Link to this sectionReference 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_tflite: Export model to TensorFlow Lite format.
        export_edgetpu: Export model to Edge TPU format.
        export_tfjs: Export model to TensorFlow.js 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, "int8": True, "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"
    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", "tflite", "edgetpu", "tfjs"}

    # 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"} and not self.args.int8:
        LOGGER.warning(f"{fmt} export requires int8=True, setting int8=True.")
        self.args.int8 = True
    if fmt == "axelera":
        if 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 not self.args.data:
            self.args.data = TASK2CALIBRATIONDATA.get(model.task)
    if fmt == "imx":
        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 == "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.int8 and check_version(trt.__version__, "==10.3.0") 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.half and self.args.int8:
        LOGGER.warning("half=True and int8=True are mutually exclusive, setting half=False.")
        self.args.half = False
    if self.args.half and fmt == "torchscript" and self.device.type == "cpu":
        LOGGER.warning(
            "half=True only compatible with GPU export for TorchScript, i.e. use device=0, setting half=False."
        )
        self.args.half = False
    self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
    if self.args.optimize:
        assert fmt != "ncnn", "optimize=True not compatible with format='ncnn', i.e. use optimize=False"
        assert self.device.type == "cpu", "optimize=True not compatible with cuda devices, i.e. use device='cpu'"
    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 not self.args.int8:
            LOGGER.warning(f"Rockchip target '{self.args.name}' requires int8=True, setting int8=True.")
            self.args.int8 = True
    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' or 'v73'
        assert self.args.name in QNN_HTP_ARCHS, (
            f"Invalid HTP architecture '{self.args.name}' for Qualcomm QNN export. Valid archs are {QNN_HTP_ARCHS} "
            "(Snapdragon 888/8Gen1/8Gen2/8Gen3/8Elite/8EliteGen5 respectively)."
        )
    if self.args.nms and model.task == "semantic":
        LOGGER.warning("'nms=True' is not valid for semantic segmentation 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 fmt != "tflite" or not ARM64 or not LINUX, "TFLite export with NMS unsupported on ARM64 Linux"
        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 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.int8 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}'."
        )
    if fmt == "tfjs" and ARM64 and LINUX:
        raise SystemError("TF.js exports are not currently supported on ARM64 Linux")
    # 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()

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

        model = FXModel(model, self.imgsz)
    if fmt in {"tflite", "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)):
            m.export = True
            m.format = self.args.format
        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 anchor count for small image sizes (required for TensorRT compatibility)
            anchors = 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, anchors)
            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"}:
        # 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 TFLite, 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.half 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().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,
        "batch": self.args.batch,
        "imgsz": self.imgsz,
        "names": model.names,
        "args": {k: 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 in {"pb", "tfjs"}:  # pb prerequisite to tfjs
            f = self.export_pb(keras_model=keras_model)
        if fmt == "tflite":
            f = self.export_tflite()
        if fmt == "edgetpu":
            f = self.export_edgetpu(tflite_model=Path(f) / f"{self.file.stem}_full_integer_quant.tflite")
        if fmt == "tfjs":
            f = self.export_tfjs()
    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 = "int8" if self.args.int8 else "half" if self.args.half else ""  # quantization
        # Export-only formats deploy in-browser and are not loadable by AutoBackend
        predict_validate = (
            ""
            if fmt == "tfjs"
            else 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}"
        )
        LOGGER.info(
            f"\nExport complete ({time.time() - t:.1f}s)"
            f"\nResults saved to {colorstr('bold', Path(f).resolve())}"
            f"{predict_validate}"
            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_axelera(self, prefix=colorstr("Axelera:")):
    """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:")):
    """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"
    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 self.model.task != "classify", "'dynamic=True' is not supported for CoreML classification models."
    f = self.file.with_suffix(".mlmodel" if mlmodel else ".mlpackage")
    if f.is_dir():
        shutil.rmtree(f)

    if self.model.task == "detect":
        model = IOSDetectModel(self.model, self.im, mlprogram=not mlmodel) if self.args.nms else self.model
    else:
        if self.args.nms:
            LOGGER.warning(f"{prefix} 'nms=True' is only available for Detect models like 'yolo26n.pt'.")
            # TODO CoreML Segment and Pose model pipelining
        model = self.model

    if self.args.dynamic:
        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=32, upper_bound=self.imgsz[0] * 2, default=self.imgsz[0]),
                ct.RangeDim(lower_bound=32, upper_bound=self.imgsz[1] * 2, default=self.imgsz[1]),
            )
        )
        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])]

    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,
        half=self.args.half,
        int8=self.args.int8,
        metadata=self.metadata,
        prefix=prefix,
    )

    if self.args.nms and self.model.task == "detect":
        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:")):
    """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:")):
    """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:")):
    """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.half,
        self.args.int8,
        self.args.dynamic,
        self.im.shape,
        dla=self.dla,
        dataset=self.get_int8_calibration_dataloader(prefix) if self.args.int8 else None,
        metadata=self.metadata,
        verbose=self.args.verbose,
        prefix=prefix,
    )

    return f

@try_export
def export_executorch(self, prefix=colorstr("ExecuTorch:")):
    """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_imx(self, prefix=colorstr("IMX:")):
    """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_mnn(self, prefix=colorstr("MNN:")):
    """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"),
        half=self.args.half,
        int8=self.args.int8,
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_ncnn(self, prefix=colorstr("NCNN:")):
    """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/"),
        half=self.args.half,
        metadata=self.metadata,
        device=self.device,
        prefix=prefix,
    )

@try_export
def export_onnx(self, prefix=colorstr("ONNX:")):
    """Export YOLO model to ONNX format."""
    requirements = ["onnx>=1.12.0,<2.0.0"]
    if self.args.simplify or (self.args.format == "onnx" and self.args.int8):
        # 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)
    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, 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

    with arange_patch(dynamic=bool(dynamic), half=self.args.half, fmt=self.args.format):
        torch2onnx(
            NMSModel(self.model, self.args) if self.args.nms else self.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}")

    # 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.half 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.int8 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:")):
    """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.half)
        YAML.save(Path(file).parent / "metadata.yaml", self.metadata)  # add metadata.yaml

    calibration_dataset, ignored_scope = None, None
    if self.args.int8:
        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(  # ignore operations
                patterns=[
                    f".*{head_module_name}/.*/Add",
                    f".*{head_module_name}/.*/Sub*",
                    f".*{head_module_name}/.*/Mul*",
                    f".*{head_module_name}/.*/Div*",
                ],
                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,
        half=self.args.half,
        int8=self.args.int8,
        calibration_dataset=calibration_dataset,
        ignored_scope=ignored_scope,
        prefix=prefix,
    )

    suffix = f"_{'int8_' if self.args.int8 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:")):
    """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:")):
    """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:")):
    """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:")):
    """Export YOLO model to RKNN format with optional INT8 quantization."""
    from ultralytics.utils.export.rknn import onnx2rknn

    self.args.opset = min(self.args.opset or 19, 19)  # rknn-toolkit expects opset<=19
    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.int8:
        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")
    return onnx2rknn(
        onnx_file=f_onnx,
        output_dir=output_dir,
        name=self.args.name,
        int8=self.args.int8,
        dataset=rknn_dataset,
        metadata=self.metadata,
        prefix=prefix,
    )

@try_export
def export_saved_model(self, prefix=colorstr("TensorFlow SavedModel:")):
    """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.int8 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()
            .astype(np.float32)
        )

    # Export to ONNX
    if isinstance(self.model.model[-1], RTDETRDecoder):
        self.args.opset = self.args.opset or 19
        assert 16 <= self.args.opset <= 19, "RTDETR export requires opset>=16;<=19"
    self.args.simplify = True
    f_onnx = self.export_onnx()  # ensure ONNX is available
    keras_model = onnx2saved_model(
        f_onnx,
        f,
        int8=self.args.int8,
        images=images,
        disable_group_convolution=self.args.format in {"tfjs", "edgetpu"},
        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_tfjs(self, prefix=colorstr("TensorFlow.js:")):
    """Export YOLO model to TensorFlow.js format."""
    assert not IS_PYTHON_MINIMUM_3_13, (
        "TensorFlow.js export not supported on Python>=3.13: tensorflowjs requires the np.object alias removed "
        "in NumPy 1.24, but Python 3.13 has no NumPy<2.1 wheels."
    )
    from ultralytics.utils.export.tensorflow import pb2tfjs

    output_dir = pb2tfjs(
        pb_file=str(self.file.with_suffix(".pb")),
        output_dir=str(self.file).replace(self.file.suffix, "_web_model/"),
        half=self.args.half,
        int8=self.args.int8,
        prefix=prefix,
    )
    YAML.save(Path(output_dir) / "metadata.yaml", self.metadata)
    return output_dir

@try_export
def export_tflite(self, prefix=colorstr("TensorFlow Lite:")):
    """Export YOLO model to TensorFlow Lite format."""
    # BUG https://github.com/ultralytics/ultralytics/issues/13436
    import tensorflow as tf

    LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
    saved_model = Path(str(self.file).replace(self.file.suffix, "_saved_model"))
    if self.args.int8:
        f = saved_model / f"{self.file.stem}_int8.tflite"  # fp32 in/out
    elif self.args.half:
        f = saved_model / f"{self.file.stem}_float16.tflite"  # fp32 in/out
    else:
        f = saved_model / f"{self.file.stem}_float32.tflite"
    return str(f)

@try_export
def export_torchscript(self, prefix=colorstr("TorchScript:")):
    """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"),
        optimize=self.args.optimize,
        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}'")
    data = (check_cls_dataset if self.model.task == "classify" else check_det_dataset)(self.args.data)
    if self.model.task == "classify":
        dataset = YOLODataset(
            data[self.args.split or "val"],
            data=data,
            fraction=self.args.fraction,
            task=self.model.task,
            imgsz=max(self.imgsz),
            augment=False,
            batch_size=self.args.batch,
        )
    else:
        cfg = deepcopy(self.args)
        cfg.imgsz = max(self.imgsz)
        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.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):

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 and Core ML 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 (~20M values at
    1024px) 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 in frozenset({"saved_model", "tflite", "tfjs"})

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 = dict(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
        if self.args.format == "tflite":  # TFLite is already normalized
            nmsbox *= multiplier
        else:
            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.int8)  # 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", "optimize", "half", "nms", "dynamic"],
            "base",
        ],
        [
            "ONNX",
            "onnx",
            ".onnx",
            True,
            True,
            ["batch", "data", "dynamic", "half", "int8", "opset", "simplify", "nms", "fraction"],
            "base",
        ],
        [
            "OpenVINO",
            "openvino",
            "_openvino_model",
            True,
            False,
            ["batch", "data", "dynamic", "half", "int8", "nms", "fraction"],
            "base",
        ],
        [
            "TensorRT",
            "engine",
            ".engine",
            False,
            True,
            ["batch", "data", "dynamic", "half", "int8", "simplify", "nms", "fraction"],
            "base",
        ],
        ["CoreML", "coreml", ".mlpackage", True, False, ["batch", "dynamic", "half", "int8", "nms"], "coreml"],
        [
            "TensorFlow SavedModel",
            "saved_model",
            "_saved_model",
            True,
            True,
            ["batch", "data", "fraction", "int8", "keras", "nms"],
            "tensorflow",
        ],
        ["TensorFlow GraphDef", "pb", ".pb", True, True, ["batch"], "tensorflow"],
        [
            "TensorFlow Lite",
            "tflite",
            ".tflite",
            True,
            False,
            ["batch", "data", "half", "int8", "nms", "fraction"],
            "tensorflow",
        ],
        ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", True, False, ["data", "fraction", "int8"], "tensorflow"],
        [
            "TensorFlow.js",
            "tfjs",
            "_web_model",
            True,
            False,
            ["batch", "data", "fraction", "half", "int8", "nms"],
            "tensorflow",
        ],
        ["PaddlePaddle", "paddle", "_paddle_model", True, True, ["batch"], "base"],
        ["MNN", "mnn", ".mnn", True, True, ["batch", "half", "int8"], "mnn"],
        ["NCNN", "ncnn", "_ncnn_model", True, True, ["batch", "half"], "ncnn"],
        ["IMX", "imx", "_imx_model", True, True, ["data", "int8", "fraction", "nms"], "isolated-imx"],
        [
            "RKNN",
            "rknn",
            "_rknn_model",
            False,
            False,
            ["batch", "name", "half", "int8", "data", "fraction"],
            "isolated-rknn",
        ],
        ["ExecuTorch", "executorch", "_executorch_model", True, False, ["batch"], "executorch"],
        [
            "Axelera AI",
            "axelera",
            "_axelera_model",
            False,
            False,
            ["batch", "int8", "fraction", "data"],
            "isolated-axelera",
        ],
        ["DEEPX", "deepx", "_deepx_model", False, False, ["data", "int8", "optimize"], "isolated-deepx"],
        ["Qualcomm QNN", "qnn", "_qnn.onnx", False, False, ["batch", "name", "int8", "fraction", "data"], "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.
    """
    export_args = ["half", "int8", "dynamic", "keras", "nms", "batch", "fraction", "data"]

    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)
    for arg in export_args:
        not_default = getattr(passed_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 > 0.0, "0.0 MB output model size"
            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 e

    return outer_func