
-        spec = spec_from_file_location("miner", path_miner)
-        module = module_from_spec(spec)
-        sys.modules["miner"] = module
-        spec.loader.exec_module(module)
-        miner_object = getattr(module, classname)
-        miner = miner_object(path_hf_repo)
-        post_check_files_against_whitelist(before=before_exec, miner_path=path_miner)
-    except Exception as e:
-        raise e
-    finally:
-        sys.meta_path.remove(HFRepoImportBlocker)
-    return miner
-
-
-def object_size_in_bytes(current_obj: Any, seen_ids: set[int], limit: int) -> int:
-    obj_id = id(current_obj)
-    if obj_id in seen_ids or limit <= 0:
-        return 0
-    seen_ids.add(obj_id)
-    total_size = 0
-    if isinstance(current_obj, torch.Tensor):
-        return current_obj.numel() * current_obj.element_size()
-    if isinstance(current_obj, ndarray):
-        return current_obj.nbytes
-    try:
-        total_size += sys.getsizeof(current_obj)
-    except TypeError:
-        pass
-    if isinstance(current_obj, dict):
-        for key, value in current_obj.items():
-            total_size += object_size_in_bytes(key, seen_ids, limit - 1)
-            total_size += object_size_in_bytes(value, seen_ids, limit - 1)
-    elif isinstance(current_obj, (list, tuple, set)):
-        try:
-            for item in current_obj:
-                total_size += object_size_in_bytes(item, seen_ids, limit - 1)
-        except TypeError:
-            pass
-    elif hasattr(current_obj, "__dict__"):
-        for value in current_obj.__dict__.values():
-            total_size += object_size_in_bytes(value, seen_ids, limit - 1)
-    return total_size
-
-
-def get_miner_size_gb(miner) -> float:
-    total_size_bytes = object_size_in_bytes(
-        current_obj=miner, seen_ids=set(), limit=100
-    )
-    return total_size_bytes / (1024**3)
-
-
-def get_video_frames_in_batches(
-    video: VideoCapture, batch_size: int
-) -> Generator[list[ndarray], None, None]:
-    batch = []
-    while True:
-        ok, frame = video.read()
-        if not ok:
-            if batch:
-                yield batch
-            break
-
-        batch.append(frame)
-
-        if len(batch) >= batch_size:
-            yield batch
-            batch = []
-
-
-##=========ENDPOINTS================
-chute = load_chute(
-    hf_repo=HF_REPO_NAME,
-    hf_revision=HF_REPO_REVISION,
-    chutes_username=CHUTES_USERNAME,
-    chute_name=CHUTE_NAME,
-    config_filename=CHUTE_CONFIG_FILENAME,
-)
-
-
-@chute.on_startup()
-async def load_model(self) -> None:
-    try:
-        self.cuda_status = verify_cuda_version()
-    except Exception as e:
-        self.cuda_status = f"❌ Failed to check cuda status: {e}"
-    print(self.cuda_status)
-
-    self.miner_size = None
-    try:
-        hf_repo_path = Path(snapshot_download(HF_REPO_NAME, revision=HF_REPO_REVISION))
-        print("✅ Huggingface Hub repo downloaded")
-
-        self.miner = load_miner_from_hf_repo(
-            path_hf_repo=hf_repo_path, filename=FILENAME, classname=CLASSNAME
-        )
-        print(f"✅ Miner loaded {self.miner}")
-        self.miner_size = get_miner_size_gb(self.miner)
-        if self.miner_size > MAX_LOADED_MODEL_SIZE_GB:
-            raise ValueError(
-                f"Loaded Miner memory footprint {self.miner_size:.2f} GB exceeds the allowed limit of {MAX_LOADED_MODEL_SIZE_GB:.2f} GB. "
-            )
-        self.status = "healthy"
-    except Exception as e:
-        self.status = f"❌ Failed to load miner: {e}"
-        self.miner = None
-        print(self.status)
-
-
-@chute.cord(public_api_path="/health")
-async def health(self, *args, **kwargs) -> dict[str, Any]:
-    return {
-        "status": self.status,
-        "model_loaded": str(self.miner),
-        "memory_gb": self.miner_size,
-        "cuda": self.cuda_status,
-    }
-
-
-@chute.cord(
-    public_api_path="/predict",
-)
-async def predict(self, data: TVPredictInput) -> dict:
-    try:
-        if self.miner is None:
-            raise ValueError("Models were not properly loaded!")
-
-        metadata = data.meta
-        batch_size = metadata.get("batch_size", 64)
-        n_keypoints = metadata.get("n_keypoints", 32)
-
-        frame_results = []
-
-        if data.frames:
-            frame_entries = [(f.frame_id, f.url, f.data) for f in data.frames]
-            async with ClientSession() as session:
-                loaded_frames: list[tuple[int, ndarray]] = []
-                for frame_id, frame_url, frame_data in frame_entries:
-                    if frame_data:
-                        try:
-                            content = b64decode(frame_data)
-                        except Exception as e:
-                            raise ValueError(f"Failed to decode frame {frame_id}: {e}")
-                    elif frame_url:
-                        url = f"https://proxy.chutes.ai/misc/proxy?url={quote(frame_url, safe='')}"
-                        async with session.get(url) as response:
-                            response.raise_for_status()
-                            content = await response.read()
-                    else:
-                        raise ValueError(f"Frame {frame_id} missing url or data")
-
-                    arr = frombuffer(content, dtype=uint8)
-                    img = imdecode(arr, IMREAD_COLOR)
-                    if img is None:
-                        raise ValueError(f"Failed to decode frame {frame_id}")
-                    loaded_frames.append((frame_id, img))
-
-            for i in range(0, len(loaded_frames), batch_size):
-                batch = loaded_frames[i : i + batch_size]
-                batch_images = [img for _, img in batch]
-                batch_frame_ids = [fid for fid, _ in batch]
-                print(f"Predicting Batch: {i // batch_size + 1}")
-                batch_frame_results = self.miner.predict_batch(
-                    batch_images=batch_images,
-                    offset=0,
-                    n_keypoints=n_keypoints,
-                )
-                for frame_id, frame_result in zip(
-                    batch_frame_ids, batch_frame_results, strict=True
-                ):
-                    fr = frame_result.model_dump()
-                    fr["frame_id"] = frame_id
-                    frame_results.append(fr)
-        else:
-            if not data.url:
-                raise ValueError("Missing both video url and frames payload")
-
-            url = f"https://proxy.chutes.ai/misc/proxy?url={quote(data.url, safe='')}"
-            async with ClientSession() as session:
-                async with session.get(url) as response:
-                    response.raise_for_status()
-                    content = await response.read()
-                    print("✅ Challenge video downloaded. Saving video to temporary file.")
-
-                    with NamedTemporaryFile(prefix="sv_video_", suffix=".mp4") as f:
-                        f.write(content)
-                        cap = VideoCapture(f.name)
-                        if not cap.isOpened():
-                            raise ValueError(
-                                f"Problem accessing downloaded video {f.name}"
-                            )
-
-                        n_frames = int(cap.get(CAP_PROP_FRAME_COUNT))
-                        print(
-                            f"Processing video with {n_frames} frames in batches of {batch_size}"
-                        )
-
-                        for batch_number, images in enumerate(
-                            get_video_frames_in_batches(
-                                video=cap, batch_size=batch_size
-                            )
-                        ):
-                            frame_number = batch_size * batch_number
-                            print(f"Predicting Batch: {batch_number + 1}")
-                            batch_frame_results = self.miner.predict_batch(
-                                batch_images=images,
-                                offset=frame_number,
-                                n_keypoints=n_keypoints,
-                            )
-                            frame_results.extend(
-                                [
-                                    frame_result.model_dump()
-                                    for frame_result in batch_frame_results
-                                ]
-                            )
-
-                        cap.release()
-                        print("✅ Frame Predictions Completed")
-
-        result = TVPredictOutput(success=True, predictions={"frames": frame_results})
-
-    except Exception as e:
-        result = TVPredictOutput(
-            success=False,
-            error=f"❌ There was a problem during prediction: {e}",
-        )
-    return result.model_dump(mode="json")

pyproject.toml

@@ -5,13 +5,14 @@ requires-python = ">=3.9"
 
 dependencies = [
   "numpy>=1.23",
-  "onnxruntime[cuda,cudnn]>=1.16",
+  "onnxruntime>=1.16",
   "opencv-python>=4.7",
   "pillow>=9.5",
   "huggingface_hub>=0.19.4",
   "pydantic>=2.0",
   "pyyaml>=6.0",
   "aiohttp>=3.9",
-  "torch",
-  "torchvision",
+  "torch==2.8.0",
+  "torchvision==0.23.0",
+  "torchaudio==2.8.0",
 ]

readme.md

@@ -1,18 +0,0 @@
----
-tags:
-- element_type:detect
-- model:ultralytics-yolo
-- subnet:winner
-- object:fire
-- object:smoke
-- object:fire extinguisher
-
-manako:
-  source: winner_fetch
-  manifest_element_name: manak0/Detect-fire
-  winner_repo_id: SuperBitDev/fire4
-  winner_revision: 0679fa90aae80dacbcaa1235755c2791da2f7cc0
-  note: E=0.03082304 (map50=0.600000, size_mb=19.465956)
----
-
-## YOLO26 ONNX detector

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f4414accb1ff7730dec4de9099e2aeab3d7301216a14515c32068b628738ae2c
-size 19407281
+oid sha256:1f9c13ee4403ddbbccd9d9707a151b1474cbb33124a2ef2965fa57d1e821ed03
+size 19287011