numberplate: quad-4 inference + v3 epoch-19 FP16 weights. Standalone repo for 30MB compliance.

chute_config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu>=1.16' 'nvidia-cudnn-cu12>=9.0' 'nvidia-cublas-cu12>=12.8' 'nvidia-cuda-runtime-cu12>=12.8' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9' 'torch>=2.8'
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 16
+
+Chute:
+  timeout_seconds: 300
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after: 288000

class_names.txt

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+numberplate

miner.py

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+"""
+SN44 number plate detection miner — single-element chute for
+manak0/Detect-number-plates-1-0.
+
+Adapted from the auto-generated detect-person-reference miner with four
+substantive changes:
+
+1. Class set is the single class ``numberplate`` (the validator's exact
+   label string).
+2. Lower confidence threshold (0.15 vs 0.25) because the validator's
+   plates are tiny — 5–92 px wide on a 1408 px frame, median ~30 px.
+   At standard 0.25 most true positives get filtered before NMS.
+3. Standard NMS replaced with Gaussian Soft-NMS (sigma=0.5). Soft-NMS
+   decays scores of overlapping boxes instead of suppressing them
+   outright, which helps on plate-dense frames (parking lot, car
+   carrier, gas station forecourt) where standard NMS over-suppresses
+   adjacent plates.
+4. CUDA library preload at import time so onnxruntime-gpu finds
+   libcudnn / libcublas from the nvidia-* pip wheels even when
+   LD_LIBRARY_PATH is not set (the chute container ships these wheels
+   but does not export them).
+
+Soft-NMS is inlined here rather than imported from /home/miner/utils
+because the chute platform sandbox restricts non-stdlib imports beyond
+the deps declared in chute_config.yml. The implementation is a
+specialised single-class version of soft_nms_yolo from
+/home/miner/utils/soft_nms.py — see that file for the full
+multi-class / multi-backend version.
+"""
+import ctypes
+import glob as _glob
+import logging as _logging
+import os
+
+_cuda_log = _logging.getLogger(__name__)
+
+
+def _preload_cuda_libs() -> None:
+    """Pre-load CUDA + cuDNN + cuBLAS shared libs from nvidia-* pip wheels.
+
+    Without this, onnxruntime-gpu's CUDAExecutionProvider silently falls
+    back to CPU because it can't dlopen libcudnn.so.9 — the nvidia
+    wheels ship the library inside `nvidia/cudnn/lib/` but do NOT add
+    that directory to the loader path. We import the wheel modules to
+    locate their lib dirs, prepend them to LD_LIBRARY_PATH for any
+    child processes, and ctypes.CDLL the .so files with RTLD_GLOBAL so
+    onnxruntime's dlopen sees them.
+    """
+    try:
+        lib_dirs: list[str] = []
+        for mod_name in (
+            "nvidia.cudnn",
+            "nvidia.cublas",
+            "nvidia.cuda_runtime",
+            "nvidia.cufft",
+            "nvidia.curand",
+            "nvidia.cusolver",
+            "nvidia.cusparse",
+            "nvidia.nvjitlink",
+        ):
+            try:
+                mod = __import__(mod_name, fromlist=["__file__"])
+                lib_dir = os.path.join(os.path.dirname(mod.__file__), "lib")
+                if os.path.isdir(lib_dir) and lib_dir not in lib_dirs:
+                    lib_dirs.append(lib_dir)
+            except ImportError:
+                pass
+
+        if not lib_dirs:
+            _cuda_log.warning("no nvidia-* lib dirs found; ORT GPU may fall back to CPU")
+            return
+
+        # Update LD_LIBRARY_PATH for any child processes / dlopen fallbacks
+        existing = os.environ.get("LD_LIBRARY_PATH", "")
+        os.environ["LD_LIBRARY_PATH"] = ":".join(
+            lib_dirs + ([existing] if existing else [])
+        )
+
+        # ctypes.CDLL each .so so the symbols are globally visible to ORT
+        for lib_dir in lib_dirs:
+            for so in sorted(_glob.glob(os.path.join(lib_dir, "lib*.so*"))):
+                try:
+                    ctypes.CDLL(so, mode=ctypes.RTLD_GLOBAL)
+                except OSError:
+                    pass
+    except Exception as e:  # pragma: no cover - best effort
+        _cuda_log.warning("CUDA preload failed: %s", e)
+
+
+_preload_cuda_libs()
+
+
+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:
+    """
+    Single-element ONNX miner for the manak0/Detect-number-plates-1-0
+    element. Auto-loaded by the chute platform; the platform passes the
+    snapshot path of the HF repo containing weights.onnx as
+    ``path_hf_repo`` and calls ``predict_batch(batch_images, offset,
+    n_keypoints)`` for each request.
+    """
+
+    def __init__(self, path_hf_repo) -> None:
+        self.path_hf_repo = Path(path_hf_repo)
+        self.class_names = ['numberplate']
+        self.session = ort.InferenceSession(
+            str(self.path_hf_repo / "numberplate_weights.onnx"),
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+        )
+        self.input_name = self.session.get_inputs()[0].name
+        input_shape = self.session.get_inputs()[0].shape
+        # expected [N, C, H, W]; dynamic-export ONNX has string placeholders
+        # for spatial dims. We always run inference at 1408 (the validator's
+        # native frame width); the ONNX accepts variable shapes via dynamic
+        # axes, and inference at 1408 gives substantially better small-plate
+        # recall than the model's training resolution (verified on the 7
+        # starter assets: 43% recall at 960 vs 60% at 1408).
+        def _maybe_int(d, default):
+            try:
+                return int(d)
+            except (TypeError, ValueError):
+                return default
+        # Hard-pin to the validator's native 1408x768 (rectangular). This
+        # is half the pixel count of a 1408x1408 square pad and matches
+        # the validator's exact frame shape, eliminating wasted padding
+        # rows. yolo11s strides are 32, both 1408 (44*32) and 768 (24*32)
+        # are valid.
+        self.input_h = 768
+        self.input_w = 1408
+        # Record what the ONNX *declared*, for diagnostic logging only
+        self._onnx_declared_h = _maybe_int(input_shape[2], None)
+        self._onnx_declared_w = _maybe_int(input_shape[3], None)
+
+        # Pre-NMS confidence threshold. The reference uses 0.25; we lower
+        # slightly because validator plates are tiny but not as far as 0.15
+        # which produces too many decayed-score ghost detections at 1408
+        # input resolution (verified on starter assets: F1 dropped from
+        # 0.625 to 0.462 at conf=0.15).
+        self.conf_threshold = 0.25
+        # Soft-NMS hyperparameters (Gaussian variant).
+        self.soft_nms_sigma = 0.5
+        # Final score floor after Soft-NMS decay. At higher input resolution
+        # the model produces more medium-confidence detections that survive
+        # decay; we keep this stricter so they don't pollute the output.
+        self.score_threshold = 0.20
+
+        # GPU warmup — force ORT / CUDA / cuDNN kernel compilation and pull
+        # the 4090 out of low-power idle state so the first real validator
+        # frame doesn't pay a ~20 ms DVFS spin-up tax. SCOREVISION_WARMUP_CALLS
+        # at the chute level defaults to 3, which is not enough to reach
+        # steady-state on this tiled inference path (measured: 3 calls -> 52
+        # ms p95 on the first few frames vs 31 ms steady). 10 full pipeline
+        # runs on a synthetic frame gets us to the fast regime before the
+        # platform warmup even starts.
+        _warmup_frame = np.zeros((self.input_h, self.input_w, 3), dtype=np.uint8)
+        for _ in range(10):
+            try:
+                self._infer_single(_warmup_frame)
+            except Exception:  # pragma: no cover - best effort
+                break
+
+    def __repr__(self) -> str:
+        return (
+            f"NumberplateMiner session={type(self.session).__name__} "
+            f"input={self.input_h}x{self.input_w} classes={len(self.class_names)}"
+        )
+
+    # ---------------------------------------------------------------- preproc
+    def _preprocess(self, image_bgr: ndarray):
+        """Letterbox the BGR image to (input_h, input_w), preserving aspect.
+
+        Returns the float32 NCHW tensor plus the metadata needed to undo
+        the letterbox during decode: (orig_h, orig_w, scale, dx, dy).
+        """
+        h, w = image_bgr.shape[:2]
+        scale = min(self.input_h / h, self.input_w / w)
+        nh, nw = int(round(h * scale)), int(round(w * scale))
+        resized = cv2.resize(image_bgr, (nw, nh))
+        # Pad to (input_h, input_w) with grey (114) - ultralytics default
+        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
+        rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
+        x = rgb.astype(np.float32) / 255.0
+        x = np.transpose(x, (2, 0, 1))[None, ...]
+        return x, (h, w, scale, dx, dy)
+
+    # ---------------------------------------------------------------- decode
+    def _normalize_predictions(self, raw: np.ndarray) -> np.ndarray:
+        """Handle both common ultralytics export shapes ([1,C,N] and [1,N,C])."""
+        pred = raw[0]
+        if pred.ndim != 2:
+            raise ValueError(f"Unexpected prediction shape: {raw.shape}")
+        if pred.shape[0] < pred.shape[1]:
+            pred = pred.transpose(1, 0)
+        return pred
+
+    # ---------------------------------------------------------------- soft NMS
+    def _soft_nms(
+        self,
+        dets: list[tuple[float, float, float, float, float, int]],
+    ) -> list[tuple[float, float, float, float, float, int]]:
+        """Gaussian Soft-NMS for a single class.
+
+        Decays each remaining box's score by ``exp(-iou^2 / sigma)`` against
+        the highest-scoring picked box, then drops anything below
+        ``self.score_threshold``. Returns detections in descending decayed
+        score order.
+        """
+        if not dets:
+            return []
+
+        boxes = np.asarray([[d[0], d[1], d[2], d[3]] for d in dets], dtype=np.float32)
+        scores = np.asarray([d[4] for d in dets], dtype=np.float32)
+        cls_ids = [int(d[5]) for d in dets]
+        n = len(dets)
+
+        keep_idx: list[int] = []
+        keep_scores: list[float] = []
+        active = np.ones(n, dtype=bool)
+
+        while True:
+            valid_mask = active & (scores >= self.score_threshold)
+            if not valid_mask.any():
+                break
+            valid_idx = np.where(valid_mask)[0]
+            m_local = valid_idx[int(np.argmax(scores[valid_idx]))]
+
+            keep_idx.append(int(m_local))
+            keep_scores.append(float(scores[m_local]))
+            active[m_local] = False
+
+            # IoU of m_local against all still-active boxes
+            others = np.where(active)[0]
+            if others.size == 0:
+                break
+            ax1 = np.maximum(boxes[m_local, 0], boxes[others, 0])
+            ay1 = np.maximum(boxes[m_local, 1], boxes[others, 1])
+            ax2 = np.minimum(boxes[m_local, 2], boxes[others, 2])
+            ay2 = np.minimum(boxes[m_local, 3], boxes[others, 3])
+            inter_w = np.clip(ax2 - ax1, a_min=0.0, a_max=None)
+            inter_h = np.clip(ay2 - ay1, a_min=0.0, a_max=None)
+            inter = inter_w * inter_h
+            area_m = max(0.0, (boxes[m_local, 2] - boxes[m_local, 0])) * \
+                     max(0.0, (boxes[m_local, 3] - boxes[m_local, 1]))
+            area_o = (
+                np.clip(boxes[others, 2] - boxes[others, 0], a_min=0.0, a_max=None) *
+                np.clip(boxes[others, 3] - boxes[others, 1], a_min=0.0, a_max=None)
+            )
+            union = area_m + area_o - inter
+            iou = np.where(union > 0.0, inter / union, 0.0)
+
+            decay = np.exp(-(iou * iou) / self.soft_nms_sigma)
+            scores[others] = scores[others] * decay
+
+        return [
+            (
+                float(boxes[i, 0]),
+                float(boxes[i, 1]),
+                float(boxes[i, 2]),
+                float(boxes[i, 3]),
+                float(s),
+                cls_ids[i],
+            )
+            for i, s in zip(keep_idx, keep_scores)
+        ]
+
+    # ---------------------------------------------------------------- inference
+    def _infer_tile(
+        self,
+        image_bgr: ndarray,
+        x0: int,
+        y0: int,
+        x1: int,
+        y1: int,
+    ) -> list[tuple[float, float, float, float, float, int]]:
+        """Run one inference pass on ``image_bgr[y0:y1, x0:x1]`` resized
+        anisotropically to ``(input_h, input_w)`` and return raw detections
+        (pre-Soft-NMS) mapped back to ORIGINAL-image coordinates.
+
+        Anisotropic resize is intentional: the tile aspect ratio differs
+        from the model input, and we want the tile pixels to magnify up to
+        the detector's stride-8 feature footprint. For the 1408x422
+        top/bottom tiles used by ``_infer_single`` this yields ~1.82x
+        vertical magnification (and 1.0x horizontal), which is what pushes
+        tiny-height plates (5-12 px on the validator's starter frames)
+        above the stride-8 threshold.
+        """
+        crop = image_bgr[y0:y1, x0:x1]
+        ch, cw = crop.shape[:2]
+        if ch == 0 or cw == 0:
+            return []
+        resized = cv2.resize(crop, (self.input_w, self.input_h))
+        rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
+        x = np.transpose(rgb.astype(np.float32) / 255.0, (2, 0, 1))[None, ...]
+        out = self.session.run(None, {self.input_name: x})[0]
+        pred = self._normalize_predictions(out)
+
+        if pred.shape[1] < 5:
+            return []
+
+        boxes_m = pred[:, :4]
+        cls_scores = pred[:, 4:]
+        if cls_scores.shape[1] == 0:
+            return []
+
+        cls_ids = np.argmax(cls_scores, axis=1)
+        confs = np.max(cls_scores, axis=1)
+        keep = confs >= self.conf_threshold
+        boxes_m = boxes_m[keep]
+        confs = confs[keep]
+        cls_ids = cls_ids[keep]
+        if boxes_m.shape[0] == 0:
+            return []
+
+        # Model-space (input_w x input_h) -> crop-space -> original image
+        sx = cw / self.input_w
+        sy = ch / self.input_h
+        dets: list[tuple[float, float, float, float, float, int]] = []
+        for i in range(boxes_m.shape[0]):
+            cx, cy, bw, bh = boxes_m[i].tolist()
+            xa = (cx - bw / 2.0) * sx + x0
+            ya = (cy - bh / 2.0) * sy + y0
+            xb = (cx + bw / 2.0) * sx + x0
+            yb = (cy + bh / 2.0) * sy + y0
+            dets.append((xa, ya, xb, yb, float(confs[i]), int(cls_ids[i])))
+        return dets
+
+    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
+        """Quad-4 (2x2 quadrant) SAHI inference.
+
+        Splits the frame into four overlapping quadrants, each
+        anisotropically resized to ``(input_h, input_w)`` for ~2x
+        magnification in both axes. This recovers plates that TB-2
+        (top/bottom only) missed — especially the 5-7 px plates in
+        image 6 that need vertical AND horizontal magnification.
+
+        Overlap is ~10% on each axis to avoid seam misses. All tile
+        detections are merged via Soft-NMS.
+
+        Measured on the 7 starter frames vs TB-2:
+            mAP@50    0.406 -> 0.489
+            recall    0.433 -> 0.500
+            wall p95   55 ms -> 98 ms (budget 10 s)
+        """
+        orig_h, orig_w = image_bgr.shape[:2]
+        OVERLAP_X = 70   # ~10% of 1408/2
+        OVERLAP_Y = 38   # ~10% of 768/2
+        mx = orig_w // 2
+        my = orig_h // 2
+
+        tiles = [
+            (0, 0, min(orig_w, mx + OVERLAP_X), min(orig_h, my + OVERLAP_Y)),      # TL
+            (max(0, mx - OVERLAP_X), 0, orig_w, min(orig_h, my + OVERLAP_Y)),      # TR
+            (0, max(0, my - OVERLAP_Y), min(orig_w, mx + OVERLAP_X), orig_h),      # BL
+            (max(0, mx - OVERLAP_X), max(0, my - OVERLAP_Y), orig_w, orig_h),      # BR
+        ]
+
+        all_dets = []
+        for x0, y0, x1, y1 in tiles:
+            all_dets.extend(self._infer_tile(image_bgr, x0, y0, x1, y1))
+
+        dets = self._soft_nms(all_dets)
+
+        out_boxes: list[BoundingBox] = []
+        for x1, y1, x2, y2, conf, cls_id in dets:
+            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)))
+            out_boxes.append(
+                BoundingBox(
+                    x1=ix1,
+                    y1=iy1,
+                    x2=ix2,
+                    y2=iy2,
+                    cls_id=cls_id,
+                    conf=max(0.0, min(1.0, conf)),
+                )
+            )
+        return out_boxes
+
+    # ---------------------------------------------------------------- entry
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        results: list[TVFrameResult] = []
+        for idx, image in enumerate(batch_images):
+            boxes = self._infer_single(image)
+            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + idx,
+                    boxes=boxes,
+                    keypoints=keypoints,
+                )
+            )
+        return results

numberplate_weights.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:955a5b36654a997cc242b60fd87070dbaf0e28247531c356fb9d5f8afa2af4b7
+size 19531262