diff --git a/scripts/openvino_predict.py b/scripts/openvino_predict.py
new file mode 100644
index 0000000..bfbe320
--- /dev/null
+++ b/scripts/openvino_predict.py
@@ -0,0 +1,246 @@
+#!/usr/bin/env python
+"""用导出的 OpenVINO 模型推理，不依赖 ultralytics 与 torch。
+
+    # 已经切好的 160 片：直接推理
+    uv run python scripts/openvino_predict.py \\
+        --model runs/detect/current_openvino_model \\
+        --images data/film_yolo_160/images/val --out /tmp/ov_pred
+
+    # 任意尺寸的原图：加 --tile-size，脚本自己切片再拼回
+    uv run python scripts/openvino_predict.py \\
+        --model runs/detect/current_openvino_model \\
+        --images runs/boxes_shap_label_studio/images --tile-size 160 --out /tmp/ov_pred
+
+产出 YOLO 格式的标签（`class cx cy w h conf`，坐标归一化），`--labels` 指向真值目录时
+还会给出逐类的 TP/FP/FN。
+
+**拷到别的设备上跑，需要这三个文件**：本脚本、`engine/tiling.py`、`engine/detection.py`
+（后两者只依赖 numpy 与标准库），外加 `pip install openvino numpy pillow`。
+
+推理契约（实测确认）：
+
+- 输入 `[1, 3, N, N]` float32，**RGB** 顺序，除以 255，无 mean/std 标准化；
+- 输出 `[1, 4 + 类别数, anchor 数]`：前 4 行是 **xywh 中心点、像素单位**（可能超出边界，
+  要裁剪），后面每行是一个类别的分数，已过 sigmoid；
+- 导出的 `metadata.yaml` 里 `end2end: false`，**必须自己做 NMS**——实测同一个目标会出
+  3 个候选框。
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import Counter
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import numpy as np  # noqa: E402
+
+from engine.detection import Box, match_boxes, nms, parse_label  # noqa: E402
+from engine.tiling import cut, merge_tiles, plan_tiles  # noqa: E402
+from utils import setup_logging  # noqa: E402
+
+logger = None
+
+IMAGE_SUFFIXES = (".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff")
+
+
+class OpenVinoDetector:
+    """加载导出目录里的 .xml/.bin 与 metadata.yaml。"""
+
+    def __init__(self, model_dir: Path, device: str = "CPU") -> None:
+        import openvino as ov
+        import yaml
+
+        xml = next(model_dir.glob("*.xml"), None)
+        if xml is None:
+            raise FileNotFoundError(f"{model_dir} 下没有 .xml，这不像 OpenVINO 的导出目录")
+        core = ov.Core()
+        self.compiled = core.compile_model(core.read_model(str(xml)), device)
+        self.output = self.compiled.output(0)
+
+        meta_file = model_dir / "metadata.yaml"
+        meta = yaml.safe_load(meta_file.read_text(encoding="utf-8")) if meta_file.exists() else {}
+        names = meta.get("names") or {}
+        self.classes = [names[i] for i in sorted(names)] if names else []
+        imgsz = meta.get("imgsz") or []
+        # 以网络自身的输入形状为准，metadata 只是参考
+        shape = self.compiled.input(0).get_partial_shape()
+        self.size = int(shape[2].get_length()) if shape[2].is_static else (imgsz[0] if imgsz else 0)
+
+    def __call__(self, patches, conf: float, iou: float) -> list[list[Box]]:
+        """一批 size×size 的图 -> 每张图的归一化框（已做 NMS）。"""
+        out = []
+        for patch in patches:
+            raw = self.compiled([self._preprocess(patch)])[self.output][0]   # (4+nc, anchors)
+            out.append(self._decode(raw, conf, iou))
+        return out
+
+    def _preprocess(self, patch: np.ndarray) -> np.ndarray:
+        """HWC uint8 -> BCHW float32 RGB，除以 255。灰度图补成三通道。"""
+        if patch.ndim == 2:
+            patch = np.repeat(patch[..., None], 3, axis=2)
+        elif patch.shape[2] == 1:
+            patch = np.repeat(patch, 3, axis=2)
+        elif patch.shape[2] == 4:
+            patch = patch[..., :3]
+        return patch.astype(np.float32).transpose(2, 0, 1)[None] / 255.0
+
+    def _decode(self, raw: np.ndarray, conf: float, iou: float) -> list[Box]:
+        """原始输出 -> 归一化 xyxy 框。坐标是像素 xywh，分数已过 sigmoid。"""
+        xywh, scores = raw[:4], raw[4:]
+        class_ids, confidences = scores.argmax(0), scores.max(0)
+        keep = confidences >= conf
+        if not keep.any():
+            return []
+        cx, cy, w, h = xywh[:, keep]
+        size = self.size
+        boxes = [
+            Box(int(class_id),
+                float(np.clip((x - width / 2) / size, 0.0, 1.0)),
+                float(np.clip((y - height / 2) / size, 0.0, 1.0)),
+                float(np.clip((x + width / 2) / size, 0.0, 1.0)),
+                float(np.clip((y + height / 2) / size, 0.0, 1.0)),
+                float(score))
+            for x, y, width, height, class_id, score in zip(
+                cx, cy, w, h, class_ids[keep], confidences[keep], strict=True,
+            )
+        ]
+        return [boxes[i] for i in nms(boxes, iou)]
+
+
+def load_image(path: Path) -> np.ndarray:
+    from PIL import Image
+
+    with Image.open(path) as handle:
+        image = handle.convert("L") if handle.mode in ("L", "1", "I;16") else handle.convert("RGB")
+        return np.asarray(image)
+
+
+def main() -> int:
+    global logger
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--model", required=True, help="OpenVINO 导出目录（含 .xml/.bin）")
+    parser.add_argument("--images", required=True, help="图片目录")
+    parser.add_argument("--out", default=None, help="把预测写成 YOLO 标签的目录")
+    parser.add_argument("--labels", default=None, help="真值标签目录，给了就统计 TP/FP/FN")
+    parser.add_argument("--device", default="CPU", help="CPU / GPU / AUTO")
+    parser.add_argument("--conf", type=float, default=0.25)
+    parser.add_argument("--iou", type=float, default=0.5, help="NMS 的 IoU 阈值")
+    parser.add_argument(
+        "--tile-size", type=int, default=0,
+        help="图片大于模型输入时需要切片；图片本身就是模型输入尺寸时留 0",
+    )
+    parser.add_argument("--tile-overlap", type=int, default=32)
+    parser.add_argument("--limit", type=int, default=0)
+    args = parser.parse_args()
+
+    logger = setup_logging()
+    images = sorted(
+        p for p in Path(args.images).iterdir() if p.suffix.lower() in IMAGE_SUFFIXES
+    )
+    if args.limit:
+        images = images[: args.limit]
+    if not images:
+        logger.error("%s 下没有图片", args.images)
+        return 1
+
+    try:
+        detector = OpenVinoDetector(Path(args.model), args.device)
+    except FileNotFoundError as exc:
+        logger.error("%s", exc)
+        return 1
+    logger.info(
+        "模型 %s（输入 %d，%d 类）| 设备 %s | 图片 %d 张",
+        args.model, detector.size, len(detector.classes), args.device, len(images),
+    )
+
+    out_dir = Path(args.out) if args.out else None
+    if out_dir:
+        out_dir.mkdir(parents=True, exist_ok=True)
+    labels_dir = Path(args.labels) if args.labels else None
+
+    per_class: Counter[str] = Counter()
+    stats = {"tp": Counter(), "fp": Counter(), "fn": Counter()}
+    with_boxes = 0
+    mismatched = 0
+
+    for index, path in enumerate(images, start=1):
+        image = load_image(path)
+        height, width = image.shape[:2]
+
+        if args.tile_size > 0:
+            tiles = plan_tiles(width, height, args.tile_size, args.tile_overlap)
+            patches = [cut(image, tile, args.tile_size) for tile in tiles]
+            boxes = merge_tiles(
+                detector(patches, args.conf, args.iou), tiles, args.tile_size, width, height
+            )
+        else:
+            if (height, width) != (detector.size, detector.size):
+                mismatched += 1
+                continue
+            boxes = detector([image], args.conf, args.iou)[0]
+
+        with_boxes += bool(boxes)
+        for box in boxes:
+            per_class[detector.classes[box.class_id] if detector.classes else box.class_id] += 1
+
+        if out_dir:
+            lines = [
+                f"{b.class_id} {(b.x1 + b.x2) / 2:.6f} {(b.y1 + b.y2) / 2:.6f} "
+                f"{b.x2 - b.x1:.6f} {b.y2 - b.y1:.6f} {b.score:.4f}"
+                for b in boxes
+            ]
+            (out_dir / f"{path.stem}.txt").write_text(
+                "".join(f"{line}\n" for line in lines), encoding="utf-8"
+            )
+
+        if labels_dir:
+            truth_file = labels_dir / f"{path.stem}.txt"
+            truth = parse_label(truth_file.read_text(encoding="utf-8")) if truth_file.exists() else []
+            match = match_boxes(boxes, truth, iou_threshold=0.5)
+            name = lambda i: detector.classes[i] if detector.classes else i  # noqa: E731
+            for _, gt_index in match.pairs:
+                stats["tp"][name(truth[gt_index].class_id)] += 1
+            for i in match.unmatched_predictions:
+                stats["fp"][name(boxes[i].class_id)] += 1
+            for i in match.unmatched_ground_truth:
+                stats["fn"][name(truth[i].class_id)] += 1
+
+        if index % 2000 == 0:
+            logger.info("处理 [%d/%d]", index, len(images))
+
+    if mismatched:
+        logger.warning(
+            "%d 张图不是 %dx%d，已跳过——它们需要切片，加 --tile-size %d",
+            mismatched, detector.size, detector.size, detector.size,
+        )
+    logger.info(
+        "完成：%d 张，有框 %d，框数 %d", len(images) - mismatched, with_boxes, sum(per_class.values())
+    )
+    logger.info("逐类框数: %s", dict(sorted(per_class.items(), key=lambda kv: -kv[1])))
+
+    if labels_dir:
+        logger.info("%-12s %8s %8s %8s %8s", "类别", "TP", "FP", "FN", "召回")
+        for name in detector.classes:
+            tp, fp, fn = (stats[k][name] for k in ("tp", "fp", "fn"))
+            if tp or fp or fn:
+                logger.info(
+                    "%-12s %8d %8d %8d %7.1f%%", name, tp, fp, fn, 100 * tp / max(tp + fn, 1)
+                )
+        if out_dir:
+            (out_dir / "summary.json").write_text(
+                json.dumps(
+                    {k: dict(v) for k, v in stats.items()}, ensure_ascii=False, indent=2
+                ),
+                encoding="utf-8",
+            )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())