Reference for ultralytics/engine/exporter.py#

class Exporter:
    """A class for exporting YOLO models to various formats.

    This class provides functionality to export YOLO models to different formats including ONNX, TensorRT, CoreML,
    TensorFlow, and others. It handles format validation, device selection, model preparation, and the actual export
    process for each supported format.

    Attributes:
        args (SimpleNamespace): Configuration arguments for the exporter.
        callbacks (dict): Dictionary of callback functions for different export events.
        im (torch.Tensor): Input tensor for model inference during export.
        model (torch.nn.Module): The YOLO model to be exported.
        file (Path): Path to the model file being exported.
        output_shape (tuple): Shape of the model output tensor(s).
        pretty_name (str): Formatted model name for display purposes.
        metadata (dict): Model metadata including description, author, version, etc.
        device (torch.device): Device on which the model is loaded.
        imgsz (list): Input image size for the model.

    Methods:
        __call__: Main export method that handles the export process.
        get_int8_calibration_dataloader: Build dataloader for INT8 calibration.
        export_torchscript: Export model to TorchScript format.
        export_onnx: Export model to ONNX format.
        export_openvino: Export model to OpenVINO format.
        export_paddle: Export model to PaddlePaddle format.
        export_mnn: Export model to MNN format.
        export_ncnn: Export model to NCNN format.
        export_coreml: Export model to CoreML format.
        export_engine: Export model to TensorRT format.
        export_saved_model: Export model to TensorFlow SavedModel format.
        export_pb: Export model to TensorFlow GraphDef format.
        export_edgetpu: Export model to Edge TPU format.
        export_rknn: Export model to RKNN format.
        export_imx: Export model to IMX format.
        export_executorch: Export model to ExecuTorch format.
        export_axelera: Export model to Axelera format.
        export_deepx: Export model to DEEPX format.

    Examples:
        Export a YOLO26 model to TorchScript format
        >>> from ultralytics.engine.exporter import Exporter
        >>> exporter = Exporter()
        >>> exporter(model="yolo26n.pt")  # exports to yolo26n.torchscript

        Export with specific arguments
        >>> args = {"format": "onnx", "dynamic": True, "quantize": 8, "data": "coco8.yaml"}
        >>> exporter = Exporter(overrides=args)
        >>> exporter(model="yolo26n.pt")
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks: dict | None = None):
        """Initialize the Exporter class.

        Args:
            cfg (str | Path | dict | SimpleNamespace, optional): Configuration file path or configuration object.
            overrides (dict, optional): Configuration overrides.
            _callbacks (dict, optional): Dictionary of callback functions.
        """
        self.args = get_cfg(cfg, overrides)
        self.callbacks = _callbacks or callbacks.get_default_callbacks()
        callbacks.add_integration_callbacks(self)

def __call__(self, model=None) -> str:
    """Export a model and return the final exported path as a string.

    Returns:
        (str): Path to the exported file or directory (the last export artifact).
    """
    t = time.time()
    fmt = self.args.format.lower()  # to lowercase
    if fmt in {"tensorrt", "trt"}:  # 'engine' aliases
        fmt = "engine"
    if fmt in {"mlmodel", "mlpackage", "mlprogram", "apple", "ios", "coreml"}:  # 'coreml' aliases
        fmt = "coreml"
    if fmt in {"huawei", "cann", "om"}:  # 'ascend' aliases
        fmt = self.args.format = "ascend"
    if fmt in {"tflite", "tfjs"}:  # deprecated formats, replaced by the unified Google LiteRT export
        LOGGER.warning(
            f"format='{fmt}' is deprecated as of 8.4.83 and has been replaced by the unified Google LiteRT "
            f"format. Exporting format='litert' instead. See https://docs.ultralytics.com/integrations/litert/"
        )
        fmt = self.args.format = "litert"
    fmts_dict = export_formats()
    fmts = tuple(fmts_dict["Argument"][1:])  # available export formats
    if fmt not in fmts:
        import difflib

        # Get the closest match if format is invalid
        matches = difflib.get_close_matches(fmt, fmts, n=1, cutoff=0.6)  # 60% similarity required to match
        if not matches:
            msg = "Model is already in PyTorch format." if fmt == "pt" else f"Invalid export format='{fmt}'."
            raise ValueError(f"{msg} Valid formats are {fmts}")
        LOGGER.warning(f"Invalid export format='{fmt}', updating to format='{matches[0]}'")
        fmt = matches[0]
    is_tf_format = fmt in {"saved_model", "pb", "edgetpu"}

    # Device
    self.dla = None
    if fmt == "engine" and self.args.device is None:
        LOGGER.warning("TensorRT requires GPU export, automatically assigning device=0")
        self.args.device = "0"
    if fmt == "engine" and "dla" in str(self.args.device):  # convert int/list to str first
        device_str = str(self.args.device)
        self.dla = device_str.rsplit(":", 1)[-1]
        self.args.device = "0"  # update device to "0"
        assert self.dla in {"0", "1"}, f"Expected device 'dla:0' or 'dla:1', but got {device_str}."
    if fmt == "imx" and self.args.device is None and torch.cuda.is_available():
        LOGGER.warning("Exporting on CPU while CUDA is available, setting device=0 for faster export on GPU.")
        self.args.device = "0"  # update device to "0"
    self.device = select_device("cpu" if self.args.device is None else self.args.device)

    # Argument compatibility checks
    fmt_keys = dict(zip(fmts_dict["Argument"], fmts_dict["Arguments"]))[fmt]
    validate_args(fmt, self.args, fmt_keys)
    if fmt in {"deepx", "axelera", "imx", "edgetpu", "qnn", "hailo"} and self.args.quantize not in {8, "w8a16"}:
        if self.args.quantize == 32:
            raise ValueError(
                f"{fmt} export only supports INT8, but got an explicit quantize=32 (FP32) request. "
                f"See {QUANTIZE_DOCS_URL}"
            )
        LOGGER.warning(f"{fmt} export requires INT8 quantization, enabling it.")
        self.args.quantize = "w8a16" if fmt == "qnn" else 8
    if fmt in {"axelera", "hailo"} and not self.args.data:
        self.args.data = TASK2CALIBRATIONDATA.get(model.task)
    if fmt == "hailo":
        assert LINUX and not ARM64, "Hailo export is only supported on Linux x86_64."
        blocks = {str(x[2]) for x in model.yaml.get("backbone", []) + model.yaml.get("head", [])}
        family = Path(getattr(model, "yaml_file", None) or model.yaml.get("yaml_file", "")).stem.lower() or (
            "yolov8" if "C2f" in blocks else "yolo11" if {"C3k2", "C2PSA"} <= blocks else ""
        )
        task26 = {Segment26: "segmentation", Pose26: "pose", OBB26: "OBB"}.get(type(model.model[-1]))
        if task26:
            raise ValueError(f"Hailo export does not currently support YOLO26 {task26} models.")
        if (
            model.task not in {"detect", "segment", "pose", "obb", "classify", "semantic", "depth"}
            or type(model.model[-1]) not in {Detect, Segment, Pose, OBB, Classify, SemanticSegment, Depth}
            or not family.startswith(("yolov8", "yolo11", "yolo26"))
        ):
            raise ValueError(
                "Hailo export currently supports YOLOv8/YOLO11/YOLO26 detection and classification models, "
                "YOLOv8/YOLO11 segmentation, pose, and OBB models, and YOLO26 semantic segmentation and depth "
                "models."
            )
        if model.task in {"semantic", "depth"} and not family.startswith("yolo26"):
            raise ValueError(f"Hailo export supports {model.task} models only for YOLO26.")
        if self.args.end2end is not None:
            raise ValueError(
                "Hailo export selects the model output path automatically; remove the end2end argument."
            )
        self.args.name = str(self.args.name or "hailo8l").lower()
        hailo_archs = ("hailo8", "hailo8l", "hailo10h", "hailo15h", "hailo15l")
        if self.args.name not in hailo_archs:
            raise ValueError(f"Invalid Hailo architecture '{self.args.name}'. Valid names are {hailo_archs}.")
    if fmt == "axelera" and model.task == "segment" and any(isinstance(m, Segment26) for m in model.modules()):
        raise ValueError("Axelera export does not currently support YOLO26 segmentation models.")
    if fmt == "imx":
        if model.task == "depth":
            raise ValueError("IMX export is not supported for depth models.")
        if not self.args.nms and model.task in {"detect", "pose", "segment"}:
            LOGGER.warning("IMX export requires nms=True, setting nms=True.")
            self.args.nms = True
        if model.task not in {"detect", "pose", "classify", "segment"}:
            raise ValueError(
                "IMX export only supported for detection, pose estimation, classification, and segmentation models."
            )
    if not hasattr(model, "names"):
        model.names = default_class_names()
    model.names = check_class_names(model.names)
    if hasattr(model, "end2end"):
        if self.args.end2end is not None:
            model.end2end = self.args.end2end
        if fmt in {"rknn", "ncnn", "executorch", "paddle", "imx", "edgetpu", "qnn"}:
            # Disable end2end branch for certain export formats as they does not support topk
            model.end2end = False
            LOGGER.warning(f"{fmt.upper()} export does not support end2end models, disabling end2end branch.")
        if fmt == "litert" and self.args.quantize in {8, "w8a16"}:
            # Static activation quantization collapses the end2end class-index output; export raw and run NMS later
            model.end2end = False
            LOGGER.warning("LiteRT INT8 export does not support end2end models, disabling end2end branch.")
        if fmt == "engine":
            try:
                import tensorrt as trt

                if check_version(trt.__version__, "<8.5.0"):
                    # https://github.com/ultralytics/ultralytics/issues/24607
                    model.end2end = False
                    LOGGER.warning(
                        "TensorRT versions earlier than 8.5.0 do not support the Mod operator in end-to-end models, disabling the end2end branch. "
                        "Please upgrade TensorRT to 8.5.0 or later to enable end2end export."
                    )

                if (
                    self.args.quantize == 8
                    and check_version(trt.__version__, ">=10.3.0,<10.4.0")  # JetPack 6 builds report 10.3.0.x
                    and is_jetson(jetpack=6)
                ):
                    # https://github.com/ultralytics/ultralytics/issues/23841
                    model.end2end = False
                    LOGGER.warning(
                        "TensorRT 10.3.0 on JetPack 6 with int8 has known end2end build issues, disabling end2end branch. "
                        "For a fix, see https://docs.ultralytics.com/guides/nvidia-jetson/#why-does-my-tensorrt-int8-export-disable-end2end-on-jetpack-6"
                        ""
                    )
            except ImportError:
                pass
    if self.args.quantize == 16 and fmt == "torchscript" and self.device.type == "cpu":
        raise ValueError("FP16 TorchScript export is only supported on GPU, i.e. use device=0.")
    self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
    if fmt == "axelera" and min(self.imgsz) < 64:
        raise ValueError(f"Axelera export requires imgsz>=64, but got imgsz={self.imgsz}.")
    if fmt == "rknn":
        if not self.args.name:
            LOGGER.warning(
                "Rockchip RKNN export requires a missing 'name' arg for processor type. "
                "Using default name='rk3588'."
            )
            self.args.name = "rk3588"
        self.args.name = self.args.name.lower()
        assert self.args.name in RKNN_CHIPS, (
            f"Invalid processor name '{self.args.name}' for Rockchip RKNN export. Valid names are {RKNN_CHIPS}."
        )
        if self.args.name in {"rv1103", "rv1106", "rv1103b", "rv1106b"} and self.args.quantize != 8:
            if self.args.quantize not in {None, 8}:
                raise ValueError(
                    f"Rockchip target '{self.args.name}' only supports INT8, but got quantize={self.args.quantize}. "
                    f"See {QUANTIZE_DOCS_URL}"
                )
            LOGGER.warning(f"Rockchip target '{self.args.name}' requires INT8 quantization, enabling it.")
            self.args.quantize = 8
        elif self.args.quantize is None:
            self.args.quantize = 16
    if fmt == "ascend":
        # No SoC allowlist: valid --soc_version values depend on which Ascend-cann-kernels-* packages are
        # installed, so a hardcoded list would reject valid targets. ATC reports an unknown SoC itself.
        if not self.args.name:
            LOGGER.warning(
                "Huawei Ascend export requires a missing 'name' arg for the target SoC. "
                "Using default name='Ascend310B4'."
            )
            self.args.name = "Ascend310B4"
        if not str(self.args.name).startswith("Ascend"):
            raise ValueError(
                f"Invalid Ascend SoC name='{self.args.name}'. Expected a CANN --soc_version such as "
                f"'Ascend310P3' or 'Ascend310B4'. See https://docs.ultralytics.com/integrations/ascend/"
            )
        if self.args.quantize is None:
            self.args.quantize = 16  # Ascend AI Core convolutions accept only FP16/INT8 inputs, never FP32
    if fmt == "qnn":
        if not self.args.name:
            LOGGER.warning(
                "Qualcomm QNN export requires a missing 'name' arg for the target Hexagon HTP architecture. "
                "Using default name='73' (Snapdragon 8 Gen 2)."
            )
            self.args.name = "73"
        self.args.name = str(self.args.name).lower().lstrip("v")  # accept '73', 'v73', or a supported SoC
        assert self.args.name in QNN_HTP_TARGETS, (
            f"Invalid Qualcomm QNN target '{self.args.name}'. Valid targets are {tuple(QNN_HTP_TARGETS)}."
        )
    if self.args.nms and model.task in {"semantic", "depth"}:
        LOGGER.warning(f"'nms=True' is not valid for {model.task} models. Forcing 'nms=False'.")
        self.args.nms = False
    if fmt == "coreml" and self.args.nms and model.task != "detect":
        LOGGER.warning("CoreML 'nms=True' is only supported for detect models. Forcing 'nms=False'.")
        self.args.nms = False
    if self.args.nms:
        assert not isinstance(model, ClassificationModel), "'nms=True' is not valid for classification models."
        assert not is_tf_format or TORCH_1_13, "TensorFlow exports with NMS require torch>=1.13"
        assert fmt != "onnx" or TORCH_1_13, "ONNX export with NMS requires torch>=1.13"
        if getattr(model, "end2end", False) or isinstance(model.model[-1], RTDETRDecoder):
            LOGGER.warning("'nms=True' is not available for end2end models. Forcing 'nms=False'.")
            self.args.nms = False
        self.args.conf = self.args.conf or 0.25  # set conf default value for nms export
    if fmt == "mnn" and self.args.nms:
        if self.args.dynamic:
            raise ValueError("Alibaba MNN export does not support combining 'dynamic=True' with 'nms=True'.")
        if model.task not in {"detect", "pose"}:
            raise ValueError("Alibaba MNN export with 'nms=True' only supports detect and pose models.")
    if fmt == "coreml":
        if self.args.batch > 1:
            assert self.args.dynamic, (
                "batch sizes > 1 are not supported without 'dynamic=True' for CoreML export. Please retry at 'dynamic=True'."
            )
        if self.args.dynamic:
            assert not self.args.nms, (
                "'nms=True' cannot be used together with 'dynamic=True' for CoreML export. Please disable one of them."
            )
            assert model.task != "classify" and not isinstance(model.model[-1], RTDETRDecoder), (
                "'dynamic=True' is not supported for CoreML classification or RT-DETR models."
            )
    if (fmt in {"engine", "coreml"} or self.args.nms) and self.args.dynamic and self.args.batch == 1:
        LOGGER.warning(
            f"'dynamic=True' model with '{'nms=True' if self.args.nms else f'format={self.args.format}'}' requires max batch size, i.e. 'batch=16'"
        )
    if fmt == "edgetpu":
        if not LINUX or ARM64:
            raise SystemError(
                "Edge TPU export only supported on non-aarch64 Linux. See https://coral.ai/docs/edgetpu/compiler"
            )
        elif self.args.batch != 1:  # see github.com/ultralytics/ultralytics/pull/13420
            LOGGER.warning("Edge TPU export requires batch size 1, setting batch=1.")
            self.args.batch = 1
    if isinstance(model, WorldModel):
        LOGGER.warning(
            "YOLOWorld (original version) export is not supported to any format. "
            "YOLOWorldv2 models (i.e. 'yolov8s-worldv2.pt') only support export to "
            "(torchscript, onnx, openvino, engine, coreml) formats. "
            "See https://docs.ultralytics.com/models/yolo-world for details."
        )
        model.clip_model = None  # openvino int8 export error: https://github.com/ultralytics/ultralytics/pull/18445
    if self.args.quantize in {8, "w8a16"} and not self.args.data:
        self.args.data = DEFAULT_CFG.data or TASK2DATA[getattr(model, "task", "detect")]  # assign default data
        LOGGER.warning(
            f"INT8 export requires a missing 'data' arg for calibration. Using default 'data={self.args.data}'."
        )
    # Recommend OpenVINO if export and Intel CPU
    if SETTINGS.get("openvino_msg"):
        if is_intel():
            LOGGER.info(
                "💡 ProTip: Export to OpenVINO format for best performance on Intel hardware."
                " Learn more at https://docs.ultralytics.com/integrations/openvino/"
            )
        SETTINGS["openvino_msg"] = False

    # Input
    im = torch.zeros(self.args.batch, model.yaml.get("channels", 3), *self.imgsz).to(self.device)
    file = Path(
        getattr(model, "pt_path", None) or getattr(model, "yaml_file", None) or model.yaml.get("yaml_file", "")
    )
    if file.suffix in {".yaml", ".yml"}:
        file = Path(file.name)

    # Update model
    model = deepcopy(model).to(self.device)
    for p in model.parameters():
        p.requires_grad = False
    model.eval()
    model.float()
    model = model.fuse(imgsz=self.imgsz)

    if fmt == "imx":
        from ultralytics.utils.export.imx import FXModel

        model = FXModel(model, self.imgsz)
    if fmt == "edgetpu":
        from ultralytics.utils.export.tensorflow import tf_wrapper

        model = tf_wrapper(model)
    if fmt == "executorch":
        from ultralytics.utils.export.executorch import executorch_wrapper

        model = executorch_wrapper(model)
    for m in model.modules():
        if isinstance(m, (Classify, SemanticSegment, Depth)):
            m.export = True
            m.format = self.args.format
            # Semantic argmax bake needs an integer graph output; TensorRT supports uint8 outputs only on TRT>=10
            # (Jetson TRT 8.x rejects them). Read the version from the package name to avoid importing tensorrt here.
            if isinstance(m, SemanticSegment) and fmt == "engine":
                cuda_major = (torch.version.cuda or "12").split(".")[0]
                m.bake_argmax = check_version(f"tensorrt-cu{cuda_major}", ">=10.0.0") or check_version(
                    "tensorrt", ">=10.0.0"
                )
        if isinstance(m, (Detect, RTDETRDecoder)):  # includes all Detect subclasses like Segment, Pose, OBB
            m.dynamic = self.args.dynamic
            m.export = True
            m.format = self.args.format
            # Clamp max_det to available queries/anchors (required for TensorRT compatibility)
            available = (
                m.num_queries
                if isinstance(m, RTDETRDecoder)
                else sum(int(self.imgsz[0] / s) * int(self.imgsz[1] / s) for s in model.stride.tolist())
            )
            m.max_det = min(self.args.max_det, available)
            m.agnostic_nms = self.args.agnostic_nms
            m.xyxy = self.args.nms and fmt != "coreml"
            m.shape = None  # reset cached shape for new export input size
            if hasattr(model, "pe") and hasattr(m, "fuse") and not hasattr(m, "lrpc"):  # for YOLOE models
                m.fuse(model.pe.to(self.device))
        elif isinstance(m, C2f) and not is_tf_format:
            # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
            m.forward = m.forward_split

    if model.task == "semantic" and fmt in {"qnn", "coreml", "ascend"}:
        # NPU-targeted semantic exports ship a compact uint8 class map instead of float logits: emitting logits
        # forces consumers to dequantize and argmax ~20M floats on the CPU every frame (measured erratic
        # 123-1065 ms on Hexagon). Not applied to LiteRT, where the GPU delegate cannot compile ArgMax (int64
        # indices) and a whole-graph CPU fallback is slower than GPU logits + consumer-side argmax. Python
        # predict/val accept both forms.
        model = ClassMapModel(model)

    y = None
    for _ in range(2):  # dry runs
        y = NMSModel(model, self.args)(im) if self.args.nms and fmt not in {"coreml", "imx"} else model(im)
    if self.args.quantize == 16 and fmt in {"onnx", "torchscript"} and self.device.type != "cpu":
        im, model = im.half(), model.half()  # to FP16

    # Assign
    self.im = im
    self.model = model
    self.file = file
    self.output_shape = (
        tuple(y.shape)
        if isinstance(y, torch.Tensor)
        else tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y)
    )
    self.pretty_name = Path(self.model.yaml.get("yaml_file", self.file)).stem.replace("yolo", "YOLO")
    data = model.args["data"] if hasattr(model, "args") and isinstance(model.args, dict) else ""
    description = f"Ultralytics {self.pretty_name} model {f'trained on {data}' if data else ''}"
    self.metadata = {
        "description": description,
        "author": "Ultralytics",
        "date": datetime.now().astimezone().isoformat(),
        "version": __version__,
        "license": "AGPL-3.0 License (https://ultralytics.com/license)",
        "docs": "https://docs.ultralytics.com",
        "stride": int(max(model.stride)),
        "task": model.task,
        "head": type(model.model[-1]).__name__,
        "batch": self.args.batch,
        "imgsz": self.imgsz,
        "names": model.names,
        "args": {k: str(v) if isinstance(v, Path) else v for k, v in self.args if k in fmt_keys},
        "channels": model.yaml.get("channels", 3),
        "end2end": getattr(model, "end2end", False),
    }  # model metadata
    if self.dla is not None:
        self.metadata["dla"] = self.dla  # make sure `AutoBackend` uses correct dla device if it has one
    if model.task == "pose":
        self.metadata["kpt_shape"] = model.model[-1].kpt_shape
        if hasattr(model, "kpt_names"):
            self.metadata["kpt_names"] = model.kpt_names

    LOGGER.info(
        f"\n{colorstr('PyTorch:')} starting from '{file}' with input shape {tuple(im.shape)} BCHW and "
        f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)"
    )
    self.run_callbacks("on_export_start")

    # Export
    if is_tf_format:
        f, keras_model = self.export_saved_model()
        if fmt == "pb":
            f = self.export_pb(keras_model=keras_model)
        if fmt == "edgetpu":
            f = self.export_edgetpu(tflite_model=Path(f) / f"{self.file.stem}_full_integer_quant.tflite")
    else:
        f = getattr(self, f"export_{fmt}")()

    # Finish
    if f:
        square = self.imgsz[0] == self.imgsz[1]
        s = (
            ""
            if square
            else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not "
            f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
        )
        imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(" ", "")
        q = "quantize=16" if self.args.quantize == 16 else ""  # FP16 inference flag for the val/predict hint
        inference_commands = (
            f"\nPredict:         yolo predict task={model.task} model={f} imgsz={imgsz} {q}"
            f"\nValidate:        yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}"
            if fmt in AutoBackend._BACKEND_MAP
            else ""
        )
        LOGGER.info(
            f"\nExport complete ({time.time() - t:.1f}s)"
            f"\nResults saved to {colorstr('bold', Path(f).resolve())}"
            f"{inference_commands}"
            f"\nVisualize:       https://netron.app"
        )

    self.run_callbacks("on_export_end")
    return f  # path to final export artifact

def _add_tflite_metadata(self, file):
    """Add metadata to *.tflite models per https://ai.google.dev/edge/litert/models/metadata."""
    import zipfile

    with zipfile.ZipFile(file, "a", zipfile.ZIP_DEFLATED) as zf:
        zf.writestr("metadata.json", json.dumps(self.metadata, indent=2))

@staticmethod
def _transform_fn(data_item) -> np.ndarray:
    """Quantization preprocessing transform for INT8 calibration (Axelera, OpenVINO, ONNX, QNN)."""
    data_item: torch.Tensor = data_item["img"] if isinstance(data_item, dict) else data_item
    assert data_item.dtype == torch.uint8, "Input image must be uint8 for the quantization preprocessing"
    im = data_item.numpy().astype(np.float32) / 255.0  # uint8 to fp16/32 and 0 - 255 to 0.0 - 1.0
    return im[None] if im.ndim == 3 else im

def add_callback(self, event: str, callback):
    """Append the given callback to the specified event."""
    self.callbacks[event].append(callback)

@try_export
def export_ascend(self, prefix=colorstr("Ascend:")):  # noqa: B008
    """Export YOLO model to Huawei Ascend offline model (.om) format."""
    from ultralytics.utils.export.ascend import _check_atc, onnx2ascend

    _check_atc()  # before the ONNX trace, so a missing toolchain does not cost a full export first
    if self.args.opset and self.args.opset > 17:
        LOGGER.warning(f"{prefix} the CANN ONNX parser requires opset<=17, setting opset=17.")
    self.args.opset = min(self.args.opset or 17, 17)
    return onnx2ascend(
        onnx_file=self.export_onnx(),
        output_dir=self.file.parent / f"{self.file.stem}_ascend_model",
        name=self.args.name,
        imgsz=self.imgsz,
        batch=self.args.batch,
        channels=self.im.shape[1],
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_axelera(self, prefix=colorstr("Axelera:")):  # noqa: B008
    """Export YOLO model to Axelera format."""
    assert LINUX and not (ARM64 and IS_DOCKER), (
        "export is only supported on Linux and is not supported on ARM64 Docker."
    )
    assert TORCH_2_8, "export requires torch>=2.8.0."

    from ultralytics.utils.export.axelera import torch2axelera

    output_dir = self.file.parent / f"{self.file.stem}_axelera_model"
    return torch2axelera(
        model=self.model,
        output_dir=output_dir,
        calibration_dataset=self.get_int8_calibration_dataloader(prefix),
        transform_fn=self._transform_fn,
        model_name=self.file.stem,
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_coreml(self, prefix=colorstr("CoreML:")):  # noqa: B008
    """Export YOLO model to CoreML format."""
    mlmodel = self.args.format.lower() == "mlmodel"  # legacy *.mlmodel export format requested
    from ultralytics.utils.export.coreml import IOSDetectModel, pipeline_coreml, torch2coreml

    # numpy 2.4.x breaks coremltools CoreML export https://github.com/apple/coremltools/issues/2633
    check_requirements(["coremltools>=9.0", "numpy>=1.14.5,<=2.3.5"])
    import coremltools as ct

    assert not WINDOWS, "CoreML export is not supported on Windows, please run on macOS or Linux."
    assert TORCH_1_11, "CoreML export requires torch>=1.11"
    f = self.file.with_suffix(".mlmodel" if mlmodel else ".mlpackage")
    if f.is_dir():
        shutil.rmtree(f)

    # TODO CoreML Segment and Pose model pipelining; 'nms=True' is forced off for non-detect tasks upstream
    model = IOSDetectModel(self.model, self.im, mlprogram=not mlmodel) if self.args.nms else self.model

    if self.args.dynamic:
        h, w = self.imgsz
        lb_h = lb_w = 32
        if getattr(self.model, "end2end", False):
            # end2end graphs bake TopK k=max_det, so the smallest declared input must still supply >= k anchors
            # or CoreML rejects the model at load; shrink the range proportionally from the traced default size
            stride = int(self.model.stride.max())
            r = self.model.model[-1].max_det / sum(int(h / s) * int(w / s) for s in self.model.stride.tolist())
            lb_h = max(lb_h, int(np.ceil(h * r**0.5 / stride)) * stride)
            lb_w = max(lb_w, int(np.ceil(w * r**0.5 / stride)) * stride)
        input_shape = ct.Shape(
            shape=(
                ct.RangeDim(lower_bound=1, upper_bound=self.args.batch, default=1),
                self.im.shape[1],
                ct.RangeDim(lower_bound=lb_h, upper_bound=h * 2, default=h),
                ct.RangeDim(lower_bound=lb_w, upper_bound=w * 2, default=w),
            )
        )
        inputs = [ct.TensorType("image", shape=input_shape)]
    else:
        inputs = [ct.ImageType("image", shape=self.im.shape, scale=1 / 255, bias=[0.0, 0.0, 0.0])]

    quantize = 16 if self.args.nms and not mlmodel and self.args.quantize is None else self.args.quantize
    self.metadata["args"]["quantize"] = quantize
    ct_model = torch2coreml(
        model=model,
        inputs=inputs,
        im=self.im,
        classifier_names=list(self.model.names.values()) if self.model.task == "classify" else None,
        mlmodel=mlmodel,
        quantize=quantize,
        metadata=self.metadata,
        prefix=prefix,
    )

    if self.args.nms:
        ct_model = pipeline_coreml(
            ct_model,
            self.output_shape,
            weights_dir=None if mlmodel else ct_model.weights_dir,
            metadata=self.metadata,
            mlmodel=mlmodel,
            iou=self.args.iou,
            conf=self.args.conf,
            agnostic_nms=self.args.agnostic_nms,
            prefix=prefix,
        )

    if self.model.task == "classify":
        ct_model.user_defined_metadata.update({"com.apple.coreml.model.preview.type": "imageClassifier"})

    try:
        ct_model.save(str(f))  # save *.mlpackage
    except Exception as e:
        LOGGER.warning(
            f"{prefix} CoreML export to *.mlpackage failed ({e}), reverting to *.mlmodel export. "
            f"Known coremltools Python 3.11 and Windows bugs https://github.com/apple/coremltools/issues/1928."
        )
        f = f.with_suffix(".mlmodel")
        ct_model.save(str(f))
    return f

@try_export
def export_deepx(self, prefix=colorstr("DEEPX:")):  # noqa: B008
    """Export YOLO model to DEEPX format."""
    assert LINUX and not ARM64, "DEEPX export only supported on non-aarch64 Linux"
    from ultralytics.utils.export.deepx import onnx2deepx

    f = self.export_onnx()
    return onnx2deepx(
        onnx_file=f,
        imgsz=self.imgsz,
        dataset=self.get_int8_calibration_dataloader(prefix),
        metadata=self.metadata,
        optimize=self.args.optimize,
        prefix=prefix,
    )

@try_export
def export_edgetpu(self, tflite_model="", prefix=colorstr("Edge TPU:")):  # noqa: B008
    """Export YOLO model to Edge TPU format https://coral.ai/docs/edgetpu/models-intro/."""
    from ultralytics.utils.export.tensorflow import tflite2edgetpu

    output_file = tflite2edgetpu(tflite_file=tflite_model, output_dir=tflite_model.parent, prefix=prefix)
    self._add_tflite_metadata(output_file)
    return output_file

@try_export
def export_engine(self, prefix=colorstr("TensorRT:")):  # noqa: B008
    """Export YOLO model to TensorRT format https://developer.nvidia.com/tensorrt."""
    assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'"
    f_onnx = self.export_onnx()  # run before TRT import https://github.com/ultralytics/ultralytics/issues/7016
    from ultralytics.utils.export.engine import onnx2engine

    assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}"
    f = self.file.with_suffix(".engine")  # TensorRT engine file
    onnx2engine(
        f_onnx,
        f,
        self.args.workspace,
        self.args.quantize,
        self.args.dynamic,
        self.im.shape,
        dla=self.dla,
        dataset=self.get_int8_calibration_dataloader(prefix) if self.args.quantize == 8 else None,
        metadata=self.metadata,
        verbose=self.args.verbose,
        prefix=prefix,
    )

    return f

@try_export
def export_executorch(self, prefix=colorstr("ExecuTorch:")):  # noqa: B008
    """Export YOLO model to ExecuTorch *.pte format."""
    assert TORCH_2_9, f"ExecuTorch requires torch>=2.9.0 but torch=={TORCH_VERSION} is installed"
    from ultralytics.utils.export.executorch import torch2executorch

    return torch2executorch(
        model=self.model,
        im=self.im,
        output_dir=str(self.file).replace(self.file.suffix, "_executorch_model/"),
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_hailo(self, prefix=colorstr("Hailo:")):  # noqa: B008
    """Export a YOLO model to Hailo Executable Format (HEF)."""
    try:
        import tensorflow as tf
        from hailo_sdk_client import ClientRunner
    except ImportError as e:
        raise ImportError("Hailo export requires the Hailo Dataflow Compiler.") from e

    calibration_dataloader = self.get_int8_calibration_dataloader(prefix)
    calibration_size = len(calibration_dataloader.dataset)
    LOGGER.warning(
        f"\nHailo level-2 optimization will use {calibration_size} calibration images. "
        "Hailo recommends at least 1,024 representative images for best accuracy. "
        'Pass data="path/to/dataset.yaml". '
        "See https://docs.ultralytics.com/integrations/hailo/#export-a-hailo-hef-model"
    )
    head_index = len(self.model.model) - 1
    head = self.model.model[head_index]
    one2one = getattr(self.model, "end2end", False)
    task = self.model.task
    if task == "classify":
        # The Classify head ends in Gemm -> Softmax; cut at the Softmax so the HEF returns the same
        # (1, nc) probabilities as the PyTorch model. The DFC translates the softmax to a native layer.
        end_nodes = [f"/model.{head_index}/Softmax"]
    elif task == "semantic":
        # Multi-class Hailo-15/10 (DFC 5.x) heads compile the bilinear upsample and ArgMax on chip. Hailo-8/8L
        # (DFC 3.x) cannot compile the Resize, and single-class heads use a threshold instead of ArgMax, so both
        # cut at the classifier logits and run the reduction on the host.
        head.bake_argmax = head.nc > 1 and self.args.name in {"hailo10h", "hailo15h", "hailo15l"}
        end_nodes = [
            f"/model.{head_index}/ArgMax"
            if head.bake_argmax
            else f"/model.{head_index}/classifier/classifier.1/Conv"
        ]
    elif task == "depth":
        # The Depth head ends in clamp/exp, log-affine calibration, and a 4x upsample. Cut at the final logit
        # conv (head.3, the last layer of the dense decoder) so the a16 HEF carries the raw logit and the host
        # mirrors Depth.forward. Same string convention as detect's `.2/Conv`; no new head attribute.
        end_nodes = [f"/model.{head_index}/head/head.3/Conv"]
    else:
        scales = range(len(head.one2one_cv2 if one2one else head.cv2))
        if one2one:
            end_nodes = [
                f"/model.{head_index}/one2one_cv{branch}.{i}/one2one_cv{branch}.{i}.2/Conv"
                for branch in (2, 3)
                for i in scales
            ]
        elif task in {"segment", "pose", "obb"}:
            # reg/cls/extra triple per scale (extra = mask coeffs, keypoints, or angle); segment adds prototypes.
            end_nodes = [
                f"/model.{head_index}/cv{branch}.{i}/cv{branch}.{i}.2/Conv" for i in scales for branch in (2, 3, 4)
            ]
            if task == "segment":
                end_nodes.append(f"/model.{head_index}/proto/cv3/act/Mul")
        else:
            end_nodes = [
                f"/model.{head_index}/cv{branch}.{i}/cv{branch}.{i}.2/Conv" for i in scales for branch in (2, 3)
            ]
    self.args.opset = 11
    f_onnx = Path(self.export_onnx())
    output_dir = self.file.parent / f"{self.file.stem}_hailo_model"
    if output_dir.exists():
        shutil.rmtree(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    try:
        runner = ClientRunner(hw_arch=self.args.name)
        runner.translate_onnx_model(str(f_onnx), self.file.stem, end_node_names=end_nodes)
        model_script = [
            "normalization1 = normalization([0, 0, 0], [255, 255, 255])",
            "model_optimization_flavor(optimization_level=2)",
            f"post_quantization_optimization(finetune, policy=enabled, dataset_size={calibration_size})",
        ]
        if one2one or task == "depth":
            # a16 on the output(s): the NMS-free detect logits and the single dense depth logit both need the
            # wider activation to keep their range (a8 collapses the depth map; validated on Hailo-8L).
            outputs = ", ".join(f"output_layer{i + 1}" for i in range(len(end_nodes)))
            model_script.append(f"quantization_param([{outputs}], precision_mode=a16_w16)")
        elif task in {"classify", "semantic"}:
            pass  # softmax/class-map is already the graph output; no NMS or activation changes needed
        else:
            outputs = [layer.inputs[0].rsplit("/", 1)[-1] for layer in runner.get_hn_model().get_output_layers()]
            if task in {"segment", "pose", "obb"}:
                # Bake sigmoid into the class convs only (position 1 of each per-scale reg/cls/extra triple).
                # Mask coeffs, prototypes, keypoints and angles stay raw and are decoded on the host.
                model_script.extend(
                    f"change_output_activation({outputs[i]}, sigmoid)" for i in range(1, 3 * len(scales), 3)
                )
            else:
                nms_config = output_dir / "nms_config.json"
                nms_config.write_text(
                    json.dumps(
                        {
                            "nms_scores_th": self.args.conf if self.args.conf is not None else 0.25,
                            "nms_iou_th": self.args.iou,
                            "image_dims": self.imgsz,
                            "max_proposals_per_class": 100,
                            "classes": len(self.model.names),
                            "regression_length": 16,
                            "background_removal": False,
                            "background_removal_index": 0,
                            "bbox_decoders": [
                                {
                                    "name": f"bbox_decoder_{stride}",
                                    "stride": stride,
                                    "reg_layer": outputs[i * 2],
                                    "cls_layer": outputs[i * 2 + 1],
                                }
                                for i, stride in enumerate(int(x) for x in head.stride)
                            ],
                        },
                        indent=2,
                    )
                )
                model_script.extend(
                    f"change_output_activation({outputs[i]}, sigmoid)" for i in range(1, len(outputs), 2)
                )
                model_script.append(f'nms_postprocess("{nms_config}", meta_arch=yolov8, engine=cpu)')
                model_script.append("allocator_param(width_splitter_defuse=disabled)")
        runner.load_model_script("\n".join(model_script))

        def calibration_dataset():
            for batch in calibration_dataloader:
                for image in batch["img"].permute(0, 2, 3, 1).numpy().astype(np.float32):
                    yield image, {}

        runner.optimize(
            lambda: tf.data.Dataset.from_generator(
                calibration_dataset,
                output_signature=(tf.TensorSpec(shape=(*self.imgsz, 3), dtype=tf.float32), {}),
            )
        )
        (output_dir / f"{self.file.stem}.hef").write_bytes(runner.compile())
        YAML.save(
            output_dir / "metadata.yaml",
            {
                **self.metadata,
                "hailo_arch": self.args.name,
                "nms": task == "detect" and not one2one,
                "semantic_baked": task == "semantic" and head.bake_argmax,
                # Depth's learned log-affine calibration is applied on the host, so it must ride in metadata.
                **({"cal_a": float(head.cal_a), "cal_b": float(head.cal_b)} if task == "depth" else {}),
            },
        )
        return str(output_dir)
    finally:
        f_onnx.unlink(missing_ok=True)

@try_export
def export_imx(self, prefix=colorstr("IMX:")):  # noqa: B008
    """Export YOLO model to IMX format."""
    assert LINUX, (
        "Export only supported on Linux."
        "See https://developer.aitrios.sony-semicon.com/en/docs/raspberry-pi-ai-camera/imx500-converter?version=3.17.3&progLang="
    )
    assert IS_PYTHON_MINIMUM_3_9, "IMX export is only supported on Python 3.9 or above."

    if getattr(self.model, "end2end", False):
        raise ValueError("IMX export is not supported for end2end models.")
    from ultralytics.utils.export.imx import torch2imx

    return torch2imx(
        model=self.model,
        output_dir=str(self.file).replace(self.file.suffix, "_imx_model/"),
        conf=self.args.conf,
        iou=self.args.iou,
        max_det=self.args.max_det,
        metadata=self.metadata,
        dataset=partial(self.get_int8_calibration_dataloader, prefix),
        prefix=prefix,
    )

@try_export
def export_litert(self, prefix=colorstr("LiteRT:")):  # noqa: B008
    """Export YOLO model to LiteRT format using litert_torch with optional INT8 quantization.

    Supports ``quantize=8`` (static INT8, int8 weights + int8 activations, requires calibration ``data``),
    ``quantize='w8a16'`` (static, int8 weights + int16 activations, requires calibration ``data``) and
    ``quantize='w8a32'`` (dynamic/weight-only INT8, int8 weights + FP32 activations, no calibration needed).
    """
    assert MACOS or (LINUX and not ARM64), "LiteRT export only supported on Linux x86 and macOS"
    from ultralytics.utils.export.litert import torch2litert

    return torch2litert(
        self.model,
        self.im,
        self.file,
        quantize=self.args.quantize,
        calibration_dataset=self.get_int8_calibration_dataloader(prefix)
        if self.args.quantize in {8, "w8a16"}
        else None,
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_mnn(self, prefix=colorstr("MNN:")):  # noqa: B008
    """Export YOLO model to MNN format using MNN https://github.com/alibaba/MNN."""
    from ultralytics.utils.export.mnn import onnx2mnn

    return onnx2mnn(
        onnx_file=self.export_onnx(),
        output_file=self.file.with_suffix(".mnn"),
        quantize=self.args.quantize,
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_ncnn(self, prefix=colorstr("NCNN:")):  # noqa: B008
    """Export YOLO model to NCNN format using PNNX https://github.com/pnnx/pnnx."""
    from ultralytics.utils.export.ncnn import torch2ncnn

    return torch2ncnn(
        model=self.model,
        im=self.im,
        output_dir=str(self.file).replace(self.file.suffix, "_ncnn_model/"),
        quantize=self.args.quantize,
        metadata=self.metadata,
        device=self.device,
        prefix=prefix,
    )

@try_export
def export_onnx(self, prefix=colorstr("ONNX:")):  # noqa: B008
    """Export YOLO model to ONNX format."""
    requirements = ["onnx>=1.16.1,<1.19.0" if self.args.format == "rknn" else "onnx>=1.12.0,<2.0.0"]
    if self.args.simplify or (self.args.format == "onnx" and self.args.quantize == 8):
        # Pass onnxruntime variants as interchangeable candidates so AutoUpdate keeps an installed build
        # (e.g. onnxruntime-qnn for QNN export) instead of reinstalling stable onnxruntime and breaking its ABI.
        ort = "onnxruntime-gpu" if "cuda" in self.device.type else "onnxruntime"
        requirements += [(ort, "onnxruntime", "onnxruntime-gpu", "onnxruntime-qnn")]
    if self.args.simplify:
        requirements += ["onnxslim>=0.1.82"]
    check_requirements(requirements)
    import onnx

    from ultralytics.utils.export.engine import best_onnx_opset, torch2onnx

    opset = self.args.opset or best_onnx_opset(onnx, cuda="cuda" in self.device.type, quantize=self.args.quantize)
    assert not isinstance(self.model.model[-1], RTDETRDecoder) or opset >= 16, "RTDETR export requires opset>=16"
    LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__} opset {opset}...")
    if self.args.nms:
        assert TORCH_1_13, f"'nms=True' ONNX export requires torch>=1.13 (found torch=={TORCH_VERSION})"

    f = str(self.file.with_suffix(".onnx"))
    output_names = ["output0", "output1"] if self.model.task == "segment" else ["output0"]
    dynamic = self.args.dynamic
    if dynamic:
        dynamic = {"images": {0: "batch", 2: "height", 3: "width"}}  # shape(1,3,640,640)
        if isinstance(self.model, SegmentationModel):
            dynamic["output0"] = {0: "batch", 2: "anchors"}  # shape(1, 116, 8400)
            dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"}  # shape(1,32,160,160)
        elif isinstance(self.model, DepthModel):
            dynamic["output0"] = {0: "batch", 2: "height", 3: "width"}  # shape(1,1,640,640) dense map, not anchors
        elif isinstance(self.model, DetectionModel):
            dynamic["output0"] = {0: "batch", 2: "anchors"}  # shape(1, 84, 8400)
        if self.args.nms:  # only batch size is dynamic with NMS
            dynamic["output0"].pop(2)
    if self.args.nms and self.model.task == "obb":
        self.args.opset = opset  # for NMSModel
        self.args.simplify = True  # fix OBB runtime error related to topk

    model = NMSModel(self.model, self.args) if self.args.nms else self.model
    # Normalize coordinates by input size so RKNN's per-tensor INT8 scale preserves class scores.
    if (
        self.args.format == "rknn"
        and self.args.quantize == 8
        and self.model.task in {"detect", "segment", "pose", "obb"}
        and not self.metadata["end2end"]
    ):
        from ultralytics.utils.export.engine import _NormalizeCoords

        model = _NormalizeCoords(
            model,
            int(self.im.shape[2]),
            int(self.im.shape[3]),
            self.model.task,
            len(self.metadata["names"]),
            self.metadata.get("kpt_shape"),
        )

    with arange_patch(dynamic=bool(dynamic), quantize=self.args.quantize, fmt=self.args.format):
        torch2onnx(
            model,
            self.im,
            f,
            opset=opset,
            input_names=["images"],
            output_names=output_names,
            dynamic=dynamic or None,
        )

    # Checks
    model_onnx = onnx.load(f)  # load onnx model

    # Simplify
    if self.args.simplify:
        try:
            import onnxslim

            LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...")
            model_onnx = onnxslim.slim(model_onnx)

        except Exception as e:
            LOGGER.warning(f"{prefix} simplifier failure: {e}")

    # CANN requires the optional score-threshold input on ONNX NonMaxSuppression nodes. Scores were already
    # filtered by args.conf in NMSModel, so zero preserves the graph's semantics.
    if self.args.format == "ascend":
        for i, node in enumerate(model_onnx.graph.node):
            if node.op_type == "NonMaxSuppression" and len(node.input) == 4:
                threshold_name = f"ascend_nms_score_threshold_{i}"
                node.input.append(threshold_name)
                model_onnx.graph.initializer.append(
                    onnx.helper.make_tensor(threshold_name, onnx.TensorProto.FLOAT, [1], [0.0])
                )

    # Metadata
    for k, v in self.metadata.items():
        meta = model_onnx.metadata_props.add()
        meta.key, meta.value = k, str(v)

    # IR version
    if getattr(model_onnx, "ir_version", 0) > 10:
        LOGGER.info(f"{prefix} limiting IR version {model_onnx.ir_version} to 10 for ONNXRuntime compatibility...")
        model_onnx.ir_version = 10

    # FP16 conversion for CPU export (GPU exports are already FP16 from model.half() during tracing)
    if self.args.quantize == 16 and self.args.format == "onnx" and self.device.type == "cpu":
        try:
            from onnxruntime.transformers import float16

            LOGGER.info(f"{prefix} converting to FP16...")
            model_onnx = float16.convert_float_to_float16(model_onnx, keep_io_types=True)
        except Exception as e:
            LOGGER.warning(f"{prefix} FP16 conversion failure: {e}")

    onnx.save(model_onnx, f)
    if self.args.quantize == 8 and self.args.format == "onnx":
        from ultralytics.utils.export.onnx import onnx_int8_quantize

        source = Path(f)
        f_int8 = str(source.with_name(f"{source.stem}_int8{source.suffix}"))
        f = onnx_int8_quantize(
            source,
            f_int8,
            self.get_int8_calibration_dataloader(prefix),
            self._transform_fn,
            batch=0 if self.args.dynamic else self.args.batch,
            prefix=prefix,
        )
        source.unlink(missing_ok=True)
    return f

@try_export
def export_openvino(self, prefix=colorstr("OpenVINO:")):  # noqa: B008
    """Export YOLO model to OpenVINO format."""
    from ultralytics.utils.export.openvino import torch2openvino

    # OpenVINO <= 2025.1.0 error on macOS 15.4+: https://github.com/openvinotoolkit/openvino/issues/30023
    check_requirements("openvino>=2025.2.0" if MACOS and MACOS_VERSION >= "15.4" else "openvino>=2024.0.0")
    import openvino as ov

    assert TORCH_2_1, f"OpenVINO export requires torch>=2.1 but torch=={TORCH_VERSION} is installed"

    def serialize(ov_model, file):
        """Set RT info, serialize, and save metadata YAML."""
        ov_model.set_rt_info("YOLO", ["model_info", "model_type"])
        ov_model.set_rt_info(True, ["model_info", "reverse_input_channels"])
        ov_model.set_rt_info(114, ["model_info", "pad_value"])
        ov_model.set_rt_info([255.0], ["model_info", "scale_values"])
        ov_model.set_rt_info(self.args.iou, ["model_info", "iou_threshold"])
        ov_model.set_rt_info([v.replace(" ", "_") for v in self.model.names.values()], ["model_info", "labels"])
        if self.model.task != "classify":
            ov_model.set_rt_info("fit_to_window_letterbox", ["model_info", "resize_type"])

        ov.save_model(ov_model, file, compress_to_fp16=self.args.quantize == 16)
        YAML.save(Path(file).parent / "metadata.yaml", self.metadata)  # add metadata.yaml

    calibration_dataset, ignored_scope = None, None
    if self.args.quantize == 8:
        check_requirements("packaging>=23.2")  # must be installed first to build nncf wheel
        check_requirements("nncf>=2.14.0,<3.0.0" if not TORCH_2_3 else "nncf>=2.14.0")
        import nncf

        calibration_dataset = nncf.Dataset(self.get_int8_calibration_dataloader(prefix), self._transform_fn)
        if isinstance(self.model.model[-1], Detect):
            # Includes all Detect subclasses like Segment, Pose, OBB, WorldDetect, YOLOEDetect
            head_module_name = ".".join(list(self.model.named_modules())[-1][0].split(".")[:2])
            ignored_scope = nncf.IgnoredScope(patterns=[f".*{head_module_name}(/|\\.dfl).*"], types=["Sigmoid"])

    ov_model = torch2openvino(
        model=NMSModel(self.model, self.args) if self.args.nms else self.model,
        im=self.im,
        dynamic=self.args.dynamic,
        quantize=self.args.quantize,
        calibration_dataset=calibration_dataset,
        ignored_scope=ignored_scope,
        prefix=prefix,
    )

    suffix = f"_{'int8_' if self.args.quantize == 8 else ''}openvino_model{os.sep}"
    f = str(self.file).replace(self.file.suffix, suffix)
    f_ov = str(Path(f) / self.file.with_suffix(".xml").name)

    serialize(ov_model, f_ov)
    return f

@try_export
def export_paddle(self, prefix=colorstr("PaddlePaddle:")):  # noqa: B008
    """Export YOLO model to PaddlePaddle format."""
    from ultralytics.utils.export.paddle import torch2paddle

    return torch2paddle(
        model=self.model,
        im=self.im,
        output_dir=str(self.file).replace(self.file.suffix, f"_paddle_model{os.sep}"),
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_pb(self, keras_model, prefix=colorstr("TensorFlow GraphDef:")):  # noqa: B008
    """Export YOLO model to TensorFlow GraphDef *.pb format https://github.com/leimao/Frozen-Graph-TensorFlow."""
    from ultralytics.utils.export.tensorflow import keras2pb

    return keras2pb(keras_model, output_file=self.file.with_suffix(".pb"), prefix=prefix)

@try_export
def export_qnn(self, prefix=colorstr("Qualcomm QNN:")):  # noqa: B008
    """Export YOLO model to a Qualcomm QNN context binary using ONNX Runtime QNN."""
    from ultralytics.utils.export.qnn import onnx2qnn

    # Wrap for Hexagon-friendly I/O: channel-last input (the class-map wrap for semantic is format-agnostic)
    model, im = self.model, self.im
    try:
        self.model, self.im = QNNModel(model), im.permute(0, 2, 3, 1)
        f_onnx = self.export_onnx()
    finally:
        self.model, self.im = model, im
    return onnx2qnn(
        onnx_file=f_onnx,
        output_file=str(self.file.with_name(f"{self.file.stem}_qnn.onnx")),
        dataset=self.get_int8_calibration_dataloader(prefix),
        transform_fn=self._transform_fn,
        name=self.args.name,
        metadata=self.metadata,
        batch=0 if self.args.dynamic else self.args.batch,
        prefix=prefix,
    )

@try_export
def export_rknn(self, prefix=colorstr("RKNN:")):  # noqa: B008
    """Export YOLO model to RKNN format with optional INT8 quantization."""
    from ultralytics.utils.export.rknn import onnx2rknn

    if self.args.opset and self.args.opset > 19:
        LOGGER.warning(f"{prefix} rknn-toolkit2 requires opset<=19, setting opset=19.")
    self.args.opset = min(self.args.opset or 19, 19)  # rknn-toolkit expects opset<=19
    self.im = self.im[:1]  # RKNN Toolkit expands the batch after calibrating the batch-1 ONNX model
    f_onnx = self.export_onnx()
    output_dir = Path(str(self.file).replace(self.file.suffix, f"_rknn_model{os.sep}"))
    rknn_dataset = None
    if self.args.quantize == 8:
        dataloader = self.get_int8_calibration_dataloader(prefix)
        image_paths = getattr(dataloader.dataset, "im_files", None)
        if image_paths is None and hasattr(dataloader.dataset, "samples"):
            image_paths = [x[0] for x in dataloader.dataset.samples]
        if not image_paths:
            raise ValueError("RKNN INT8 export requires a calibration dataset with image file paths.")
        output_dir.mkdir(parents=True, exist_ok=True)
        rknn_dataset = output_dir / "dataset.txt"
        rknn_dataset.write_text("\n".join(str(Path(x).resolve()) for x in image_paths) + "\n")
    try:
        return onnx2rknn(
            onnx_file=f_onnx,
            output_dir=output_dir,
            name=self.args.name,
            quantize=self.args.quantize,
            batch=self.args.batch,
            dataset=rknn_dataset,
            metadata=self.metadata,
            prefix=prefix,
        )
    finally:
        if self.args.quantize == 8:  # INT8 graphs hold normalized coordinates, so they are not reusable
            Path(f_onnx).unlink(missing_ok=True)

@try_export
def export_saved_model(self, prefix=colorstr("TensorFlow SavedModel:")):  # noqa: B008
    """Export YOLO model to TensorFlow SavedModel format."""
    assert not (MACOS and IS_PYTHON_MINIMUM_3_13), (
        "TensorFlow exports not supported on macOS with Python>=3.13: the ai-edge-litert macOS wheel fails to load "
        "(missing libpywrap_litert_common.dylib). TensorFlow export works on Linux Python 3.13."
    )
    from ultralytics.utils.export.tensorflow import onnx2saved_model

    f = Path(str(self.file).replace(self.file.suffix, "_saved_model"))
    if f.is_dir():
        shutil.rmtree(f)  # delete output folder

    # Export to TF
    images = None
    if self.args.quantize == 8 and self.args.data:
        images = [batch["img"] for batch in self.get_int8_calibration_dataloader(prefix)]
        images = (
            torch.nn.functional.interpolate(torch.cat(images, 0).float(), size=self.imgsz)
            .permute(0, 2, 3, 1)
            .numpy()
        )

    # Export to ONNX
    if isinstance(self.model.model[-1], RTDETRDecoder):
        self.args.opset = self.args.opset or 19
        assert self.args.opset <= 19, "RTDETR TensorFlow export requires opset<=19"
    self.args.simplify = True
    f_onnx = self.export_onnx()  # ensure ONNX is available
    keras_model = onnx2saved_model(
        f_onnx,
        f,
        quantize=self.args.quantize,
        images=images,
        disable_group_convolution=self.args.format == "edgetpu",
        cuda=self.device.type == "cuda",
        prefix=prefix,
    )
    YAML.save(f / "metadata.yaml", self.metadata)  # add metadata.yaml
    # Add TFLite metadata
    for file in f.rglob("*.tflite"):
        file.unlink() if "quant_with_int16_act.tflite" in str(file) else self._add_tflite_metadata(file)

    return str(f), keras_model  # or keras_model = tf.saved_model.load(f, tags=None, options=None)

@try_export
def export_torchscript(self, prefix=colorstr("TorchScript:")):  # noqa: B008
    """Export YOLO model to TorchScript format."""
    from ultralytics.utils.export.torchscript import torch2torchscript

    return torch2torchscript(
        model=NMSModel(self.model, self.args) if self.args.nms else self.model,
        im=self.im,
        output_file=self.file.with_suffix(".torchscript"),
        metadata=self.metadata,
        prefix=prefix,
    )

def get_int8_calibration_dataloader(self, prefix=""):
    """Build and return a dataloader for calibration of INT8 models."""
    LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'")
    cfg = deepcopy(self.args)
    cfg.imgsz = max(self.imgsz)
    if self.model.task == "classify":
        import torchvision.transforms as T  # scope for faster 'import ultralytics'

        data = check_cls_dataset(self.args.data, split=self.args.split)
        dataset = ClassificationDataset(data[self.args.split or "val"], args=cfg, augment=False)
        if self.args.fraction < 1.0:
            dataset.samples = dataset.samples[: round(len(dataset.samples) * self.args.fraction)]
        # INT8 backends divide images by 255, so emit uint8 [0, 255] center-cropped like classify inference
        dataset.torch_transforms = T.Compose([T.Resize(cfg.imgsz), T.CenterCrop(cfg.imgsz), T.PILToTensor()])
    else:
        data = check_det_dataset(self.args.data, split=self.args.split)
        dataset = build_yolo_dataset(
            cfg,
            data[self.args.split or "val"],
            self.args.batch,
            data,
            mode="val",
            fraction=self.args.fraction,
        )
    if hasattr(dataset, "transforms") and hasattr(dataset.transforms.transforms[0], "new_shape"):
        dataset.transforms.transforms[0].new_shape = self.imgsz  # LetterBox with non-square imgsz
    n = len(dataset)
    if n < 1:
        raise ValueError(f"The calibration dataset must have at least 1 image, but found {n} images.")
    batch = min(self.args.batch, n)
    if n < self.args.batch:
        LOGGER.warning(
            f"{prefix} calibration dataset has only {n} images, reducing calibration batch size to {batch}."
        )
    if self.args.format == "axelera" and n < 100:
        LOGGER.warning(f"{prefix} >100 images required for Axelera calibration, found {n} images.")
    elif self.args.format != "axelera" and n < 300:
        LOGGER.warning(f"{prefix} >300 images recommended for INT8 calibration, found {n} images.")
    return build_dataloader(dataset, batch=batch, workers=0, drop_last=True)  # required for batch loading

def run_callbacks(self, event: str):
    """Execute all callbacks for a given event."""
    for callback in self.callbacks.get(event, []):
        callback(self)

class ExportWrapper(torch.nn.Module):
    """Base for export-time model wrappers: stores the wrapped model and forwards attribute lookups.

    Subclasses adapt a fused YOLO model's inference I/O for a specific deployment contract (layout, output
    reduction) while the exporter keeps interacting with the wrapper as if it were the model itself.
    """

    def __init__(self, model):
        """Wrap a fused YOLO `model` prepared for export."""
        super().__init__()
        # Stored under a private name so attribute forwarding resolves `wrapper.model` to the wrapped model's own
        # `model` (its nn.Sequential), keeping exporter code like `self.model.model[-1]` working unchanged.
        self._model = model
        self.task = model.task

def __getattr__(self, name):
    """Forward attribute lookups (model, names, stride, yaml, args, ...) to the wrapped model."""
    try:
        return super().__getattr__(name)
    except AttributeError:
        return getattr(self._model, name)

class QNNModel(ExportWrapper):
    """Wraps a YOLO model with channel-last inference input for Qualcomm QNN export.

    Traced by the standard ONNX export (`export_qnn` swaps it in with a channel-last dummy input). The graph takes `[N,
    H, W, C]` images - the Hexagon HTP's native layout and what camera pipelines produce - so ONNX Runtime's layout
    transformer folds the wrapper's Transpose into the NPU partition during context generation, and neither the NPU
    (boundary transpose) nor the consuming app (CPU-side permute) pays a per-inference layout cost.

    Attributes:
        task (str): The wrapped model's task, forwarded for the ONNX export plumbing.
    """

def forward(self, x):
    """Run inference on channel-last `[N, H, W, C]` input normalized to [0, 1]."""
    return self._model(x.permute(0, 3, 1, 2))  # the wrapped model is NCHW; the transpose folds into the NPU graph

class ClassMapModel(ExportWrapper):
    """Reduces semantic-segmentation logits to a compact integer class map for export.

    Applied to QNN, Core ML and Ascend semantic exports, where the argmax runs on the NPU: deployment consumers want
    per-pixel class indices, and shipping float logits instead forces a dequantize + argmax over large tensors (~8M
    values at the standard 640px mobile input) on the consumer's CPU every frame - measured as both slow and highly
    variable on mobile NPUs. The argmax cannot live in the model's own forward because it is non-differentiable
    (training needs logits), so it is attached here at export time, mirroring how `NMSModel` adds suppression only for
    export.

    Attributes:
        task (str): The wrapped model's task ("semantic").
        dtype (torch.dtype): Class-index dtype; uint8 unless the model has more than 256 classes.
    """

    def __init__(self, model):
        """Wrap a fused semantic `model` so export emits class indices instead of logits."""
        super().__init__(model)
        # uint8 quarters the NPU->CPU output transfer vs int32 and Core ML promotes it to int32 in-spec;
        # int32 only when more than 256 classes make uint8 indices ambiguous.
        self.dtype = torch.uint8 if len(model.names) <= 256 else torch.int32

def forward(self, x):
    """Run the wrapped model and return a `[N, H, W]` integer class map instead of float logits."""
    y = self._model(x)
    y = y[0] if isinstance(y, (list, tuple)) else y
    # Single-channel (binary) models threshold the logit, matching predict/val semantics for nc == 1
    return (y.argmax(1) if y.shape[1] > 1 else y[:, 0].gt(0)).to(self.dtype)

class NMSModel(torch.nn.Module):
    """Model wrapper with embedded NMS for Detect, Segment, Pose and OBB."""

    def __init__(self, model, args):
        """Initialize the NMSModel.

        Args:
            model (torch.nn.Module): The model to wrap with NMS postprocessing.
            args (SimpleNamespace): The export arguments.
        """
        super().__init__()
        self.model = model
        self.args = args
        self.obb = model.task == "obb"
        self.is_tf = self.args.format == "saved_model"

def forward(self, x):
    """Perform inference with NMS post-processing. Supports Detect, Segment, OBB and Pose.

    Args:
        x (torch.Tensor): The preprocessed tensor with shape (B, C, H, W).

    Returns:
        (torch.Tensor | tuple): Tensor of shape (B, max_det, 4 + 2 + extra_shape) where B is the batch size, or a
            tuple of (detections, proto) for segmentation models.
    """
    from torchvision.ops import nms

    preds = self.model(x)
    pred = preds[0] if isinstance(preds, tuple) else preds
    kwargs = {"device": pred.device, "dtype": pred.dtype}
    bs = pred.shape[0]
    pred = pred.transpose(-1, -2)  # shape(1,84,6300) to shape(1,6300,84)
    extra_shape = pred.shape[-1] - (4 + len(self.model.names))  # extras from Segment, OBB, Pose
    if self.args.dynamic and self.args.batch > 1:  # batch size needs to always be same due to loop unroll
        pad = torch.zeros(torch.max(torch.tensor(self.args.batch - bs), torch.tensor(0)), *pred.shape[1:], **kwargs)
        pred = torch.cat((pred, pad))
    boxes, scores, extras = pred.split([4, len(self.model.names), extra_shape], dim=2)
    scores, classes = scores.max(dim=-1)
    self.args.max_det = min(pred.shape[1], self.args.max_det)  # in case num_anchors < max_det
    # (N, max_det, 4 coords + 1 class score + 1 class label + extra_shape).
    out = torch.zeros(pred.shape[0], self.args.max_det, boxes.shape[-1] + 2 + extra_shape, **kwargs)
    for i in range(bs):
        box, cls, score, extra = boxes[i], classes[i], scores[i], extras[i]
        mask = score > self.args.conf
        if self.is_tf or (self.args.format == "onnx" and self.obb):
            # TFLite GatherND error if mask is empty
            score *= mask
            # Explicit length otherwise reshape error, hardcoded to `self.args.max_det * 5`
            mask = score.topk(min(self.args.max_det * 5, score.shape[0])).indices
        box, score, cls, extra = box[mask], score[mask], cls[mask], extra[mask]
        nmsbox = box.clone()
        # `8` is the minimum value experimented to get correct NMS results for obb
        multiplier = 8 if self.obb else 1 / max(len(self.model.names), 1)
        # Normalize boxes for NMS since large values for class offset causes issue with int8 quantization
        nmsbox = multiplier * (nmsbox / torch.tensor(x.shape[2:], **kwargs).max())
        if not self.args.agnostic_nms:  # class-wise NMS
            end = 2 if self.obb else 4
            # fully explicit expansion otherwise reshape error
            cls_offset = cls.view(cls.shape[0], 1).expand(cls.shape[0], end)
            offbox = nmsbox[:, :end] + cls_offset * multiplier
            nmsbox = torch.cat((offbox, nmsbox[:, end:]), dim=-1)
        nms_fn = (
            partial(
                TorchNMS.fast_nms,
                use_triu=not (
                    self.is_tf
                    or (self.args.opset or 14) < 14
                    or (self.args.format == "openvino" and self.args.quantize == 8)  # OpenVINO INT8 error with triu
                ),
                iou_func=batch_probiou,
                exit_early=False,
            )
            if self.obb
            else nms
        )
        keep = nms_fn(
            torch.cat([nmsbox, extra], dim=-1) if self.obb else nmsbox,
            score,
            self.args.iou,
        )[: self.args.max_det]
        dets = torch.cat(
            [box[keep], score[keep].view(-1, 1), cls[keep].view(-1, 1).to(out.dtype), extra[keep]], dim=-1
        )
        # Zero-pad to max_det size to avoid reshape error
        pad = (0, 0, 0, self.args.max_det - dets.shape[0])
        out[i] = torch.nn.functional.pad(dets, pad)
    return (out[:bs], preds[1]) if self.model.task == "segment" else out[:bs]

def export_formats():
    """Return a dictionary of Ultralytics YOLO export formats."""
    #          Format, Argument, Suffix, CPU, GPU, Arguments, Env
    x = [
        ["PyTorch", "-", ".pt", True, True, [], "base"],
        [
            "TorchScript",
            "torchscript",
            ".torchscript",
            True,
            True,
            ["batch", "quantize", "nms", "dynamic"],
            "base",
        ],
        [
            "ONNX",
            "onnx",
            ".onnx",
            True,
            True,
            ["batch", "data", "dynamic", "quantize", "opset", "simplify", "nms", "fraction"],
            "base",
        ],
        [
            "OpenVINO",
            "openvino",
            "_openvino_model",
            True,
            False,
            ["batch", "data", "dynamic", "quantize", "nms", "fraction"],
            "base",
        ],
        [
            "TensorRT",
            "engine",
            ".engine",
            False,
            True,
            ["batch", "data", "dynamic", "quantize", "opset", "simplify", "workspace", "nms", "fraction"],
            "base",
        ],
        ["CoreML", "coreml", ".mlpackage", True, False, ["batch", "dynamic", "quantize", "nms"], "coreml"],
        [
            "TensorFlow SavedModel",
            "saved_model",
            "_saved_model",
            True,
            True,
            ["batch", "data", "fraction", "quantize", "opset", "keras", "nms"],
            "tensorflow",
        ],
        ["TensorFlow GraphDef", "pb", ".pb", True, True, ["batch", "opset"], "tensorflow"],
        [
            "TensorFlow Edge TPU",
            "edgetpu",
            "_edgetpu.tflite",
            True,
            False,
            ["data", "fraction", "quantize", "opset"],
            "tensorflow",
        ],
        ["PaddlePaddle", "paddle", "_paddle_model", True, True, ["batch"], "base"],
        ["MNN", "mnn", ".mnn", True, True, ["batch", "dynamic", "quantize", "opset", "simplify", "nms"], "mnn"],
        ["NCNN", "ncnn", "_ncnn_model", True, True, ["batch", "quantize"], "ncnn"],
        ["IMX", "imx", "_imx_model", True, True, ["data", "quantize", "fraction", "nms"], "isolated-imx"],
        [
            "RKNN",
            "rknn",
            "_rknn_model",
            False,
            False,
            ["batch", "name", "quantize", "opset", "simplify", "data", "fraction"],
            "isolated-rknn",
        ],
        ["ExecuTorch", "executorch", "_executorch_model", True, False, ["batch"], "executorch"],
        [
            "Axelera AI",
            "axelera",
            "_axelera_model",
            False,
            False,
            ["batch", "quantize", "fraction", "data"],
            "isolated-axelera",
        ],
        [
            "DEEPX",
            "deepx",
            "_deepx_model",
            False,
            False,
            ["data", "quantize", "opset", "simplify", "optimize"],
            "isolated-deepx",
        ],
        [
            "Qualcomm QNN",
            "qnn",
            "_qnn.onnx",
            False,
            False,
            ["batch", "name", "quantize", "opset", "simplify", "fraction", "data"],
            "base",
        ],
        ["LiteRT", "litert", ".tflite", True, False, ["batch", "quantize", "data", "fraction"], "litert"],
        [
            "Hailo",
            "hailo",
            "_hailo_model",
            False,
            False,
            ["name", "quantize", "data", "fraction", "simplify", "conf", "iou"],
            "base",
        ],
        [
            "Huawei Ascend",
            "ascend",
            "_ascend_model",
            False,
            False,
            ["batch", "name", "quantize", "opset", "simplify", "nms"],
            "base",
        ],
    ]
    return dict(zip(["Format", "Argument", "Suffix", "CPU", "GPU", "Arguments", "Env"], zip(*x)))

def validate_args(format, passed_args, valid_args):
    """Validate arguments based on the export format.

    Args:
        format (str): The export format.
        passed_args (SimpleNamespace): The arguments used during export.
        valid_args (list): List of valid arguments for the format.

    Raises:
        AssertionError: If an unsupported argument is used, or if the format lacks supported argument listings.
    """
    # Format-specific args come from the export table; skip inference args and quantize (validated above)
    export_args = sorted(set().union(*export_formats()["Arguments"]) - {"conf", "iou", "name", "quantize"})

    assert valid_args is not None, f"ERROR ❌️ valid arguments for '{format}' not listed."
    custom = {"batch": 1, "data": None, "device": None}  # exporter defaults
    default_args = get_cfg(DEFAULT_CFG, custom)
    if passed_args.quantize is not None:  # 32/None (FP32) is universal except FP32_UNSUPPORTED_FORMATS
        options = [label for label, formats in QUANTIZE_PRECISIONS if format in formats]
        if format not in FP32_UNSUPPORTED_FORMATS:
            options.append("32 (FP32)")
        hint = f"format='{format}' supports quantize={', '.join(options) or 'none'} (or None for FP32). See {QUANTIZE_DOCS_URL}"
        if passed_args.quantize == 16:  # FP16
            assert format in FP16_FORMATS, f"ERROR ❌️ quantize=16 (FP16) is not supported; {hint}"
        elif passed_args.quantize == 8:  # INT8
            assert format in INT8_FORMATS, f"ERROR ❌️ quantize=8 (INT8) is not supported; {hint}"
        elif passed_args.quantize == "w8a16":  # INT8 weights + 16-bit activations (FP16; INT16 on LiteRT)
            assert format in W8A16_FORMATS, f"ERROR ❌️ quantize='w8a16' is not supported; {hint}"
        elif passed_args.quantize == "w8a32":  # INT8 weights + FP32 activations (dynamic/weight-only INT8)
            assert format in W8A32_FORMATS, f"ERROR ❌️ quantize='w8a32' is not supported; {hint}"
        elif passed_args.quantize == 32:  # FP32
            assert format not in FP32_UNSUPPORTED_FORMATS, f"ERROR ❌️ quantize=32 (FP32) is not supported; {hint}"
    for arg in export_args:
        not_default = getattr(passed_args, arg, getattr(default_args, arg, None)) != getattr(default_args, arg, None)
        if not_default:
            assert arg in valid_args, f"ERROR ❌️ argument '{arg}' is not supported for format='{format}'"

def try_export(inner_func):
    """YOLO export decorator, i.e. @try_export."""
    inner_args = get_default_args(inner_func)

    def outer_func(*args, **kwargs):
        """Export a model."""
        prefix = inner_args["prefix"]
        dt = 0.0
        try:
            with Profile() as dt:
                f = inner_func(*args, **kwargs)  # exported file/dir or tuple of (file/dir, *)
            path = f if isinstance(f, (str, Path)) else f[0]
            mb = file_size(path)
            assert mb > 0.1, f"{mb:.3f} MB output model too small (likely corrupt or unsupported ops)"
            LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as '{path}' ({mb:.1f} MB)")
            return f
        except Exception as e:
            LOGGER.error(f"{prefix} export failure {dt.t:.1f}s: {e}")
            raise

    return outer_func