diff --git a/docs/en/integrations/sony-imx500.md b/docs/en/integrations/sony-imx500.md
index 5d2eed7f96..40a65ab822 100644
--- a/docs/en/integrations/sony-imx500.md
+++ b/docs/en/integrations/sony-imx500.md
@@ -205,9 +205,9 @@ The export process will create an ONNX model for quantization validation, along
         ├── dnnParams.xml
         ├── labels.txt
         ├── packerOut.zip
-        ├── yolo11n_imx.onnx
-        ├── yolo11n_imx_MemoryReport.json
-        └── yolo11n_imx.pbtxt
+        ├── model_imx.onnx
+        ├── model_imx_MemoryReport.json
+        └── model_imx.pbtxt
         ```
 
     === "Pose Estimation"
@@ -217,9 +217,9 @@ The export process will create an ONNX model for quantization validation, along
         ├── dnnParams.xml
         ├── labels.txt
         ├── packerOut.zip
-        ├── yolo11n-pose_imx.onnx
-        ├── yolo11n-pose_imx_MemoryReport.json
-        └── yolo11n-pose_imx.pbtxt
+        ├── model_imx.onnx
+        ├── model_imx_MemoryReport.json
+        └── model_imx.pbtxt
         ```
 
     === "Classification"
@@ -229,9 +229,9 @@ The export process will create an ONNX model for quantization validation, along
         ├── dnnParams.xml
         ├── labels.txt
         ├── packerOut.zip
-        ├── yolo11n-cls_imx.onnx
-        ├── yolo11n-cls_imx_MemoryReport.json
-        └── yolo11n-cls_imx.pbtxt
+        ├── model_imx.onnx
+        ├── model_imx_MemoryReport.json
+        └── model_imx.pbtxt
         ```
 
     === "Instance Segmentation"
@@ -241,9 +241,9 @@ The export process will create an ONNX model for quantization validation, along
         ├── dnnParams.xml
         ├── labels.txt
         ├── packerOut.zip
-        ├── yolo11n-seg_imx.onnx
-        ├── yolo11n-seg_imx_MemoryReport.json
-        └── yolo11n-seg_imx.pbtxt
+        ├── model_imx.onnx
+        ├── model_imx_MemoryReport.json
+        └── model_imx.pbtxt
         ```
 
 ## Using IMX500 Export in Deployment
diff --git a/tests/test_exports.py b/tests/test_exports.py
index 38fcd668ba..51b73040d5 100644
--- a/tests/test_exports.py
+++ b/tests/test_exports.py
@@ -341,7 +341,7 @@ def test_export_executorch():
     file = YOLO(MODEL).export(format="executorch", imgsz=32)
     assert Path(file).exists(), f"ExecuTorch export failed, directory not found: {file}"
     # Check that .pte file exists in the exported directory
-    pte_file = Path(file) / Path(MODEL).with_suffix(".pte").name
+    pte_file = Path(file) / "model.pte"
     assert pte_file.exists(), f"ExecuTorch .pte file not found: {pte_file}"
     # Check that metadata.yaml exists
     metadata_file = Path(file) / "metadata.yaml"
@@ -359,8 +359,7 @@ def test_export_executorch_matrix(task):
     file = YOLO(TASK2MODEL[task]).export(format="executorch", imgsz=32)
     assert Path(file).exists(), f"ExecuTorch export failed for task '{task}', directory not found: {file}"
     # Check that .pte file exists in the exported directory
-    model_name = Path(TASK2MODEL[task]).with_suffix(".pte").name
-    pte_file = Path(file) / model_name
+    pte_file = Path(file) / "model.pte"
     assert pte_file.exists(), f"ExecuTorch .pte file not found for task '{task}': {pte_file}"
     # Check that metadata.yaml exists
     metadata_file = Path(file) / "metadata.yaml"
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index a939faa1cb..1184e63c07 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
 
-__version__ = "8.4.37"
+__version__ = "8.4.38"
 
 import importlib
 import os
diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index 4565f86f46..32ee9d544f 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -601,9 +601,9 @@ class Exporter:
         from ultralytics.utils.export.torchscript import torch2torchscript
 
         return torch2torchscript(
-            NMSModel(self.model, self.args) if self.args.nms else self.model,
-            self.im,
-            self.file,
+            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,
@@ -692,9 +692,9 @@ class Exporter:
     @try_export
     def export_openvino(self, prefix=colorstr("OpenVINO:")):
         """Export YOLO model to OpenVINO format."""
-        from ultralytics.utils.export import torch2openvino
+        from ultralytics.utils.export.openvino import torch2openvino
 
-        # OpenVINO <= 2025.1.0 error on macOS 15.4+: https://github.com/openvinotoolkit/openvino/issues/30023"
+        # 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
 
@@ -757,16 +757,26 @@ class Exporter:
         """Export YOLO model to PaddlePaddle format."""
         from ultralytics.utils.export.paddle import torch2paddle
 
-        return torch2paddle(self.model, self.im, self.file, self.metadata, prefix)
+        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_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
 
-        f_onnx = self.export_onnx()
         return onnx2mnn(
-            f_onnx, self.file, half=self.args.half, int8=self.args.int8, metadata=self.metadata, prefix=prefix
+            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
@@ -775,9 +785,9 @@ class Exporter:
         from ultralytics.utils.export.ncnn import torch2ncnn
 
         return torch2ncnn(
-            self.model,
-            self.im,
-            self.file,
+            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,
@@ -986,9 +996,7 @@ class Exporter:
         """Export YOLO model to TensorFlow GraphDef *.pb format https://github.com/leimao/Frozen-Graph-TensorFlow."""
         from ultralytics.utils.export.tensorflow import keras2pb
 
-        f = self.file.with_suffix(".pb")
-        keras2pb(keras_model, f, prefix)
-        return f
+        return keras2pb(keras_model, output_file=self.file.with_suffix(".pb"), prefix=prefix)
 
     @try_export
     def export_tflite(self, prefix=colorstr("TensorFlow Lite:")):
@@ -1016,11 +1024,13 @@ class Exporter:
 
         from ultralytics.utils.export.axelera import torch2axelera
 
+        output_dir = self.file.parent / f"{self.file.stem}_axelera_model"
         return torch2axelera(
             model=self.model,
-            file=self.file,
+            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,
         )
@@ -1032,7 +1042,13 @@ class Exporter:
         check_executorch_requirements()
         from ultralytics.utils.export.executorch import torch2executorch
 
-        return torch2executorch(self.model, self.file, self.im, metadata=self.metadata, prefix=prefix)
+        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_edgetpu(self, tflite_model="", prefix=colorstr("Edge TPU:")):
@@ -1055,10 +1071,9 @@ class Exporter:
         from ultralytics.utils.export.tensorflow import tflite2edgetpu
 
         LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...")
-        tflite2edgetpu(tflite_file=tflite_model, output_dir=tflite_model.parent, prefix=prefix)
-        f = str(tflite_model).replace(".tflite", "_edgetpu.tflite")  # Edge TPU model
-        self._add_tflite_metadata(f)
-        return f
+        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_tfjs(self, prefix=colorstr("TensorFlow.js:")):
@@ -1066,12 +1081,15 @@ class Exporter:
         check_requirements("tensorflowjs")
         from ultralytics.utils.export.tensorflow import pb2tfjs
 
-        f = str(self.file).replace(self.file.suffix, "_web_model")  # js dir
-        f_pb = str(self.file.with_suffix(".pb"))  # *.pb path
-        pb2tfjs(pb_file=f_pb, output_dir=f, half=self.args.half, int8=self.args.int8, prefix=prefix)
-        # Add metadata
-        YAML.save(Path(f) / "metadata.yaml", self.metadata)  # add metadata.yaml
-        return f
+        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_rknn(self, prefix=colorstr("RKNN:")):
@@ -1080,7 +1098,13 @@ class Exporter:
 
         self.args.opset = min(self.args.opset or 19, 19)  # rknn-toolkit expects opset<=19
         f_onnx = self.export_onnx()
-        return onnx2rknn(f_onnx, name=self.args.name, metadata=self.metadata, prefix=prefix)
+        return onnx2rknn(
+            onnx_file=f_onnx,
+            output_dir=str(self.file).replace(self.file.suffix, f"_rknn_model{os.sep}"),
+            name=self.args.name,
+            metadata=self.metadata,
+            prefix=prefix,
+        )
 
     @try_export
     def export_imx(self, prefix=colorstr("IMX:")):
@@ -1120,11 +1144,11 @@ class Exporter:
                 check_apt_requirements(["openjdk-17-jre"])
 
         return torch2imx(
-            self.model,
-            self.file,
-            self.args.conf,
-            self.args.iou,
-            self.args.max_det,
+            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=self.get_int8_calibration_dataloader(prefix),
             prefix=prefix,
diff --git a/ultralytics/nn/backends/coreml.py b/ultralytics/nn/backends/coreml.py
index 9f96c66ec7..d36dfc2e56 100644
--- a/ultralytics/nn/backends/coreml.py
+++ b/ultralytics/nn/backends/coreml.py
@@ -32,7 +32,9 @@ class CoreMLBackend(BaseBackend):
 
         LOGGER.info(f"Loading {weight} for CoreML inference...")
         self.model = ct.models.MLModel(weight)
-        self.dynamic = self.model.get_spec().description.input[0].type.HasField("multiArrayType")
+        spec = self.model.get_spec()
+        self.input_name = spec.description.input[0].name
+        self.dynamic = spec.description.input[0].type.HasField("multiArrayType")
 
         # Load metadata
         self.apply_metadata(dict(self.model.user_defined_metadata))
@@ -50,7 +52,7 @@ class CoreMLBackend(BaseBackend):
         h, w = im.shape[1:3]
 
         im = im.transpose(0, 3, 1, 2) if self.dynamic else Image.fromarray((im[0] * 255).astype("uint8"))
-        y = self.model.predict({"image": im})
+        y = self.model.predict({self.input_name: im})
         if "confidence" in y:  # NMS included
             from ultralytics.utils.ops import xywh2xyxy
 
diff --git a/ultralytics/utils/export/axelera.py b/ultralytics/utils/export/axelera.py
index cdd3b0f390..35cd24f516 100644
--- a/ultralytics/utils/export/axelera.py
+++ b/ultralytics/utils/export/axelera.py
@@ -16,24 +16,26 @@ from ultralytics.utils.checks import check_requirements
 
 def torch2axelera(
     model: torch.nn.Module,
-    file: str | Path,
+    output_dir: Path | str,
     calibration_dataset: torch.utils.data.DataLoader,
     transform_fn: Callable[[Any], np.ndarray],
+    model_name: str = "model",
     metadata: dict | None = None,
     prefix: str = "",
-) -> Path:
+) -> str:
     """Convert a YOLO model to Axelera format.
 
     Args:
         model (torch.nn.Module): Source YOLO model for quantization.
-        file (str | Path): Source model file path used to derive output names.
+        output_dir (Path | str): Directory to save the exported Axelera model.
         calibration_dataset (torch.utils.data.DataLoader): Calibration dataloader for quantization.
         transform_fn (Callable[[Any], np.ndarray]): Calibration preprocessing transform function.
+        model_name (str, optional): Name for the compiled model. Defaults to "model".
         metadata (dict | None, optional): Optional metadata to save as YAML. Defaults to None.
         prefix (str, optional): Prefix for log messages. Defaults to "".
 
     Returns:
-        (Path): Path to exported Axelera model directory.
+        (str): Path to exported Axelera model directory.
     """
     prev_protobuf = os.environ.get("PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION")
     os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
@@ -51,10 +53,8 @@ def torch2axelera(
 
     LOGGER.info(f"\n{prefix} starting export with Axelera compiler...")
 
-    file = Path(file)
-    model_name = file.stem
-    export_path = Path(f"{model_name}_axelera_model")
-    export_path.mkdir(exist_ok=True)
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
 
     axelera_model_metadata = extract_ultralytics_metadata(model)
     config = CompilerConfig(
@@ -71,22 +71,22 @@ def torch2axelera(
         config=config,
         transform_fn=transform_fn,
     )
-    compiler.compile(model=qmodel, config=config, output_dir=export_path)
+    compiler.compile(model=qmodel, config=config, output_dir=output_dir)
 
     for artifact in [f"{model_name}.axm", "compiler_config_final.toml"]:
         artifact_path = Path(artifact)
         if artifact_path.exists():
-            artifact_path.replace(export_path / artifact_path.name)
+            artifact_path.replace(output_dir / artifact_path.name)
 
     # Remove intermediate compiler artifacts, keeping only the compiled model and config.
     keep_suffixes = {".axm"}
     keep_names = {"compiler_config_final.toml", "metadata.yaml"}
-    for f in export_path.iterdir():
+    for f in output_dir.iterdir():
         if f.is_file() and f.suffix not in keep_suffixes and f.name not in keep_names:
             f.unlink()
 
     if metadata is not None:
-        YAML.save(export_path / "metadata.yaml", metadata)
+        YAML.save(output_dir / "metadata.yaml", metadata)
 
     # Restore original PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION value
     if prev_protobuf is None:
@@ -94,4 +94,4 @@ def torch2axelera(
     else:
         os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = prev_protobuf
 
-    return export_path
+    return str(output_dir)
diff --git a/ultralytics/utils/export/coreml.py b/ultralytics/utils/export/coreml.py
index 12c19dd129..6c6c72f15f 100644
--- a/ultralytics/utils/export/coreml.py
+++ b/ultralytics/utils/export/coreml.py
@@ -46,7 +46,7 @@ class IOSDetectModel(nn.Module):
 
 def pipeline_coreml(
     model: Any,
-    output_shape: tuple,
+    output_shape: tuple[int, ...],
     metadata: dict,
     mlmodel: bool = False,
     iou: float = 0.45,
@@ -59,7 +59,7 @@ def pipeline_coreml(
 
     Args:
         model: CoreML model.
-        output_shape (tuple): Output shape tuple from the exporter.
+        output_shape (tuple[int, ...]): Output shape tuple from the exporter.
         metadata (dict): Model metadata.
         mlmodel (bool): Whether the model is an MLModel (vs MLProgram).
         iou (float): IoU threshold for NMS.
@@ -168,13 +168,13 @@ def torch2coreml(
     inputs: list,
     im: torch.Tensor,
     classifier_names: list[str] | None,
-    coreml_file: Path | str | None = None,
+    output_file: Path | str | None = None,
     mlmodel: bool = False,
     half: bool = False,
     int8: bool = False,
     metadata: dict | None = None,
     prefix: str = "",
-):
+) -> Any:
     """Export a PyTorch model to CoreML ``.mlpackage`` or ``.mlmodel`` format.
 
     Args:
@@ -182,7 +182,7 @@ def torch2coreml(
         inputs (list): CoreML input descriptions for the model.
         im (torch.Tensor): Example input tensor for tracing.
         classifier_names (list[str] | None): Class names for classifier config, or None if not a classifier.
-        coreml_file (Path | str | None): Output file path, or None to skip saving.
+        output_file (Path | str | None): Output file path, or None to skip saving.
         mlmodel (bool): Whether to export as ``.mlmodel`` (neural network) instead of ``.mlpackage`` (ML program).
         half (bool): Whether to quantize to FP16.
         int8 (bool): Whether to quantize to INT8.
@@ -229,14 +229,14 @@ def torch2coreml(
     ct_model.version = m.pop("version", "")
     ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items()})
 
-    if coreml_file is not None:
+    if output_file is not None:
         try:
-            ct_model.save(str(coreml_file))  # save *.mlpackage
+            ct_model.save(str(output_file))  # 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."
             )
-            coreml_file = Path(coreml_file).with_suffix(".mlmodel")
-            ct_model.save(str(coreml_file))
+            output_file = Path(output_file).with_suffix(".mlmodel")
+            ct_model.save(str(output_file))
     return ct_model
diff --git a/ultralytics/utils/export/engine.py b/ultralytics/utils/export/engine.py
index a2ce93e458..ab619761c4 100644
--- a/ultralytics/utils/export/engine.py
+++ b/ultralytics/utils/export/engine.py
@@ -44,46 +44,54 @@ def best_onnx_opset(onnx: types.ModuleType, cuda: bool = False) -> int:
 
 @ThreadingLocked()
 def torch2onnx(
-    torch_model: torch.nn.Module,
-    im: torch.Tensor,
-    onnx_file: str,
+    model: torch.nn.Module,
+    im: torch.Tensor | tuple[torch.Tensor, ...],
+    output_file: Path | str,
     opset: int = 14,
-    input_names: list[str] = ["images"],
-    output_names: list[str] = ["output0"],
-    dynamic: bool | dict = False,
-) -> None:
+    input_names: list[str] | None = None,
+    output_names: list[str] | None = None,
+    dynamic: dict | None = None,
+) -> str:
     """Export a PyTorch model to ONNX format.
 
     Args:
-        torch_model (torch.nn.Module): The PyTorch model to export.
-        im (torch.Tensor): Example input tensor for the model.
-        onnx_file (str): Path to save the exported ONNX file.
+        model (torch.nn.Module): The PyTorch model to export.
+        im (torch.Tensor | tuple[torch.Tensor, ...]): Example input tensor(s) for tracing.
+        output_file (Path | str): Path to save the exported ONNX file.
         opset (int): ONNX opset version to use for export.
-        input_names (list[str]): List of input tensor names.
-        output_names (list[str]): List of output tensor names.
-        dynamic (bool | dict, optional): Whether to enable dynamic axes.
+        input_names (list[str] | None): List of input tensor names. Defaults to ``["images"]``.
+        output_names (list[str] | None): List of output tensor names. Defaults to ``["output0"]``.
+        dynamic (dict | None): Dictionary specifying dynamic axes for inputs and outputs.
+
+    Returns:
+        (str): Path to the exported ONNX file.
 
     Notes:
         Setting `do_constant_folding=True` may cause issues with DNN inference for torch>=1.12.
     """
+    if input_names is None:
+        input_names = ["images"]
+    if output_names is None:
+        output_names = ["output0"]
     kwargs = {"dynamo": False} if TORCH_2_4 else {}
     torch.onnx.export(
-        torch_model,
+        model,
         im,
-        onnx_file,
+        output_file,
         verbose=False,
         opset_version=opset,
         do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
         input_names=input_names,
         output_names=output_names,
-        dynamic_axes=dynamic or None,
+        dynamic_axes=dynamic,
         **kwargs,
     )
+    return str(output_file)
 
 
 def onnx2engine(
     onnx_file: str,
-    engine_file: str | None = None,
+    output_file: Path | str | None = None,
     workspace: int | None = None,
     half: bool = False,
     int8: bool = False,
@@ -94,12 +102,12 @@ def onnx2engine(
     metadata: dict | None = None,
     verbose: bool = False,
     prefix: str = "",
-) -> None:
+) -> str:
     """Export a YOLO model to TensorRT engine format.
 
     Args:
         onnx_file (str): Path to the ONNX file to be converted.
-        engine_file (str | None): Path to save the generated TensorRT engine file.
+        output_file (Path | str | None): Path to save the generated TensorRT engine file.
         workspace (int | None): Workspace size in GB for TensorRT.
         half (bool, optional): Enable FP16 precision.
         int8 (bool, optional): Enable INT8 precision.
@@ -111,6 +119,9 @@ def onnx2engine(
         verbose (bool, optional): Enable verbose logging.
         prefix (str, optional): Prefix for log messages.
 
+    Returns:
+        (str): Path to the exported engine file.
+
     Raises:
         ValueError: If DLA is enabled on non-Jetson devices or required precision is not set.
         RuntimeError: If the ONNX file cannot be parsed.
@@ -122,7 +133,7 @@ def onnx2engine(
     """
     import tensorrt as trt
 
-    engine_file = engine_file or Path(onnx_file).with_suffix(".engine")
+    output_file = output_file or Path(onnx_file).with_suffix(".engine")
 
     logger = trt.Logger(trt.Logger.INFO)
     if verbose:
@@ -178,7 +189,7 @@ def onnx2engine(
         if int8 and not is_trt10:  # deprecated in TensorRT 10, causes internal errors
             config.set_calibration_profile(profile)
 
-    LOGGER.info(f"{prefix} building {'INT8' if int8 else 'FP' + ('16' if half else '32')} engine as {engine_file}")
+    LOGGER.info(f"{prefix} building {'INT8' if int8 else 'FP' + ('16' if half else '32')} engine as {output_file}")
     if int8:
         config.set_flag(trt.BuilderFlag.INT8)
         config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED
@@ -263,16 +274,17 @@ def onnx2engine(
         engine = builder.build_serialized_network(network, config)
         if engine is None:
             raise RuntimeError("TensorRT engine build failed, check logs for errors")
-        with open(engine_file, "wb") as t:
+        with open(output_file, "wb") as t:
             if metadata is not None:
                 meta = json.dumps(metadata)
                 t.write(len(meta).to_bytes(4, byteorder="little", signed=True))
                 t.write(meta.encode())
             t.write(engine)
     else:
-        with builder.build_engine(network, config) as engine, open(engine_file, "wb") as t:
+        with builder.build_engine(network, config) as engine, open(output_file, "wb") as t:
             if metadata is not None:
                 meta = json.dumps(metadata)
                 t.write(len(meta).to_bytes(4, byteorder="little", signed=True))
                 t.write(meta.encode())
             t.write(engine.serialize())
+    return str(output_file)
diff --git a/ultralytics/utils/export/executorch.py b/ultralytics/utils/export/executorch.py
index 5606be8acc..15e00805b9 100644
--- a/ultralytics/utils/export/executorch.py
+++ b/ultralytics/utils/export/executorch.py
@@ -39,8 +39,8 @@ def _executorch_kpts_decode(self, kpts: torch.Tensor, is_pose26: bool = False) -
 
 def torch2executorch(
     model: torch.nn.Module,
-    file: Path | str,
-    sample_input: torch.Tensor,
+    im: torch.Tensor,
+    output_dir: Path | str,
     metadata: dict | None = None,
     prefix: str = "",
 ) -> str:
@@ -48,8 +48,8 @@ def torch2executorch(
 
     Args:
         model (torch.nn.Module): The PyTorch model to export.
-        file (Path | str): Source model file path used to derive output names.
-        sample_input (torch.Tensor): Example input tensor for tracing/export.
+        im (torch.Tensor): Example input tensor for tracing/export.
+        output_dir (Path | str): Directory to save the exported ExecuTorch model.
         metadata (dict | None, optional): Optional metadata to save as YAML.
         prefix (str, optional): Prefix for log messages.
 
@@ -62,13 +62,12 @@ def torch2executorch(
 
     LOGGER.info(f"\n{prefix} starting export with ExecuTorch {executorch_version.__version__}...")
 
-    file = Path(file)
-    output_dir = Path(str(file).replace(file.suffix, "_executorch_model"))
+    output_dir = Path(output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
-    pte_file = output_dir / file.with_suffix(".pte").name
+    pte_file = output_dir / "model.pte"
     et_program = to_edge_transform_and_lower(
-        torch.export.export(model, (sample_input,)),
+        torch.export.export(model, (im,)),
         partitioner=[XnnpackPartitioner()],
     ).to_executorch()
     pte_file.write_bytes(et_program.buffer)
diff --git a/ultralytics/utils/export/imx.py b/ultralytics/utils/export/imx.py
index ba8e462d71..21689891fa 100644
--- a/ultralytics/utils/export/imx.py
+++ b/ultralytics/utils/export/imx.py
@@ -203,7 +203,7 @@ class NMSWrapper(torch.nn.Module):
 
 def torch2imx(
     model: torch.nn.Module,
-    file: Path | str,
+    output_dir: Path | str,
     conf: float,
     iou: float,
     max_det: int,
@@ -211,7 +211,7 @@ def torch2imx(
     gptq: bool = False,
     dataset=None,
     prefix: str = "",
-):
+) -> str:
     """Export YOLO model to IMX format for deployment on Sony IMX500 devices.
 
     This function quantizes a YOLO model using Model Compression Toolkit (MCT) and exports it to IMX format compatible
@@ -220,7 +220,7 @@ def torch2imx(
 
     Args:
         model (torch.nn.Module): The YOLO model to export. Must be YOLOv8n or YOLO11n.
-        file (Path | str): Output file path for the exported model.
+        output_dir (Path | str): Directory to save the exported IMX model.
         conf (float): Confidence threshold for NMS post-processing.
         iou (float): IoU threshold for NMS post-processing.
         max_det (int): Maximum number of detections to return.
@@ -231,7 +231,7 @@ def torch2imx(
         prefix (str, optional): Logging prefix string. Defaults to "".
 
     Returns:
-        (Path): Path to the exported IMX model directory.
+        (str): Path to the exported IMX model directory.
 
     Raises:
         ValueError: If the model is not a supported YOLOv8n or YOLO11n variant.
@@ -239,7 +239,7 @@ def torch2imx(
     Examples:
         >>> from ultralytics import YOLO
         >>> model = YOLO("yolo11n.pt")
-        >>> path = torch2imx(model, "model.imx", conf=0.25, iou=0.7, max_det=300)
+        >>> path = torch2imx(model, "output_dir/", conf=0.25, iou=0.7, max_det=300)
 
     Notes:
         - Requires model_compression_toolkit, onnx, edgemdt_tpc, and edge-mdt-cl packages
@@ -309,9 +309,9 @@ def torch2imx(
             task=model.task,
         )
 
-    f = Path(str(file).replace(file.suffix, "_imx_model"))
-    f.mkdir(exist_ok=True)
-    onnx_model = f / Path(str(file.name).replace(file.suffix, "_imx.onnx"))  # js dir
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    onnx_model = output_dir / "model_imx.onnx"
 
     with onnx_export_patch():
         mct.exporter.pytorch_export_model(
@@ -319,7 +319,7 @@ def torch2imx(
         )
 
     model_onnx = onnx.load(onnx_model)  # load onnx model
-    for k, v in metadata.items():
+    for k, v in (metadata or {}).items():
         meta = model_onnx.metadata_props.add()
         meta.key, meta.value = k, str(v)
 
@@ -334,12 +334,12 @@ def torch2imx(
         raise FileNotFoundError("imxconv-pt not found. Install with: pip install imx500-converter[pt]")
 
     subprocess.run(
-        [str(imxconv), "-i", str(onnx_model), "-o", str(f), "--no-input-persistency", "--overwrite-output"],
+        [str(imxconv), "-i", str(onnx_model), "-o", str(output_dir), "--no-input-persistency", "--overwrite-output"],
         check=True,
     )
 
     # Needed for imx models.
-    with open(f / "labels.txt", "w", encoding="utf-8") as file:
-        file.writelines([f"{name}\n" for _, name in model.names.items()])
+    with open(output_dir / "labels.txt", "w", encoding="utf-8") as labels_file:
+        labels_file.writelines([f"{name}\n" for _, name in model.names.items()])
 
-    return f
+    return str(output_dir)
diff --git a/ultralytics/utils/export/mnn.py b/ultralytics/utils/export/mnn.py
index 3cebf890ec..0142f83385 100644
--- a/ultralytics/utils/export/mnn.py
+++ b/ultralytics/utils/export/mnn.py
@@ -9,8 +9,8 @@ from ultralytics.utils import LOGGER
 
 
 def onnx2mnn(
-    f_onnx: str,
-    file: Path | str,
+    onnx_file: str,
+    output_file: Path | str,
     half: bool = False,
     int8: bool = False,
     metadata: dict | None = None,
@@ -19,8 +19,8 @@ def onnx2mnn(
     """Convert an ONNX model to MNN format.
 
     Args:
-        f_onnx (str): Path to the source ONNX file.
-        file (Path | str): Source model path used to derive the output ``.mnn`` path.
+        onnx_file (str): Path to the source ONNX file.
+        output_file (Path | str): Path to save the exported MNN model.
         half (bool): Whether to enable FP16 conversion.
         int8 (bool): Whether to enable INT8 weight quantization.
         metadata (dict | None): Optional metadata embedded via ``--bizCode``.
@@ -33,23 +33,31 @@ def onnx2mnn(
     from ultralytics.utils.torch_utils import TORCH_1_10
 
     assert TORCH_1_10, "MNN export requires torch>=1.10.0 to avoid segmentation faults"
-    assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}"
+    assert Path(onnx_file).exists(), f"failed to export ONNX file: {onnx_file}"
 
     check_requirements("MNN>=2.9.6")
     import MNN
     from MNN.tools import mnnconvert
 
     LOGGER.info(f"\n{prefix} starting export with MNN {MNN.version()}...")
-    file = Path(file)
-    f = str(file.with_suffix(".mnn"))  # MNN model file
-    mnn_args = ["", "-f", "ONNX", "--modelFile", f_onnx, "--MNNModel", f, "--bizCode", json.dumps(metadata or {})]
+    mnn_args = [
+        "",
+        "-f",
+        "ONNX",
+        "--modelFile",
+        onnx_file,
+        "--MNNModel",
+        str(output_file),
+        "--bizCode",
+        json.dumps(metadata or {}),
+    ]
     if int8:
         mnn_args.extend(("--weightQuantBits", "8"))
     if half:
         mnn_args.append("--fp16")
     mnnconvert.convert(mnn_args)
     # Remove scratch file created during model convert optimize
-    convert_scratch = file.parent / ".__convert_external_data.bin"
+    convert_scratch = Path(output_file).parent / ".__convert_external_data.bin"
     if convert_scratch.exists():
         convert_scratch.unlink()
-    return f
+    return str(output_file)
diff --git a/ultralytics/utils/export/ncnn.py b/ultralytics/utils/export/ncnn.py
index 28d33517d1..177e267ee7 100644
--- a/ultralytics/utils/export/ncnn.py
+++ b/ultralytics/utils/export/ncnn.py
@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import os
 from pathlib import Path
 
 import torch
@@ -13,7 +12,7 @@ from ultralytics.utils import LOGGER, YAML
 def torch2ncnn(
     model: torch.nn.Module,
     im: torch.Tensor,
-    file: Path | str,
+    output_dir: Path | str,
     half: bool = False,
     metadata: dict | None = None,
     device: torch.device | None = None,
@@ -24,7 +23,7 @@ def torch2ncnn(
     Args:
         model (torch.nn.Module): The PyTorch model to export.
         im (torch.Tensor): Example input tensor for tracing.
-        file (Path | str): Source model path used to derive the output directory.
+        output_dir (Path | str): Directory to save the exported NCNN model.
         half (bool): Whether to enable FP16 export.
         metadata (dict | None): Optional metadata saved as ``metadata.yaml``.
         device (torch.device | None): Device the model lives on.
@@ -41,23 +40,22 @@ def torch2ncnn(
     import pnnx
 
     LOGGER.info(f"\n{prefix} starting export with NCNN {ncnn.__version__} and PNNX {pnnx.__version__}...")
-    file = Path(file)
-    f = Path(str(file).replace(file.suffix, f"_ncnn_model{os.sep}"))
+    output_dir = Path(output_dir)
 
     ncnn_args = dict(
-        ncnnparam=(f / "model.ncnn.param").as_posix(),
-        ncnnbin=(f / "model.ncnn.bin").as_posix(),
-        ncnnpy=(f / "model_ncnn.py").as_posix(),
+        ncnnparam=(output_dir / "model.ncnn.param").as_posix(),
+        ncnnbin=(output_dir / "model.ncnn.bin").as_posix(),
+        ncnnpy=(output_dir / "model_ncnn.py").as_posix(),
     )
     pnnx_args = dict(
-        ptpath=(f / "model.pt").as_posix(),
-        pnnxparam=(f / "model.pnnx.param").as_posix(),
-        pnnxbin=(f / "model.pnnx.bin").as_posix(),
-        pnnxpy=(f / "model_pnnx.py").as_posix(),
-        pnnxonnx=(f / "model.pnnx.onnx").as_posix(),
+        ptpath=(output_dir / "model.pt").as_posix(),
+        pnnxparam=(output_dir / "model.pnnx.param").as_posix(),
+        pnnxbin=(output_dir / "model.pnnx.bin").as_posix(),
+        pnnxpy=(output_dir / "model_pnnx.py").as_posix(),
+        pnnxonnx=(output_dir / "model.pnnx.onnx").as_posix(),
     )
 
-    f.mkdir(exist_ok=True)  # make ncnn_model directory
+    output_dir.mkdir(parents=True, exist_ok=True)  # make ncnn_model directory
     device_type = device.type if device is not None else "cpu"
     pnnx.export(model, inputs=im, **ncnn_args, **pnnx_args, fp16=half, device=device_type)
 
@@ -65,5 +63,5 @@ def torch2ncnn(
         Path(f_debug).unlink(missing_ok=True)
 
     if metadata:
-        YAML.save(f / "metadata.yaml", metadata)  # add metadata.yaml
-    return str(f)
+        YAML.save(output_dir / "metadata.yaml", metadata)  # add metadata.yaml
+    return str(output_dir)
diff --git a/ultralytics/utils/export/openvino.py b/ultralytics/utils/export/openvino.py
index c804a982d5..d15f07dbda 100644
--- a/ultralytics/utils/export/openvino.py
+++ b/ultralytics/utils/export/openvino.py
@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import os
 from pathlib import Path
 from typing import Any
 
@@ -13,25 +12,25 @@ from ultralytics.utils import LOGGER
 
 def torch2openvino(
     model: torch.nn.Module,
-    im: torch.Tensor,
-    file: Path | str | None = None,
+    im: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, ...],
+    output_dir: Path | str | None = None,
     dynamic: bool = False,
     half: bool = False,
     int8: bool = False,
     calibration_dataset: Any | None = None,
     ignored_scope: dict | None = None,
     prefix: str = "",
-) -> str:
+) -> Any:
     """Export a PyTorch model to OpenVINO format with optional INT8 quantization.
 
     Args:
         model (torch.nn.Module): The model to export (may be NMS-wrapped).
-        im (torch.Tensor): Example input tensor.
-        file (Path | str | None): Source model path used to derive output directory.
+        im (torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor, ...]): Example input tensor(s) for tracing.
+        output_dir (Path | str | None): Directory to save the exported OpenVINO model.
         dynamic (bool): Whether to use dynamic input shapes.
         half (bool): Whether to compress to FP16.
         int8 (bool): Whether to apply INT8 quantization.
-        calibration_dataset (nn.Dataset): Dataset for nncf.Dataset (required when ``int8=True``).
+        calibration_dataset (nncf.Dataset | None): Dataset for INT8 calibration (required when ``int8=True``).
         ignored_scope (dict | None): Kwargs passed to ``nncf.IgnoredScope`` for head patterns.
         prefix (str): Prefix for log messages.
 
@@ -42,7 +41,8 @@ def torch2openvino(
 
     LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...")
 
-    ov_model = ov.convert_model(model, input=None if dynamic else [im.shape], example_input=im)
+    input_shape = [i.shape for i in im] if isinstance(im, (list, tuple)) else im.shape
+    ov_model = ov.convert_model(model, input=None if dynamic else input_shape, example_input=im)
     if int8:
         import nncf
 
@@ -53,10 +53,9 @@ def torch2openvino(
             ignored_scope=ignored_scope,
         )
 
-    if file is not None:
-        file = Path(file)
-        suffix = f"_{'int8_' if int8 else ''}openvino_model{os.sep}"
-        f = str(file).replace(file.suffix, suffix)
-        f_ov = str(Path(f) / file.with_suffix(".xml").name)
-        ov.save_model(ov_model, f_ov, compress_to_fp16=half)
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        output_file = output_dir / "model.xml"
+        ov.save_model(ov_model, output_file, compress_to_fp16=half)
     return ov_model
diff --git a/ultralytics/utils/export/paddle.py b/ultralytics/utils/export/paddle.py
index 841a01a919..7d3f467019 100644
--- a/ultralytics/utils/export/paddle.py
+++ b/ultralytics/utils/export/paddle.py
@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import os
 from pathlib import Path
 
 import torch
@@ -13,7 +12,7 @@ from ultralytics.utils import ARM64, IS_JETSON, LOGGER, YAML
 def torch2paddle(
     model: torch.nn.Module,
     im: torch.Tensor,
-    file: Path | str,
+    output_dir: Path | str,
     metadata: dict | None = None,
     prefix: str = "",
 ) -> str:
@@ -22,7 +21,7 @@ def torch2paddle(
     Args:
         model (torch.nn.Module): The PyTorch model to export.
         im (torch.Tensor): Example input tensor for tracing.
-        file (Path | str): Source model path used to derive the output directory.
+        output_dir (Path | str): Directory to save the exported PaddlePaddle model.
         metadata (dict | None): Optional metadata saved as ``metadata.yaml``.
         prefix (str): Prefix for log messages.
 
@@ -47,10 +46,8 @@ def torch2paddle(
     from x2paddle.convert import pytorch2paddle
 
     LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...")
-    file = Path(file)
-    f = str(file).replace(file.suffix, f"_paddle_model{os.sep}")
 
-    pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im])  # export
+    pytorch2paddle(module=model, save_dir=output_dir, jit_type="trace", input_examples=[im])  # export
     if metadata:
-        YAML.save(Path(f) / "metadata.yaml", metadata)  # add metadata.yaml
-    return f
+        YAML.save(Path(output_dir) / "metadata.yaml", metadata)  # add metadata.yaml
+    return str(output_dir)
diff --git a/ultralytics/utils/export/rknn.py b/ultralytics/utils/export/rknn.py
index d1aacec58b..1c8da6ba9d 100644
--- a/ultralytics/utils/export/rknn.py
+++ b/ultralytics/utils/export/rknn.py
@@ -8,21 +8,23 @@ from ultralytics.utils import IS_COLAB, LOGGER, YAML
 
 
 def onnx2rknn(
-    f_onnx: str,
+    onnx_file: str,
+    output_dir: Path | str,
     name: str = "rk3588",
     metadata: dict | None = None,
     prefix: str = "",
-) -> Path:
+) -> str:
     """Export an ONNX model to RKNN format for Rockchip NPUs.
 
     Args:
-        f_onnx (str): Path to the source ONNX file (already exported, opset <=19).
+        onnx_file (str): Path to the source ONNX file (already exported, opset <=19).
+        output_dir (Path | str): Directory to save the exported RKNN model.
         name (str): Target platform name (e.g. ``"rk3588"``).
         metadata (dict | None): Metadata saved as ``metadata.yaml``.
         prefix (str): Prefix for log messages.
 
     Returns:
-        (Path): Path to the exported ``_rknn_model`` directory.
+        (str): Path to the exported ``_rknn_model`` directory.
     """
     from ultralytics.utils.checks import check_requirements
 
@@ -38,14 +40,14 @@ def onnx2rknn(
 
     from rknn.api import RKNN
 
-    export_path = Path(f"{Path(f_onnx).stem}_rknn_model")
-    export_path.mkdir(exist_ok=True)
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
 
     rknn = RKNN(verbose=False)
     rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform=name)
-    rknn.load_onnx(model=f_onnx)
+    rknn.load_onnx(model=onnx_file)
     rknn.build(do_quantization=False)  # TODO: Add quantization support
-    rknn.export_rknn(str(export_path / f"{Path(f_onnx).stem}-{name}.rknn"))
+    rknn.export_rknn(str(output_dir / f"{Path(onnx_file).stem}-{name}.rknn"))
     if metadata:
-        YAML.save(export_path / "metadata.yaml", metadata)
-    return export_path
+        YAML.save(output_dir / "metadata.yaml", metadata)
+    return str(output_dir)
diff --git a/ultralytics/utils/export/tensorflow.py b/ultralytics/utils/export/tensorflow.py
index c70a709875..322edb76c9 100644
--- a/ultralytics/utils/export/tensorflow.py
+++ b/ultralytics/utils/export/tensorflow.py
@@ -59,19 +59,19 @@ def _tf_kpts_decode(self, kpts: torch.Tensor, is_pose26: bool = False) -> torch.
 
 def onnx2saved_model(
     onnx_file: str,
-    output_dir: Path,
+    output_dir: Path | str,
     int8: bool = False,
-    images: np.ndarray = None,
+    images: np.ndarray | None = None,
     disable_group_convolution: bool = False,
-    prefix="",
+    prefix: str = "",
 ):
     """Convert an ONNX model to TensorFlow SavedModel format using onnx2tf.
 
     Args:
         onnx_file (str): ONNX file path.
-        output_dir (Path): Output directory path for the SavedModel.
+        output_dir (Path | str): Output directory path for the SavedModel.
         int8 (bool, optional): Enable INT8 quantization. Defaults to False.
-        images (np.ndarray, optional): Calibration images for INT8 quantization in BHWC format.
+        images (np.ndarray | None, optional): Calibration images for INT8 quantization in BHWC format.
         disable_group_convolution (bool, optional): Disable group convolution optimization. Defaults to False.
         prefix (str, optional): Logging prefix. Defaults to "".
 
@@ -82,6 +82,7 @@ def onnx2saved_model(
         - Requires onnx2tf package. Downloads calibration data if INT8 quantization is enabled.
         - Removes temporary files and renames quantized models after conversion.
     """
+    output_dir = Path(output_dir)
     # Pre-download calibration file to fix https://github.com/PINTO0309/onnx2tf/issues/545
     onnx2tf_file = Path("calibration_image_sample_data_20x128x128x3_float32.npy")
     if not onnx2tf_file.exists():
@@ -118,7 +119,7 @@ def onnx2saved_model(
         verbosity="error",  # note INT8-FP16 activation bug https://github.com/ultralytics/ultralytics/issues/15873
         output_integer_quantized_tflite=int8,
         custom_input_op_name_np_data_path=np_data,
-        enable_batchmatmul_unfold=True and not int8,  # fix lower no. of detected objects on GPU delegate
+        enable_batchmatmul_unfold=not int8,  # fix lower no. of detected objects on GPU delegate
         output_signaturedefs=True,  # fix error with Attention block group convolution
         disable_group_convolution=disable_group_convolution,  # fix error with group convolution
     )
@@ -133,14 +134,17 @@ def onnx2saved_model(
     return keras_model
 
 
-def keras2pb(keras_model, file: Path, prefix=""):
+def keras2pb(keras_model, output_file: Path | str, prefix: str = "") -> str:
     """Convert a Keras model to TensorFlow GraphDef (.pb) format.
 
     Args:
         keras_model (keras.Model): Keras model to convert to frozen graph format.
-        file (Path): Output file path (suffix will be changed to .pb).
+        output_file (Path | str): Output file path (suffix will be changed to .pb).
         prefix (str, optional): Logging prefix. Defaults to "".
 
+    Returns:
+        (str): Path to the exported ``.pb`` file.
+
     Notes:
         Creates a frozen graph by converting variables to constants for inference optimization.
     """
@@ -152,10 +156,14 @@ def keras2pb(keras_model, file: Path, prefix=""):
     m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
     frozen_func = convert_variables_to_constants_v2(m)
     frozen_func.graph.as_graph_def()
-    tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(file.parent), name=file.name, as_text=False)
+    output_file = Path(output_file)
+    tf.io.write_graph(
+        graph_or_graph_def=frozen_func.graph, logdir=str(output_file.parent), name=output_file.name, as_text=False
+    )
+    return str(output_file)
 
 
-def tflite2edgetpu(tflite_file: str | Path, output_dir: str | Path, prefix: str = ""):
+def tflite2edgetpu(tflite_file: str | Path, output_dir: str | Path, prefix: str = "") -> str:
     """Convert a TensorFlow Lite model to Edge TPU format using the Edge TPU compiler.
 
     Args:
@@ -163,6 +171,9 @@ def tflite2edgetpu(tflite_file: str | Path, output_dir: str | Path, prefix: str
         output_dir (str | Path): Output directory path for the compiled Edge TPU model.
         prefix (str, optional): Logging prefix. Defaults to "".
 
+    Returns:
+        (str): Path to the exported Edge TPU model file.
+
     Notes:
         Requires the Edge TPU compiler to be installed. The function compiles the TFLite model
         for optimal performance on Google's Edge TPU hardware accelerator.
@@ -180,9 +191,10 @@ def tflite2edgetpu(tflite_file: str | Path, output_dir: str | Path, prefix: str
     )
     LOGGER.info(f"{prefix} running '{cmd}'")
     subprocess.run(cmd, shell=True)
+    return str(Path(output_dir) / f"{Path(tflite_file).stem}_edgetpu.tflite")
 
 
-def pb2tfjs(pb_file: str, output_dir: str, half: bool = False, int8: bool = False, prefix: str = ""):
+def pb2tfjs(pb_file: str, output_dir: str, half: bool = False, int8: bool = False, prefix: str = "") -> str:
     """Convert a TensorFlow GraphDef (.pb) model to TensorFlow.js format.
 
     Args:
@@ -192,6 +204,9 @@ def pb2tfjs(pb_file: str, output_dir: str, half: bool = False, int8: bool = Fals
         int8 (bool, optional): Enable INT8 quantization. Defaults to False.
         prefix (str, optional): Logging prefix. Defaults to "".
 
+    Returns:
+        (str): Path to the exported TensorFlow.js model directory.
+
     Notes:
         Requires tensorflowjs package. Uses tensorflowjs_converter command-line tool for conversion.
         Handles spaces in file paths and warns if output directory contains spaces.
@@ -204,8 +219,8 @@ def pb2tfjs(pb_file: str, output_dir: str, half: bool = False, int8: bool = Fals
     LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...")
 
     gd = tf.Graph().as_graph_def()  # TF GraphDef
-    with open(pb_file, "rb") as file:
-        gd.ParseFromString(file.read())
+    with open(pb_file, "rb") as f:
+        gd.ParseFromString(f.read())
     outputs = ",".join(gd_outputs(gd))
     LOGGER.info(f"\n{prefix} output node names: {outputs}")
 
@@ -220,6 +235,7 @@ def pb2tfjs(pb_file: str, output_dir: str, half: bool = False, int8: bool = Fals
 
     if " " in output_dir:
         LOGGER.warning(f"{prefix} your model may not work correctly with spaces in path '{output_dir}'.")
+    return str(output_dir)
 
 
 def gd_outputs(gd):
diff --git a/ultralytics/utils/export/torchscript.py b/ultralytics/utils/export/torchscript.py
index 6805eac217..077b69f075 100644
--- a/ultralytics/utils/export/torchscript.py
+++ b/ultralytics/utils/export/torchscript.py
@@ -13,35 +13,34 @@ from ultralytics.utils import LOGGER, TORCH_VERSION
 def torch2torchscript(
     model: torch.nn.Module,
     im: torch.Tensor,
-    file: Path | str,
+    output_file: Path | str,
     optimize: bool = False,
     metadata: dict | None = None,
     prefix: str = "",
-) -> Path:
+) -> str:
     """Export a PyTorch model to TorchScript format.
 
     Args:
         model (torch.nn.Module): The PyTorch model to export (may be NMS-wrapped).
         im (torch.Tensor): Example input tensor for tracing.
-        file (Path | str): Source model file path used to derive output path.
+        output_file (Path | str): Path to save the exported TorchScript model.
         optimize (bool): Whether to optimize for mobile deployment.
         metadata (dict | None): Optional metadata to embed in the TorchScript archive.
         prefix (str): Prefix for log messages.
 
     Returns:
-        (Path): Path to the exported ``.torchscript`` file.
+        (str): Path to the exported ``.torchscript`` file.
     """
     LOGGER.info(f"\n{prefix} starting export with torch {TORCH_VERSION}...")
-    file = Path(file)
-    f = file.with_suffix(".torchscript")
 
+    output_file = str(output_file)
     ts = torch.jit.trace(model, im, strict=False)
     extra_files = {"config.txt": json.dumps(metadata or {})}  # torch._C.ExtraFilesMap()
     if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
         LOGGER.info(f"{prefix} optimizing for mobile...")
         from torch.utils.mobile_optimizer import optimize_for_mobile
 
-        optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
+        optimize_for_mobile(ts)._save_for_lite_interpreter(output_file, _extra_files=extra_files)
     else:
-        ts.save(str(f), _extra_files=extra_files)
-    return f
+        ts.save(output_file, _extra_files=extra_files)
+    return output_file