subnet_bridge: copy winning miner repo into library

README.md

@@ -10,40 +10,9 @@ tags:
 manako:
   source: winner_fetch
   manifest_element_name: manak0/Detect-fire
-  winner_repo_id: meaculpitt/ScoreVision-Fire
-  winner_revision: 71ae3d3e59ced8b330eea5e95710318175bb1342
-  note: E=0.11785877 (map50=0.600000, size_mb=5.090839)
+  winner_repo_id: navierstocks/fire-light
+  winner_revision: 95133792375f1fd3e5f192d0494c3b02f770cdc4
+  note: E=0.03088120 (map50=0.600000, size_mb=19.429295)
 ---
 
-# ScoreVision-Fire — meaculpitt v2.1
-
-SN44 fire-detection miner for the `manak0/Detect-fire` element.
-
-## Pipeline
-- **Architecture**: yolo26n
-- **Resolution**: 1408×768 input → letterbox → 960×960
-- **Preprocessing**: `cv2.dnn.blobFromImage` (fused C++ resize+normalize+transpose)
-- **Inference**: single-pass FP16 ONNX, NMS baked in
-- **Output shape**: `[1, 300, 6]` (xyxy, conf, cls)
-- **Latency**: ~35 ms p95 on RTX 4090 (fits the 50 ms gate)
-
-## Classes (validator GT order, NOT the published class_names.txt order)
-- 0: fire
-- 1: smoke
-- 2: fire extinguisher
-
-Verified by audit of alfred8995/fire001 (scores 1.00) and navierstocks/fire
-(scores 0.96): both use [fire, smoke, fire_extinguisher] and the validator's
-GT order matches. Our model was trained with [fire, fire_ext, smoke]; miner.py
-applies cls_remap=[0,2,1] to translate model output to validator index.
-
-## Training
-- 22,796 training images (validator-synth + Simuletic + D-Fire + z5atr, SHA1 deduped)
-- 2,532 validation images (random 90/10 split, seed=42)
-- 100 epochs, yolo26n, imgsz=960, batch=8, AdamW lr0=0.001 cos_lr
-- CCTV augmentation chain (cctv_aug_patch)
-
-## Benchmarks
-- Broader merged val mAP50: 0.785
-- Validator-distribution synth val mAP50: 0.640 (+24.7 pts above 0.393 baseline)
-- Per-class on synth val: fire=0.523, fire_extinguisher=0.647, smoke=0.749
+## YOLO26 ONNX detector

chute_config.yml

@@ -8,22 +8,14 @@ Image:
 NodeSelector:
   gpu_count: 1
   min_vram_gb_per_gpu: 16
-  # SN44 chute platform mandates TEE + pro_6000 include for new elements
-  # (verified by crime + beverage deploys 2026-05-04). Cheaper-GPU config
-  # caused repeated 500 ContentTypeError on POST /chutes/.
-  max_hourly_price_per_gpu: 2.00
+  max_hourly_price_per_gpu: 2
   include:
-    - "pro_6000"
-  exclude:
-    - "5090"
-    - b200
-    - h200
-    - h20
-    - mi300x
+    - pro_6000
 
 Chute:
+  timeout_seconds: 900
   concurrency: 4
   max_instances: 5
   scaling_threshold: 0.5
-  shutdown_after_seconds: 288000   # 80h idle
+  shutdown_after_seconds: 288000
   tee: true

class_names.txt

@@ -1,3 +0,0 @@
-fire
-smoke
-fire extinguisher

miner.py

@@ -1,24 +1,10 @@
-# build-marker: fire-v2-blob-imgsz960
-"""SN44 fire detection miner — yolo26n single-pass @ imgsz=960.
-
-v2 (2026-05-09): trained on merged 25k pool (validator-synth + D-Fire +
-Simuletic + z5atr). FP16 ONNX, ~5 MB. Single forward pass at imgsz=960
-fits the 50 ms p95 latency gate (~35 ms on 4090, blobFromImage preproc).
-
-SAHI tiling was tested but blew the latency budget (5x preproc/postproc
-overhead). Code preserved at fire/deploy/miner_sahi.py for later experiments.
-
-Classes (validator order from manak0/Detect-fire class_names.txt):
-  0=fire, 1=fire extinguisher, 2=smoke
-
-Single ONNX expected at path_hf_repo/weights.onnx (yolo26n e2e [1,300,6]).
-"""
-import math
 from pathlib import Path
+import math
 
 import cv2
 import numpy as np
 import onnxruntime as ort
+from numpy import ndarray
 from pydantic import BaseModel
 
 
@@ -38,144 +24,192 @@ class TVFrameResult(BaseModel):
 
 
 class Miner:
-    def __init__(self, path_hf_repo) -> None:
-        self.path_hf_repo = Path(path_hf_repo)
-        # Validator's actual GT class order is [fire, smoke, fire extinguisher]
-        # — verified by audit of alfred8995/fire001 (scores 1.00) and
-        # navierstocks/fire (scores 0.96), both using this order. The published
-        # manak0/Detect-fire class_names.txt list [fire, fire_ext, smoke] does
-        # NOT match the actual scoring index.
-        # Our model was trained with [fire, fire_ext, smoke] (cls=1=ext, cls=2=smoke).
-        # cls_remap translates model output index → validator GT index.
-        self.class_names = ["fire", "smoke", "fire extinguisher"]
-        model_class_order = ["fire", "fire extinguisher", "smoke"]
-        self.cls_remap = np.array(
-            [self.class_names.index(n) for n in model_class_order],
-            dtype=np.int32,
-        )  # → [0, 2, 1]: model cls 0→0, 1→2, 2→1
+    """ONNX Runtime miner. Soft per-class NMS + sanity filter + flip TTA."""
+
+    class_names = ["fire", "smoke", "fire extinguisher"]
+    input_size = 1280
+    iou_thres = 0.3
+    soft_sigma = 0.5
+    min_side = 8.0
+    min_box_area = 144.0
+    max_aspect_ratio = 6.0
+    max_det = 100
+    _conf_thres_array = np.array([0.25, 0.15, 0.15], dtype=np.float32)
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        model_path = path_hf_repo / "weights.onnx"
+        print("ORT version:", ort.__version__)
 
         try:
             ort.preload_dlls()
-        except Exception:
-            pass
+            print("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(self.path_hf_repo / "weights.onnx"),
+                str(model_path),
                 sess_options=sess_options,
                 providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
             )
-        except Exception:
+            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(self.path_hf_repo / "weights.onnx"),
+                str(model_path),
                 sess_options=sess_options,
                 providers=["CPUExecutionProvider"],
             )
+
+        print("ORT session providers:", self.session.get_providers())
+
+        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 = [o.name for o in self.session.get_outputs()]
-        self.input_dtype = (np.float16
-                            if 'float16' in self.session.get_inputs()[0].type
-                            else np.float32)
-
-        self.input_h = 960
-        self.input_w = 960
-        self.conf_thres_per_class = np.array([0.20, 0.20, 0.20], dtype=np.float32)
-        self.iou_thresh = 0.5
-        self.cross_iou_thresh = 0.7
-        self.max_det = 100
-        self.min_box_area = 64
-        self.min_side = 6
-        self.max_aspect_ratio = 10.0
-
-        warm = np.zeros((768, 1408, 3), dtype=np.uint8)
-        for _ in range(3):
-            try: self._infer_single(warm)
-            except Exception: break
-
-    def __repr__(self):
-        thr = ",".join(f"{n[:4]}={t:.2f}" for n, t
-                       in zip(self.class_names, self.conf_thres_per_class.tolist()))
-        return (f"FireMiner v2  yolo26n@{self.input_w} single-pass blob  "
-                f"conf=[{thr}]  iou={self.iou_thresh}")
-
-    def _preprocess(self, image_bgr):
-        """Letterbox + cv2.dnn.blobFromImage (fused C++ resize/normalize/transpose)."""
-        h, w = image_bgr.shape[:2]
-        ratio = min(self.input_w / w, self.input_h / h)
-        nw, nh = int(round(w * ratio)), int(round(h * ratio))
-        if (nw, nh) != (w, h):
-            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            resized = cv2.resize(image_bgr, (nw, nh), interpolation=interp)
-        else:
-            resized = image_bgr
-        canvas = np.full((self.input_h, self.input_w, 3), 114, dtype=np.uint8)
-        dy = (self.input_h - nh) // 2
-        dx = (self.input_w - nw) // 2
-        canvas[dy:dy+nh, dx:dx+nw] = resized
-        # blobFromImage: fused BGR→RGB (swapRB) + /255 + transpose CHW + add batch dim
-        blob = cv2.dnn.blobFromImage(
-            canvas, scalefactor=1/255.0,
-            size=(self.input_w, self.input_h),
-            mean=(0, 0, 0), swapRB=True, crop=False,
+        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=self.input_size)
+        self.input_width = self._safe_dim(self.input_shape[3], default=self.input_size)
+
+        print(f"ONNX model loaded from: {model_path}")
+        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()})"
         )
-        if self.input_dtype == np.float16:
-            blob = blob.astype(np.float16)
-        return blob, ratio, (float(dx), float(dy))
-
-    def _infer_single(self, image_bgr):
-        inp, ratio, (dx, dy) = self._preprocess(image_bgr)
-        out = self.session.run(self.output_names, {self.input_name: inp})[0]
-        if out.ndim == 3: out = out[0]
-        confs_all = out[:, 4].astype(np.float32)
-        cls_all = self.cls_remap[out[:, 5].astype(np.int32)]
-        cls_idx = np.clip(cls_all, 0, len(self.conf_thres_per_class) - 1)
-        keep = confs_all >= self.conf_thres_per_class[cls_idx]
-        if not keep.any(): return []
-        out = out[keep]
-        boxes = out[:, :4].astype(np.float32).copy()
-        confs = out[:, 4].astype(np.float32)
-        cls_ids = self.cls_remap[out[:, 5].astype(np.int32)]
-        boxes[:, [0, 2]] = (boxes[:, [0, 2]] - dx) / ratio
-        boxes[:, [1, 3]] = (boxes[:, [1, 3]] - dy) / ratio
-        oh, ow = image_bgr.shape[:2]
-        boxes[:, [0, 2]] = np.clip(boxes[:, [0, 2]], 0, ow - 1)
-        boxes[:, [1, 3]] = np.clip(boxes[:, [1, 3]], 0, oh - 1)
-        if len(boxes) > 1:
-            keep_idx = self._per_class_hard_nms(boxes, confs, cls_ids, self.iou_thresh)
-            keep_idx = keep_idx[: self.max_det]
-            boxes, confs, cls_ids = boxes[keep_idx], confs[keep_idx], cls_ids[keep_idx]
-            boxes, confs, cls_ids = self._cross_class_dedup(
-                boxes, confs, cls_ids, self.cross_iou_thresh)
-        return self._to_boundingboxes(boxes, confs, cls_ids, ow, oh)
 
     @staticmethod
-    def _hard_nms(boxes, scores, iou_thresh):
+    def _safe_dim(value, default: int) -> int:
+        return value if isinstance(value, int) and value > 0 else default
+
+    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)[::-1]
-        keep, suppressed = [], np.zeros(n, dtype=bool)
-        for i in range(n):
-            idx = order[i]
-            if suppressed[idx]: continue
-            keep.append(int(idx))
-            bi = boxes[idx]
-            for k in range(i + 1, n):
-                jdx = order[k]
-                if suppressed[jdx]: continue
-                bj = boxes[jdx]
-                xx1, yy1 = max(bi[0], bj[0]), max(bi[1], bj[1])
-                xx2, yy2 = min(bi[2], bj[2]), min(bi[3], bj[3])
-                inter = max(0.0, xx2-xx1) * max(0.0, yy2-yy1)
-                ai = (bi[2]-bi[0])*(bi[3]-bi[1]); aj = (bj[2]-bj[0])*(bj[3]-bj[1])
-                iou = inter / (ai + aj - inter + 1e-7)
-                if iou > iou_thresh: suppressed[jdx] = True
+        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, scores, cls_ids, iou_thresh):
-        if len(boxes) == 0: return np.array([], dtype=np.intp)
-        all_keep = []
+    @staticmethod
+    def _soft_nms(boxes: np.ndarray, scores: np.ndarray,
+                  sigma: float, score_thresh: float = 0.001
+                  ) -> tuple[np.ndarray, np.ndarray]:
+        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)
+            a_i = max(0.0, float((boxes[i, 2] - boxes[i, 0]) *
+                                 (boxes[i, 3] - boxes[i, 1])))
+            a_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 / (a_i + a_j - inter + 1e-7)
+            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
+        mask = scores > score_thresh
+        return order[mask], scores[mask]
+
+    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]
@@ -184,52 +218,271 @@ class Miner:
         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
+                            ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+        out_b: list = []
+        out_s: list = []
+        out_c: list = []
+        for c in np.unique(cls_ids):
+            mask = cls_ids == c
+            sub_b = boxes[mask]
+            sub_s = scores[mask]
+            sub_c = cls_ids[mask]
+            idx, decayed = self._soft_nms(sub_b, sub_s, self.soft_sigma)
+            if len(idx) == 0:
+                continue
+            out_b.append(sub_b[idx])
+            out_s.append(decayed)
+            out_c.append(sub_c[idx])
+        if not out_b:
+            return (np.empty((0, 4), dtype=np.float32),
+                    np.empty((0,), dtype=np.float32),
+                    np.empty((0,), dtype=cls_ids.dtype))
+        return (np.concatenate(out_b, axis=0),
+                np.concatenate(out_s, axis=0),
+                np.concatenate(out_c, axis=0))
+
+    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]:
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+        orig_w, orig_h = orig_size
+        image_area = float(orig_w * orig_h)
+        bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0])
+        bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
+        area = bw * bh
+        ar = np.where(
+            (bw > 0) & (bh > 0),
+            np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6)),
+            np.inf,
+        )
+        keep = (
+            (bw >= self.min_side) & (bh >= self.min_side) &
+            (area >= self.min_box_area) &
+            (area <= 0.95 * image_area) &
+            (ar <= self.max_aspect_ratio)
+        )
+        return boxes[keep], scores[keep], cls_ids[keep]
+
     @staticmethod
-    def _cross_class_dedup(boxes, scores, cls_ids, iou_thresh):
-        n = len(boxes)
-        if n <= 1: return boxes, scores, cls_ids
-        areas = np.maximum(0.0, boxes[:, 2]-boxes[:, 0]) * np.maximum(0.0, boxes[:, 3]-boxes[:, 1])
-        order = np.lexsort((-scores, -areas))
-        suppressed = np.zeros(n, dtype=bool); keep = []
-        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)
-            ai = max(1e-7, float((bi[2]-bi[0])*(bi[3]-bi[1])))
-            iou = inter / (ai + areas - inter + 1e-7)
-            dup = iou > iou_thresh; dup[i] = False
-            suppressed |= dup
-        kept = np.array(keep, dtype=np.intp)
-        return boxes[kept], scores[kept], cls_ids[kept]
-
-    def _to_boundingboxes(self, boxes, confs, cls_ids, orig_w, orig_h):
-        out = []
-        for i in range(len(boxes)):
-            x1, y1, x2, y2 = boxes[i]
-            ix1 = max(0, min(orig_w, math.floor(x1)))
-            iy1 = max(0, min(orig_h, math.floor(y1)))
-            ix2 = max(0, min(orig_w, math.ceil(x2)))
-            iy2 = max(0, min(orig_h, math.ceil(y2)))
-            if ix2 <= ix1 or iy2 <= iy1: continue
-            bw, bh = ix2 - ix1, iy2 - iy1
-            if bw * bh < self.min_box_area: continue
-            if min(bw, bh) < self.min_side: continue
-            ar = max(bw / max(bh, 1), bh / max(bw, 1))
-            if ar > self.max_aspect_ratio: continue
-            out.append(BoundingBox(x1=ix1, y1=iy1, x2=ix2, y2=iy2, cls_id=int(cls_ids[i]),
-                                   conf=max(0.0, min(1.0, float(confs[i])))))
+    def _max_score_per_cluster(post_boxes: np.ndarray,
+                               full_boxes: np.ndarray,
+                               full_scores: np.ndarray,
+                               iou_thresh: float) -> np.ndarray:
+        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
+            out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
         return out
 
-    def predict_batch(self, batch_images, offset, n_keypoints):
-        results = []
-        for idx, image in enumerate(batch_images):
-            boxes = self._infer_single(image)
-            results.append(TVFrameResult(
-                frame_id=offset + idx,
-                boxes=boxes,
-                keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
-            ))
+    def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray,
+                           cls_ids: np.ndarray, orig_size: tuple[int, int]
+                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        boxes, scores, cls_ids = self._filter_sane_boxes(
+            boxes, scores, cls_ids, orig_size
+        )
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+        if len(boxes) > 1:
+            boxes, scores, cls_ids = self._per_class_soft_nms(boxes, scores, cls_ids)
+        if len(scores) > self.max_det:
+            top = np.argsort(-scores)[: self.max_det]
+            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
+        return boxes, scores, cls_ids
+
+    def _decode_final_dets(self, preds: np.ndarray, ratio: float,
+                           pad: tuple[float, float],
+                           orig_size: tuple[int, int]) -> list[BoundingBox]:
+        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)
+
+        keep = scores >= self._conf_thres_array[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._per_view_pipeline(
+            boxes, scores, cls_ids, orig_size
+        )
+        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]:
+        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]
+
+        keep = scores >= self._conf_thres_array[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._per_view_pipeline(
+            boxes, scores, cls_ids, orig_size
+        )
+        return self._build_results(boxes, scores, cls_ids)
+
+    @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 _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[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]:
+        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)
+
+        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
+        if len(hard_keep) == 0:
+            return []
+        hard_keep = hard_keep[: self.max_det]
+        boosted = self._max_score_per_cluster(
+            coords[hard_keep], coords, scores, self.iou_thres
+        )
+
+        return [
+            BoundingBox(
+                x1=all_boxes[i].x1,
+                y1=all_boxes[i].y1,
+                x2=all_boxes[i].x2,
+                y2=all_boxes[i].y2,
+                cls_id=all_boxes[i].cls_id,
+                conf=float(boosted[j]),
+            )
+            for j, i in enumerate(hard_keep)
+        ]
+
+    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:
+                boxes = self._predict_tta(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

model_type.json

@@ -1,4 +0,0 @@
-{
-  "task_type": "object-detection",
-  "model_type": "yolov26-nano"
-}

readme.md

@@ -0,0 +1,18 @@
+---
+tags:
+- element_type:detect
+- model:onnxruntime
+- subnet:winner
+- object:fire
+- object:smoke
+- object:fire extinguisher
+
+manako:
+  source: winner_fetch
+  manifest_element_name: manak0/Detect-fire
+  winner_repo_id: navierstocks/fire-light
+  winner_revision: 95133792375f1fd3e5f192d0494c3b02f770cdc4
+  note: E=0.03088120 (map50=0.600000, size_mb=19.429295)
+---
+
+## YOLO26 ONNX detector

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0bfd3fd0b1dca617b05f93fb1ce92aadc8f6ee8e80255c2eb0818b143b4056d6
-size 5077018
+oid sha256:40ec65251e308d8240c59ea7704956fc44823e750067e433f287aec71e8939ac
+size 19407447