RF-DETR XLarge

License Notice

This model is licensed under the Platform Model License (PML-1.0) and requires pip install rfdetr[plus]. See the rfdetr_plus repository for license details.

Bases: RFDETR

Source code in .venv/lib/python3.10/site-packages/rfdetr_plus/models/detection.py

class RFDETRXLarge(RFDETR):
    size: Literal["rfdetr-xlarge"] = "rfdetr-xlarge"

    def __init__(self, **kwargs: Any) -> None:
        kwargs.pop("accept_platform_model_license", None)
        super().__init__(**kwargs)

    def get_model_config(self, **kwargs: Any) -> RFDETRXLargeConfig:
        return RFDETRXLargeConfig(**kwargs)

    def get_train_config(self, **kwargs: Any) -> TrainConfig:
        return TrainConfig(**kwargs)

Attributes

class_names property

Retrieve the class names supported by the loaded model.

Returns:

Type	Description
list[str]	A list of class name strings, 0-indexed. When no custom class
list[str]	names are embedded in the checkpoint, returns the standard 80
list[str]	COCO class names.

Functions

deploy_to_roboflow(workspace, project_id, version, api_key=None, size=None)

Deploy the trained RF-DETR model to Roboflow.

Deploying with Roboflow will create a Serverless API to which you can make requests.

You can also download weights into a Roboflow Inference deployment for use in Roboflow Workflows and on-device deployment.

Parameters:

Name	Type	Description	Default
workspace	str	The name of the Roboflow workspace to deploy to.	required
project_id	str	The project ID to which the model will be deployed.	required
version	int | str	The project version to which the model will be deployed.	required
api_key	str | None	Your Roboflow API key. If not provided, it will be read from the environment variable ROBOFLOW_API_KEY.	None
size	str | None	The size of the model to deploy. If not provided, it will default to the size of the model being trained (e.g., "rfdetr-base", "rfdetr-large", etc.).	None

Raises:

Type	Description
ValueError	If the api_key is not provided and not found in the environment variable ROBOFLOW_API_KEY, or if the size is not set for custom architectures.

Source code in src/rfdetr/detr.py

def deploy_to_roboflow(
    self,
    workspace: str,
    project_id: str,
    version: int | str,
    api_key: str | None = None,
    size: str | None = None,
) -> None:
    """Deploy the trained RF-DETR model to Roboflow.

    Deploying with Roboflow will create a Serverless API to which you can make requests.

    You can also download weights into a Roboflow Inference deployment for use in
    Roboflow Workflows and on-device deployment.

    Args:
        workspace: The name of the Roboflow workspace to deploy to.
        project_id: The project ID to which the model will be deployed.
        version: The project version to which the model will be deployed.
        api_key: Your Roboflow API key. If not provided,
            it will be read from the environment variable `ROBOFLOW_API_KEY`.
        size: The size of the model to deploy. If not provided,
            it will default to the size of the model being trained (e.g., "rfdetr-base", "rfdetr-large", etc.).

    Raises:
        ValueError: If the `api_key` is not provided and not found in the
            environment variable `ROBOFLOW_API_KEY`, or if the `size` is
            not set for custom architectures.

    """
    from roboflow import Roboflow

    if api_key is None:
        api_key = os.getenv("ROBOFLOW_API_KEY")
        if api_key is None:
            raise ValueError("Set api_key=<KEY> in deploy_to_roboflow or export ROBOFLOW_API_KEY=<KEY>")

    rf = Roboflow(api_key=api_key)
    workspace = rf.workspace(workspace)

    if self.size is None and size is None:
        raise ValueError("Must set size for custom architectures")

    size = self.size or size
    with tempfile.TemporaryDirectory(prefix="roboflow_upload_") as tmp_out_dir:
        # Write class_names.txt so the Roboflow upload pipeline can discover
        # the class labels without relying on args.class_names in the checkpoint.
        class_names_path = os.path.join(tmp_out_dir, "class_names.txt")
        with open(class_names_path, "w", encoding="utf-8", newline="\n") as f:
            f.write("\n".join(self.class_names))

        # Also embed class_names in the args namespace so that any code path
        # that loads the checkpoint directly (e.g. roboflow-python's second
        # fallback) can find them.  Mutating the shared SimpleNamespace is
        # intentional here: this mirrors reinitialize_detection_head(), which
        # already mutates args.num_classes in-place.
        args = self.model.args
        if not hasattr(args, "class_names") or args.class_names is None:
            args.class_names = self.class_names

        outpath = os.path.join(tmp_out_dir, "weights.pt")
        torch.save({"model": self.model.model.state_dict(), "args": args}, outpath)
        project = workspace.project(project_id)
        project_version = project.version(version)
        project_version.deploy(model_type=size, model_path=tmp_out_dir, filename="weights.pt")

export(output_dir='output', infer_dir=None, simplify=False, backbone_only=False, opset_version=17, verbose=True, force=False, shape=None, batch_size=1, dynamic_batch=False, patch_size=None, format='onnx', quantization=None, calibration_data=None, max_images=100)

Export the trained model to ONNX or TFLite format.

See the export documentation <https://rfdetr.roboflow.com/learn/export/>_ for more information.

Parameters:

Name	Type	Description	Default
output_dir	str	Directory to write the exported model to.	'output'
infer_dir	str	Optional directory of sample images for dynamic-axes inference.	None
simplify	bool	Deprecated and ignored. Simplification is no longer run.	False
backbone_only	bool	Export only the backbone (feature extractor).	False
opset_version	int	ONNX opset version to target.	17
verbose	bool	Print export progress information.	True
force	bool	Deprecated and ignored.	False
shape	tuple[int, int] | None	(height, width) tuple; defaults to square at model resolution. Both dimensions must be divisible by patch_size * num_windows.	None
batch_size	int	Static batch size to bake into the ONNX graph.	1
dynamic_batch	bool	If True, export with a dynamic batch dimension so the ONNX model accepts variable batch sizes at runtime.	False
patch_size	int | None	Backbone patch size. Defaults to the value stored in model_config.patch_size (typically 14 or 16). When provided explicitly it must match the instantiated model's patch size. Shape divisibility is validated against patch_size * num_windows.	None
format	str	Export format — "onnx" (default) or "tflite". When "tflite" is selected the model is first exported to ONNX then converted to TFLite via onnx2tf. Requires pip install rfdetr[onnx,tflite].	'onnx'
quantization	str | None	TFLite quantization mode (ignored when format="onnx"). One of None, "fp32", "fp16", "int8". None / "fp32" / "fp16" produce FP32 + FP16 .tflite files; "int8" additionally produces an INT8-quantized model.	None
calibration_data	str | ndarray | None	Representative images for INT8 calibration and onnx2tf output validation. Accepts: None — auto-generate random data (sufficient for fp32/fp16; warns for int8). A directory path (str) containing JPEG/PNG images — the converter automatically loads, resizes, and prepares them. This is the simplest approach. A path (str) to a .npy file of shape (N, H, W, 3), dtype float32, values in [0, 1]. A :class:numpy.ndarray with the same format. For INT8 quantization, provide 20–100 representative images from your training/validation set for best accuracy.	None
max_images	int	Maximum number of images to load from a calibration directory. Defaults to 100. Only used when calibration_data is a directory path.	100

Source code in src/rfdetr/detr.py

@deprecated(
    target=True,
    # `simplify` / `force` are retained for API compatibility and treated as no-op.
    args_mapping={"simplify": False, "force": False},
    deprecated_in="1.6",
    remove_in="1.8",
    num_warns=1,
    stream=functools.partial(warnings.warn, category=DeprecationWarning, stacklevel=2),
)
def export(
    self,
    output_dir: str = "output",
    infer_dir: str = None,
    simplify: bool = False,
    backbone_only: bool = False,
    opset_version: int = 17,
    verbose: bool = True,
    force: bool = False,
    shape: tuple[int, int] | None = None,
    batch_size: int = 1,
    dynamic_batch: bool = False,
    patch_size: int | None = None,
    format: str = "onnx",
    quantization: str | None = None,
    calibration_data: str | np.ndarray | None = None,
    max_images: int = 100,
) -> None:
    """Export the trained model to ONNX or TFLite format.

    See the `export documentation <https://rfdetr.roboflow.com/learn/export/>`_
    for more information.

    Args:
        output_dir: Directory to write the exported model to.
        infer_dir: Optional directory of sample images for dynamic-axes inference.
        simplify: Deprecated and ignored. Simplification is no longer run.
        backbone_only: Export only the backbone (feature extractor).
        opset_version: ONNX opset version to target.
        verbose: Print export progress information.
        force: Deprecated and ignored.
        shape: ``(height, width)`` tuple; defaults to square at model resolution.
            Both dimensions must be divisible by ``patch_size * num_windows``.
        batch_size: Static batch size to bake into the ONNX graph.
        dynamic_batch: If True, export with a dynamic batch dimension
            so the ONNX model accepts variable batch sizes at runtime.
        patch_size: Backbone patch size. Defaults to the value stored in
            ``model_config.patch_size`` (typically 14 or 16). When provided
            explicitly it must match the instantiated model's patch size.
            Shape divisibility is validated against ``patch_size * num_windows``.
        format: Export format — ``"onnx"`` (default) or ``"tflite"``.
            When ``"tflite"`` is selected the model is first exported to ONNX
            then converted to TFLite via ``onnx2tf``.  Requires
            ``pip install rfdetr[onnx,tflite]``.
        quantization: TFLite quantization mode (ignored when
            ``format="onnx"``).  One of ``None``, ``"fp32"``, ``"fp16"``,
            ``"int8"``.  ``None`` / ``"fp32"`` / ``"fp16"`` produce FP32 +
            FP16 ``.tflite`` files; ``"int8"`` additionally produces an
            INT8-quantized model.
        calibration_data: Representative images for INT8 calibration
            and ``onnx2tf`` output validation.  Accepts:

            * ``None`` — auto-generate random data (sufficient for
              fp32/fp16; warns for int8).
            * A **directory path** (``str``) containing JPEG/PNG
              images — the converter automatically loads, resizes, and
              prepares them.  This is the simplest approach.
            * A path (``str``) to a ``.npy`` file of shape
              ``(N, H, W, 3)``, dtype float32, values in ``[0, 1]``.
            * A :class:`numpy.ndarray` with the same format.

            For INT8 quantization, provide 20–100 representative
            images from your training/validation set for best accuracy.
        max_images: Maximum number of images to load from a
            calibration directory.  Defaults to ``100``.  Only used
            when *calibration_data* is a directory path.
    """
    logger.info("Exporting model to ONNX format")
    _valid_formats = ("onnx", "tflite")
    if format not in _valid_formats:
        raise ValueError(f"Unsupported export format {format!r}. Choose from: {_valid_formats}")
    try:
        from rfdetr.export.main import export_onnx, make_infer_image
    except ImportError:
        logger.error(
            "It seems some dependencies for ONNX export are missing."
            " Please run `pip install rfdetr[onnx]` and try again.",
        )
        raise

    device = self.model.device
    # deepcopy(self.model.model.to("cpu")) moves the live model to CPU as a
    # side-effect before copying.  The finally block guarantees the original
    # model is restored to its original device even if export or conversion
    # raises an exception (review H1).
    model = deepcopy(self.model.model.to("cpu"))
    model.to(device)
    try:
        os.makedirs(output_dir, exist_ok=True)
        output_dir_path = Path(output_dir)
        patch_size = _resolve_patch_size(patch_size, self.model_config, "export")
        num_windows = getattr(self.model_config, "num_windows", 1)
        if isinstance(num_windows, bool) or not isinstance(num_windows, int) or num_windows <= 0:
            raise ValueError(f"num_windows must be a positive integer, got {num_windows!r}")
        block_size = patch_size * num_windows
        if shape is None:
            shape = (self.model.resolution, self.model.resolution)
            if shape[0] % block_size != 0:
                raise ValueError(
                    f"Model's default resolution ({self.model.resolution}) is not divisible by "
                    f"block_size={block_size} (patch_size={patch_size} * num_windows={num_windows}). "
                    f"Provide an explicit shape divisible by {block_size}.",
                )
        else:
            shape = _validate_shape_dims(shape, block_size, patch_size, num_windows)

        input_tensors = make_infer_image(
            infer_dir, shape, batch_size, device, num_channels=self.model_config.num_channels
        ).to(device)
        input_names = ["input"]
        if backbone_only:
            output_names = ["features"]
        elif self.model_config.segmentation_head:
            output_names = ["dets", "labels", "masks"]
        else:
            output_names = ["dets", "labels"]

        if dynamic_batch:
            dynamic_axes = {name: {0: "batch"} for name in input_names + output_names}
        else:
            dynamic_axes = None
        model.eval()
        with torch.no_grad():
            if backbone_only:
                features = model(input_tensors)
                logger.debug(f"PyTorch inference output shape: {features.shape}")
            elif self.model_config.segmentation_head:
                outputs = model(input_tensors)
                dets = outputs["pred_boxes"]
                labels = outputs["pred_logits"]
                masks = outputs["pred_masks"]
                if isinstance(masks, torch.Tensor):
                    logger.debug(
                        f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}, "
                        f"Masks: {masks.shape}",
                    )
                else:
                    logger.debug(f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}")
            else:
                outputs = model(input_tensors)
                dets = outputs["pred_boxes"]
                labels = outputs["pred_logits"]
                logger.debug(f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}")

        model.cpu()
        input_tensors = input_tensors.cpu()

        output_file = export_onnx(
            output_dir=str(output_dir_path),
            model=model,
            input_names=input_names,
            input_tensors=input_tensors,
            output_names=output_names,
            dynamic_axes=dynamic_axes,
            backbone_only=backbone_only,
            verbose=verbose,
            opset_version=opset_version,
            variant_name=getattr(self, "size", None),
        )

        logger.info(f"Successfully exported ONNX model to: {output_file}")

        if format == "tflite":
            try:
                from rfdetr.export._tflite.converter import export_tflite
            except ImportError:
                logger.error(
                    "It seems some dependencies for TFLite export are missing."
                    " Please run `pip install rfdetr[onnx,tflite]` and try again.",
                )
                raise

            tflite_path = export_tflite(
                onnx_path=output_file,
                output_dir=str(output_dir_path),
                quantization=quantization,
                calibration_data=calibration_data,
                verbosity="info" if verbose else "error",
                max_images=max_images,
                verbose=verbose,
            )
            logger.info(f"Successfully exported TFLite model to: {tflite_path}")

        logger.info("Export completed successfully")
    finally:
        self.model.model = self.model.model.to(device)

from_checkpoint(path, **kwargs) classmethod

Load an RF-DETR model from a training checkpoint, automatically inferring the model class.

The correct subclass is resolved in order of preference:

1. model_name key in the checkpoint (written by the PTL training stack since v1.7.0).
2. pretrain_weights field in the checkpoint's args entry (legacy fallback).

Both legacy argparse.Namespace checkpoints (produced by engine.py) and dict-style checkpoints (produced by the PTL training stack) are supported.

Parameters:

Name	Type	Description	Default
path	str | PathLike[str]	Path to a checkpoint file (e.g. checkpoint_best_total.pth).	required
**kwargs	Any	Additional keyword arguments forwarded to the model constructor (e.g. accept_platform_model_license=True for XLarge / 2XLarge models).	{}

Returns:

Type	Description
RFDETR	An instance of the appropriate :class:RFDETR subclass loaded from
RFDETR	the checkpoint.

Warning

This method calls torch.load with weights_only=False, which unpickles arbitrary Python objects. Only load checkpoints from trusted sources.

Raises:

Type	Description
FileNotFoundError	If path does not exist.
OSError	If path exists but cannot be read.
KeyError	If the checkpoint does not contain an "args" key.
ValueError	If the model class cannot be inferred from model_name or pretrain_weights.

Examples:

>>> model = RFDETR.from_checkpoint("checkpoint_best_total.pth")
>>> model = RFDETRSmall.from_checkpoint("checkpoint_best_total.pth")

Source code in src/rfdetr/detr.py

@classmethod
def from_checkpoint(cls, path: str | os.PathLike[str], **kwargs: Any) -> RFDETR:
    """Load an RF-DETR model from a training checkpoint, automatically
    inferring the model class.

    The correct subclass is resolved in order of preference:

    1. ``model_name`` key in the checkpoint (written by the PTL training
       stack since v1.7.0).
    2. ``pretrain_weights`` field in the checkpoint's ``args`` entry
       (legacy fallback).

    Both legacy ``argparse.Namespace`` checkpoints (produced by
    ``engine.py``) and dict-style checkpoints (produced by the PTL
    training stack) are supported.

    Args:
        path: Path to a checkpoint file (e.g. ``checkpoint_best_total.pth``).
        **kwargs: Additional keyword arguments forwarded to the model
            constructor (e.g. ``accept_platform_model_license=True`` for
            XLarge / 2XLarge models).

    Returns:
        An instance of the appropriate :class:`RFDETR` subclass loaded from
        the checkpoint.

    Warning:
        This method calls ``torch.load`` with ``weights_only=False``, which
        unpickles arbitrary Python objects. Only load checkpoints from
        trusted sources.

    Raises:
        FileNotFoundError: If *path* does not exist.
        OSError: If *path* exists but cannot be read.
        KeyError: If the checkpoint does not contain an ``"args"`` key.
        ValueError: If the model class cannot be inferred from
            ``model_name`` or ``pretrain_weights``.

    Examples:
        >>> model = RFDETR.from_checkpoint("checkpoint_best_total.pth")  # doctest: +SKIP
        >>> model = RFDETRSmall.from_checkpoint("checkpoint_best_total.pth")  # doctest: +SKIP
    """
    # Local import breaks the variants → detr import cycle.
    import rfdetr.variants as rfdetr_variants

    _plus_available = False
    _plus_symbols: dict[str, type[RFDETR]] = {}
    _plus_entries: list[tuple[str, type[RFDETR]]] = []
    try:
        import rfdetr.platform.models as platform_models

        for class_symbol in _CHECKPOINT_PLUS_MODEL_NAME_CLASS_SYMBOLS:
            plus_obj = getattr(platform_models, class_symbol)
            _plus_symbols[class_symbol] = plus_obj
        _plus_entries = [
            (name, _plus_symbols[class_symbol]) for name, class_symbol in _CHECKPOINT_PLUS_MODEL_MAP_ENTRIES
        ]
        _plus_available = True
    except (ImportError, AttributeError):
        _plus_symbols = {}

    # weights_only=False is required because legacy checkpoints embed
    # argparse.Namespace objects that cannot be deserialised with
    # weights_only=True.
    ckpt: dict[str, Any] = torch.load(path, map_location="cpu", weights_only=False)
    args = ckpt["args"]

    _variant_name_to_class: dict[str, type[RFDETR]] = {
        getattr(variant_obj, "__name__", symbol): variant_obj
        for symbol in dir(rfdetr_variants)
        if symbol.startswith("RFDETR")
        for variant_obj in [getattr(rfdetr_variants, symbol)]
    }
    _variant_symbols: dict[str, type[RFDETR]] = {
        class_symbol: _variant_name_to_class[class_symbol] for class_symbol in _CHECKPOINT_MODEL_NAME_CLASS_SYMBOLS
    }
    # Build in three explicit segments: seg-* entries, then plus-model entries
    # (xlarge/2xlarge), then base entries — order determines lookup priority.
    _seg_map: list[tuple[str, type[RFDETR]]] = [
        (name, _variant_symbols[class_symbol])
        for name, class_symbol in _CHECKPOINT_MODEL_MAP_ENTRIES
        if name.startswith("seg-")
    ]
    _base_map: list[tuple[str, type[RFDETR]]] = [
        (name, _variant_symbols[class_symbol])
        for name, class_symbol in _CHECKPOINT_MODEL_MAP_ENTRIES
        if not name.startswith("seg-")
    ]
    _model_map: list[tuple[str, type[RFDETR]]] = _seg_map + _plus_entries + _base_map

    # New checkpoints store model_name directly — use it when available.
    _name_map: dict[str, type[RFDETR]] = dict(_variant_symbols)
    # Plus-model classes are resolved only when rfdetr_plus is installed.
    if _plus_available:
        _name_map.update(_plus_symbols)
    saved_model_name = ckpt.get("model_name")
    model_cls: type[RFDETR] | None = None
    if isinstance(saved_model_name, str):
        normalized_name = saved_model_name.strip()
        if normalized_name:
            model_cls = _name_map.get(normalized_name)
    else:
        normalized_name = ""

    # Fall back to pretrain_weights filename parsing for older checkpoints.
    if isinstance(args, dict):
        weights_name = str(args.get("pretrain_weights", "")).lower()
    else:
        weights_name = str(getattr(args, "pretrain_weights", "")).lower()

    if model_cls is None:
        # Guard: plus-only checkpoints should raise an actionable install error
        # when rfdetr_plus is missing, regardless of whether class inference
        # relies on model_name (new format) or pretrain_weights (legacy format).
        plus_by_model_name = normalized_name in _CHECKPOINT_PLUS_MODEL_NAME_CLASS_SYMBOLS
        plus_by_weights_name = "xlarge" in weights_name and "seg-" not in weights_name
        if not _plus_available and (plus_by_model_name or plus_by_weights_name):
            from rfdetr.platform import _INSTALL_MSG

            raise ImportError(
                f"Checkpoint model_name={saved_model_name!r}, pretrain_weights={weights_name!r} requires the "
                f"rfdetr_plus package. " + _INSTALL_MSG.format(name="platform model downloads")
            )

        for name, klass in _model_map:
            if name in weights_name:
                model_cls = klass
                break

    if model_cls is None:
        raise ValueError(
            f"Could not infer model class from checkpoint at {path!r} "
            f"(model_name={saved_model_name!r}, pretrain_weights={weights_name!r}). "
            f"Please instantiate the model class directly."
        )

    if isinstance(args, dict):
        num_classes: int | None = args.get("num_classes")
    else:
        num_classes = getattr(args, "num_classes", None)

    # pretrain_weights is placed after **kwargs so it always wins even if
    # a caller accidentally passes pretrain_weights inside kwargs.
    constructor_kwargs: dict[str, Any] = {**kwargs, "pretrain_weights": str(path)}
    if num_classes is not None and "num_classes" not in kwargs:
        constructor_kwargs["num_classes"] = num_classes

    return model_cls(**constructor_kwargs)

get_model(config)

Retrieve a model context from the provided architecture configuration.

Parameters:

Name	Type	Description	Default
config	ModelConfig	Architecture configuration.	required

Returns:

Type	Description
ModelContext	ModelContext with model, postprocess, device, resolution, args,
ModelContext	and class_names attributes.

Source code in src/rfdetr/detr.py

def get_model(self, config: ModelConfig) -> ModelContext:
    """Retrieve a model context from the provided architecture configuration.

    Args:
        config: Architecture configuration.

    Returns:
        ModelContext with model, postprocess, device, resolution, args,
        and class_names attributes.

    """
    return _build_model_context(config)

maybe_download_pretrain_weights()

Download pre-trained weights if they are not already downloaded.

Bare filenames (no directory component, e.g. rf-detr-base.pth) are resolved to the model cache directory — set the RF_HOME environment variable to override the location (default: ~/.roboflow/models). Resolution happens in ModelConfig.expand_path for explicitly-provided values, and here as a fallback for field defaults (which Pydantic does not validate by default).

Paths that already contain a directory component are used as-is; the parent directory is created if it does not yet exist.

Source code in src/rfdetr/detr.py

def maybe_download_pretrain_weights(self):
    """Download pre-trained weights if they are not already downloaded.

    Bare filenames (no directory component, e.g. ``rf-detr-base.pth``) are
    resolved to the model cache directory — set the ``RF_HOME`` environment
    variable to override the location (default: ``~/.roboflow/models``).
    Resolution happens in ``ModelConfig.expand_path`` for explicitly-provided
    values, and here as a fallback for field defaults (which Pydantic does not
    validate by default).

    Paths that already contain a directory component are used as-is; the
    parent directory is created if it does not yet exist.
    """
    pretrain_weights = self.model_config.pretrain_weights
    if pretrain_weights is None:
        return
    if not os.path.dirname(pretrain_weights):
        # Field default was not processed by expand_path — resolve to cache dir.
        cache_dir = get_model_cache_dir()
        os.makedirs(cache_dir, exist_ok=True)
        pretrain_weights = os.path.join(cache_dir, pretrain_weights)
    else:
        os.makedirs(os.path.dirname(pretrain_weights), exist_ok=True)
    self.model_config.pretrain_weights = pretrain_weights
    download_pretrain_weights(self.model_config.pretrain_weights)

optimize_for_inference(compile=True, batch_size=1, dtype=torch.float32)

Optimize the model for inference with optional JIT compilation and dtype casting.

Operations are wrapped in the correct CUDA device context to prevent context leaks on multi-GPU setups. When compile=True the model is traced with torch.jit.trace using a dummy input of batch_size images at the model's current resolution.

Parameters:

Name	Type	Description	Default
compile	bool	If True, trace the model with torch.jit.trace to obtain a JIT-compiled ScriptModule. Set to False for broader compatibility (e.g. models with dynamic control flow).	True
batch_size	int	Number of images the traced model will be optimized for. Ignored when compile=False.	1
dtype	dtype | str	Target floating-point dtype for the inference model. Accepts a torch.dtype directly (e.g. torch.float16) or its string name (e.g. "float16"). Defaults to torch.float32.	float32

Raises:

Type	Description
TypeError	If dtype is not a torch.dtype, or if dtype is a string that does not correspond to a valid torch.dtype attribute.

Examples:

>>> from types import SimpleNamespace
>>> import torch
>>> class _TinyModel(torch.nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.linear = torch.nn.Linear(1, 1)
...     def forward(self, x):
...         return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
...     def export(self):
...         return None
>>> class _TinyContext:
...     def __init__(self):
...         self.device = torch.device("cpu")
...         self.resolution = 28
...         self.model = _TinyModel()
...         self.inference_model = None
>>> model = object.__new__(RFDETR)
>>> model.model_config = SimpleNamespace(num_channels=3)
>>> model.model = _TinyContext()
>>> model._is_optimized_for_inference = False
>>> model._has_warned_about_not_being_optimized_for_inference = False
>>> model._optimized_has_been_compiled = False
>>> model._optimized_batch_size = None
>>> model._optimized_resolution = None
>>> model._optimized_dtype = None
>>> model.optimize_for_inference(compile=False, dtype="float16")
>>> model._is_optimized_for_inference
True
>>> model._optimized_dtype
torch.float16

Source code in src/rfdetr/detr.py

def optimize_for_inference(
    self, compile: bool = True, batch_size: int = 1, dtype: torch.dtype | str = torch.float32
) -> None:
    """Optimize the model for inference with optional JIT compilation and dtype casting.

    Operations are wrapped in the correct CUDA device context to prevent context
    leaks on multi-GPU setups. When ``compile=True`` the model is traced with
    ``torch.jit.trace`` using a dummy input of ``batch_size`` images at the
    model's current resolution.

    Args:
        compile: If ``True``, trace the model with ``torch.jit.trace`` to obtain
            a JIT-compiled ``ScriptModule``. Set to ``False`` for broader
            compatibility (e.g. models with dynamic control flow).
        batch_size: Number of images the traced model will be optimized for.
            Ignored when ``compile=False``.
        dtype: Target floating-point dtype for the inference model. Accepts a
            ``torch.dtype`` directly (e.g. ``torch.float16``) or its string name
            (e.g. ``"float16"``). Defaults to ``torch.float32``.

    Raises:
        TypeError: If ``dtype`` is not a ``torch.dtype``, or if ``dtype`` is a
            string that does not correspond to a valid ``torch.dtype`` attribute.

    Examples:
        >>> from types import SimpleNamespace
        >>> import torch
        >>> class _TinyModel(torch.nn.Module):
        ...     def __init__(self):
        ...         super().__init__()
        ...         self.linear = torch.nn.Linear(1, 1)
        ...     def forward(self, x):
        ...         return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
        ...     def export(self):
        ...         return None
        >>> class _TinyContext:
        ...     def __init__(self):
        ...         self.device = torch.device("cpu")
        ...         self.resolution = 28
        ...         self.model = _TinyModel()
        ...         self.inference_model = None
        >>> model = object.__new__(RFDETR)
        >>> model.model_config = SimpleNamespace(num_channels=3)
        >>> model.model = _TinyContext()
        >>> model._is_optimized_for_inference = False
        >>> model._has_warned_about_not_being_optimized_for_inference = False
        >>> model._optimized_has_been_compiled = False
        >>> model._optimized_batch_size = None
        >>> model._optimized_resolution = None
        >>> model._optimized_dtype = None
        >>> model.optimize_for_inference(compile=False, dtype="float16")
        >>> model._is_optimized_for_inference
        True
        >>> model._optimized_dtype
        torch.float16
    """
    if isinstance(dtype, str):
        try:
            dtype = getattr(torch, dtype)
        except AttributeError:
            raise TypeError(f"dtype must be a torch.dtype or a string name of a dtype, got {dtype!r}") from None
    if not isinstance(dtype, torch.dtype):
        raise TypeError(f"dtype must be a torch.dtype or a string name of a dtype, got {type(dtype)!r}")

    # Clear any previously optimized state before starting a new optimization run.
    self.remove_optimized_model()

    _ensure_model_on_device(self.model)
    device = self.model.device
    cuda_ctx = torch.cuda.device(device) if device.type == "cuda" else contextlib.nullcontext()

    try:
        with cuda_ctx:
            self.model.inference_model = deepcopy(self.model.model)
            self.model.inference_model.eval()
            self.model.inference_model.export()

            self.model.inference_model = self.model.inference_model.to(dtype=dtype)

            if compile:
                self.model.inference_model = torch.jit.trace(
                    self.model.inference_model,
                    torch.randn(
                        batch_size,
                        self.model_config.num_channels,
                        self.model.resolution,
                        self.model.resolution,
                        device=self.model.device,
                        dtype=dtype,
                    ),
                )
                self._optimized_has_been_compiled = True
                self._optimized_batch_size = batch_size

            # Set success flags only after all operations complete.
            self._optimized_resolution = self.model.resolution
            self._is_optimized_for_inference = True
            self._optimized_dtype = dtype
    except Exception:
        # Ensure the object is left in a consistent, unoptimized state if optimization fails.
        with contextlib.suppress(Exception):
            self.remove_optimized_model()
        raise

predict(images, threshold=0.5, shape=None, patch_size=None, include_source_image=True, **kwargs)

Performs object detection on the input images and returns bounding box predictions.

This method accepts a single image or a list of images in various formats (file path, image url, PIL Image, NumPy array, or torch.Tensor). The images should be in RGB channel order. If a torch.Tensor is provided, it must already be normalized to values in the [0, 1] range and have the shape (C, H, W).

Parameters:

Name	Type	Description	Default
images	str | Image | ndarray | Tensor | list[str | ndarray | Image | Tensor]	A single image or a list of images to process. Images can be provided as file paths, PIL Images, NumPy arrays, or torch.Tensors.	required
threshold	float	The minimum confidence score needed to consider a detected bounding box valid.	0.5
shape	tuple[int, int] | None	Optional (height, width) tuple to resize images to before inference. When provided, overrides the model's default inference resolution. The tuple should match the resolution used when exporting the model (typically a square shape). Both dimensions must be positive integers divisible by patch_size * num_windows. Defaults to (model.resolution, model.resolution) when not set.	None
patch_size	int | None	Backbone patch size used for shape divisibility validation. Defaults to model_config.patch_size (typically 14 for large models, 16 for smaller ones). Divisibility is checked against patch_size * num_windows.	None
include_source_image	bool	Whether to attach the original image as source_image in detections.metadata. Defaults to True. Set to False to reduce memory use when source images are not needed.	True
**kwargs	Any	Additional keyword arguments.	{}

Returns:

Name	Type	Description
	Detections | list[Detections]	A single or multiple Detections objects, each containing bounding box
	Detections | list[Detections]	coordinates, confidence scores, and class IDs. The data dict of
each	Detections | list[Detections]	class:~supervision.Detections object contains class_name
	Detections | list[Detections]	as a string array corresponding to each detection and source_shape
	Detections | list[Detections]	as an int64 array of shape (N, 2) with [height, width] rows.
	Detections | list[Detections]	source_shape is stored per detection so supervision indexing works
	Detections | list[Detections]	correctly. It was previously a (height, width) Python tuple;
	Detections | list[Detections]	callers using isinstance(v, tuple) or v == (H, W) must be
	Detections | list[Detections]	updated. The metadata dict contains source_image as the original
	Detections | list[Detections]	uint8 image array of shape (H, W, 3) when
	Detections | list[Detections]	include_source_image=True.

Note

source_image moved from detections.data to detections.metadata. Update callers reading detections.data["source_image"] to use detections.metadata["source_image"].

Raises:

Type	Description
ValueError	If shape cannot be unpacked as a two-element sequence, if either dimension does not support the __index__ protocol (e.g. float) or is a bool, if either dimension is zero or negative, if either dimension is not divisible by patch_size * num_windows, or if patch_size is not a positive integer.

Source code in src/rfdetr/detr.py

def predict(
    self,
    images: str | Image.Image | np.ndarray | torch.Tensor | list[str | np.ndarray | Image.Image | torch.Tensor],
    threshold: float = 0.5,
    shape: tuple[int, int] | None = None,
    patch_size: int | None = None,
    include_source_image: bool = True,
    **kwargs: Any,
) -> sv.Detections | list[sv.Detections]:
    """Performs object detection on the input images and returns bounding box
    predictions.

    This method accepts a single image or a list of images in various formats
    (file path, image url, PIL Image, NumPy array, or torch.Tensor). The images should be in
    RGB channel order. If a torch.Tensor is provided, it must already be normalized
    to values in the [0, 1] range and have the shape (C, H, W).

    Args:
        images:
            A single image or a list of images to process. Images can be provided
            as file paths, PIL Images, NumPy arrays, or torch.Tensors.
        threshold:
            The minimum confidence score needed to consider a detected bounding box valid.
        shape:
            Optional ``(height, width)`` tuple to resize images to before inference.
            When provided, overrides the model's default inference resolution. The
            tuple should match the resolution used when exporting the model
            (typically a square shape). Both dimensions must be positive integers
            divisible by ``patch_size * num_windows``. Defaults to
            ``(model.resolution, model.resolution)`` when not set.
        patch_size:
            Backbone patch size used for shape divisibility validation. Defaults
            to ``model_config.patch_size`` (typically 14 for large models, 16 for
            smaller ones). Divisibility is checked against
            ``patch_size * num_windows``.
        include_source_image:
            Whether to attach the original image as ``source_image`` in
            ``detections.metadata``. Defaults to ``True``.  Set to ``False``
            to reduce memory use when source images are not needed.
        **kwargs:
            Additional keyword arguments.

    Returns:
        A single or multiple Detections objects, each containing bounding box
        coordinates, confidence scores, and class IDs. The ``data`` dict of
        each :class:`~supervision.Detections` object contains ``class_name``
        as a string array corresponding to each detection and ``source_shape``
        as an ``int64`` array of shape ``(N, 2)`` with ``[height, width]`` rows.
        ``source_shape`` is stored per detection so supervision indexing works
        correctly. It was previously a ``(height, width)`` Python ``tuple``;
        callers using ``isinstance(v, tuple)`` or ``v == (H, W)`` must be
        updated. The ``metadata`` dict contains ``source_image`` as the original
        ``uint8`` image array of shape ``(H, W, 3)`` when
        ``include_source_image=True``.

    Note:
        ``source_image`` moved from ``detections.data`` to
        ``detections.metadata``. Update callers reading
        ``detections.data["source_image"]`` to use
        ``detections.metadata["source_image"]``.

    Raises:
        ValueError: If ``shape`` cannot be unpacked as a two-element sequence,
            if either dimension does not support the ``__index__`` protocol
            (e.g. ``float``) or is a ``bool``, if either dimension is zero or
            negative, if either dimension is not divisible by
            ``patch_size * num_windows``, or if ``patch_size`` is not a positive
            integer.

    """
    import supervision as sv

    _ensure_model_on_device(self.model)

    patch_size = _resolve_patch_size(patch_size, self.model_config, "predict")
    num_windows = getattr(self.model_config, "num_windows", 1)
    if isinstance(num_windows, bool) or not isinstance(num_windows, int) or num_windows <= 0:
        raise ValueError(f"model_config.num_windows must be a positive integer, got {num_windows!r}")
    block_size = patch_size * num_windows

    if shape is None:
        default_res = self.model.resolution
        if default_res % block_size != 0:
            raise ValueError(
                f"Model's default resolution ({default_res}) is not divisible by "
                f"block_size={block_size} (patch_size={patch_size} * num_windows={num_windows}). "
                f"Provide an explicit shape divisible by {block_size}.",
            )
    else:
        shape = _validate_shape_dims(shape, block_size, patch_size, num_windows)

    if not self._is_optimized_for_inference and not self._has_warned_about_not_being_optimized_for_inference:
        logger.warning(
            "Model is not optimized for inference. Latency may be higher than expected."
            " You can optimize the model for inference by calling model.optimize_for_inference().",
        )
        self._has_warned_about_not_being_optimized_for_inference = True

        self.model.model.eval()

    if not isinstance(images, list):
        images = [images]

    orig_sizes = []
    processed_images = []
    source_images = [] if include_source_image else None

    for img in images:
        if isinstance(img, str):
            if img.startswith("http"):
                img = requests.get(img, stream=True).raw
            img = Image.open(img)

        if not isinstance(img, torch.Tensor):
            if include_source_image:
                src = np.array(img)
                if src.dtype != np.uint8:
                    src = (src * 255).clip(0, 255).astype(np.uint8)
                source_images.append(src)
            img = F.to_tensor(img)
        elif include_source_image:
            source_images.append((img.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8))

        if (img > 1).any():
            raise ValueError(
                "Image has pixel values above 1. Please ensure the image is normalized (scaled to [0, 1]).",
            )
        if (img < 0).any():
            raise ValueError(
                "Image has pixel values below 0. Please ensure the image is normalized (scaled to [0, 1]).",
            )
        if img.shape[0] != self.model_config.num_channels:
            raise ValueError(
                "Invalid tensor image shape. Tensor inputs to `predict()` must be in (C, H, W) format "
                f"with C matching the model configuration ({self.model_config.num_channels} channels). "
                f"Received tensor with shape {tuple(img.shape)}."
            )
        img_tensor = img

        h, w = img_tensor.shape[1:]
        orig_sizes.append((h, w))

        img_tensor = img_tensor.to(self.model.device)
        resize_to = list(shape) if shape is not None else [self.model.resolution, self.model.resolution]
        img_tensor = F.resize(img_tensor, resize_to)
        img_tensor = F.normalize(img_tensor, self.means, self.stds)

        processed_images.append(img_tensor)

    batch_tensor = torch.stack(processed_images)

    if self._is_optimized_for_inference:
        if (
            self._optimized_resolution != batch_tensor.shape[2]
            or self._optimized_resolution != batch_tensor.shape[3]
        ):
            # this could happen if someone manually changes self.model.resolution after optimizing the model,
            # or if predict(shape=...) is used with a shape that doesn't match the compiled square resolution.
            raise ValueError(
                f"Resolution mismatch. "
                f"Model was optimized for resolution {self._optimized_resolution}x{self._optimized_resolution}, "
                f"but got {batch_tensor.shape[2]}x{batch_tensor.shape[3]}."
                " You can explicitly remove the optimized model by calling model.remove_optimized_model().",
            )
        if self._optimized_has_been_compiled:
            if self._optimized_batch_size != batch_tensor.shape[0]:
                raise ValueError(
                    f"Batch size mismatch. "
                    f"Optimized model was compiled for batch size {self._optimized_batch_size}, "
                    f"but got {batch_tensor.shape[0]}."
                    " You can explicitly remove the optimized model by calling model.remove_optimized_model()."
                    " Alternatively, you can recompile the optimized model for a different batch size"
                    " by calling model.optimize_for_inference(batch_size=<new_batch_size>).",
                )

    with torch.no_grad():
        if self._is_optimized_for_inference:
            predictions = self.model.inference_model(batch_tensor.to(dtype=self._optimized_dtype))
        else:
            predictions = self.model.model(batch_tensor)
        if isinstance(predictions, tuple):
            return_predictions = {
                "pred_logits": predictions[1],
                "pred_boxes": predictions[0],
            }
            if len(predictions) == 3:
                return_predictions["pred_masks"] = predictions[2]
            predictions = return_predictions
        target_sizes = torch.tensor(orig_sizes, device=self.model.device)
        results = self.model.postprocess(predictions, target_sizes=target_sizes)

    model_class_names = self.class_names
    n = len(model_class_names)
    detections_list = []
    for i, result in enumerate(results):
        scores = result["scores"]
        labels = result["labels"]
        boxes = result["boxes"]

        keep = scores > threshold
        scores = scores[keep]
        labels = labels[keep]
        boxes = boxes[keep]

        if "masks" in result:
            masks = result["masks"]
            masks = masks[keep]

            detections = sv.Detections(
                xyxy=boxes.float().cpu().numpy(),
                confidence=scores.float().cpu().numpy(),
                class_id=labels.cpu().numpy(),
                mask=masks.squeeze(1).cpu().numpy(),
            )
        else:
            detections = sv.Detections(
                xyxy=boxes.float().cpu().numpy(),
                confidence=scores.float().cpu().numpy(),
                class_id=labels.cpu().numpy(),
            )

        if include_source_image:
            detections.metadata["source_image"] = source_images[i]
        detections.data["source_shape"] = np.tile(np.array(orig_sizes[i], dtype=np.int64), (len(detections), 1))

        # Attach class names so callers can map class_id → name without a
        # separate lookup.  class_id is always 0-indexed regardless of the
        # original dataset format (COCO category IDs are remapped during
        # training), so class_names[class_id] is the correct mapping.
        # Always set data["class_name"] for a consistent interface.
        #
        # RF-DETR uses num_classes + 1 logits internally; class index n is the
        # background/no-object class and is expected — map it to "__background__"
        # without warning.  Indices outside [0, n] are genuinely unexpected and
        # still produce an empty string with a one-time warning.
        class_ids = detections.class_id if detections.class_id is not None else np.array([], dtype=int)
        truly_oob = [cid for cid in class_ids if not (0 <= cid <= n)]
        if truly_oob:
            logger.warning_once(
                "predict() encountered class_id values out of range [0, %d]: %s — mapping to empty string",
                n,
                truly_oob[:5],
            )
        detections.data["class_name"] = np.array(
            [
                model_class_names[cid] if 0 <= cid < n else ("__background__" if cid == n else "")
                for cid in class_ids
            ],
            dtype=object,
        )

        detections_list.append(detections)

    return detections_list if len(detections_list) > 1 else detections_list[0]

remove_optimized_model()

Remove the optimized inference model and reset all optimization flags.

Clears model.inference_model and resets all internal state set by :meth:optimize_for_inference. Safe to call even if the model has not been optimized.

Examples:

>>> from types import SimpleNamespace
>>> import torch
>>> class _TinyModel(torch.nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.linear = torch.nn.Linear(1, 1)
...     def forward(self, x):
...         return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
...     def export(self):
...         return None
>>> class _TinyContext:
...     def __init__(self):
...         self.device = torch.device("cpu")
...         self.resolution = 28
...         self.model = _TinyModel()
...         self.inference_model = None
>>> model = object.__new__(RFDETR)
>>> model.model_config = SimpleNamespace(num_channels=3)
>>> model.model = _TinyContext()
>>> model._is_optimized_for_inference = False
>>> model._has_warned_about_not_being_optimized_for_inference = False
>>> model._optimized_has_been_compiled = False
>>> model._optimized_batch_size = None
>>> model._optimized_resolution = None
>>> model._optimized_dtype = None
>>> model.optimize_for_inference(compile=False)
>>> model.remove_optimized_model()
>>> model._is_optimized_for_inference
False

Source code in src/rfdetr/detr.py

def remove_optimized_model(self) -> None:
    """Remove the optimized inference model and reset all optimization flags.

    Clears ``model.inference_model`` and resets all internal state set by
    :meth:`optimize_for_inference`. Safe to call even if the model has not
    been optimized.

    Examples:
        >>> from types import SimpleNamespace
        >>> import torch
        >>> class _TinyModel(torch.nn.Module):
        ...     def __init__(self):
        ...         super().__init__()
        ...         self.linear = torch.nn.Linear(1, 1)
        ...     def forward(self, x):
        ...         return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
        ...     def export(self):
        ...         return None
        >>> class _TinyContext:
        ...     def __init__(self):
        ...         self.device = torch.device("cpu")
        ...         self.resolution = 28
        ...         self.model = _TinyModel()
        ...         self.inference_model = None
        >>> model = object.__new__(RFDETR)
        >>> model.model_config = SimpleNamespace(num_channels=3)
        >>> model.model = _TinyContext()
        >>> model._is_optimized_for_inference = False
        >>> model._has_warned_about_not_being_optimized_for_inference = False
        >>> model._optimized_has_been_compiled = False
        >>> model._optimized_batch_size = None
        >>> model._optimized_resolution = None
        >>> model._optimized_dtype = None
        >>> model.optimize_for_inference(compile=False)
        >>> model.remove_optimized_model()
        >>> model._is_optimized_for_inference
        False
    """
    self.model.inference_model = None
    self._is_optimized_for_inference = False
    self._optimized_has_been_compiled = False
    self._optimized_batch_size = None
    self._optimized_resolution = None
    self._optimized_dtype = None

train(**kwargs)

Train an RF-DETR model via the PyTorch Lightning stack.

All keyword arguments are forwarded to :meth:get_train_config to build a :class:~rfdetr.config.TrainConfig. Several kwargs are absorbed and handled specially so that existing call-sites do not break:

• resolution — updates the model's input resolution by mutating :attr:model_config.resolution in place before the train config is built. This change persists on :attr:model_config after :meth:train returns. The value must be a positive integer divisible by patch_size * num_windows for the model variant; a :class:ValueError is raised otherwise. :attr:model_config.positional_encoding_size is also updated when the config derives it formulaically (PE == resolution // patch_size); configs with a pretrained-specific PE value (e.g. RFDETRBase uses DINOv2's PE=37 at 560 px) are left unchanged to preserve checkpoint compatibility.
• device — normalized via :class:torch.device and mapped to PyTorch Lightning trainer arguments. "cpu" becomes accelerator="cpu"; "cuda" and "cuda:N" become accelerator="gpu" and optionally devices=[N]; "mps" becomes accelerator="mps". Other valid torch device types fall back to PTL auto-detection and emit a :class:UserWarning.
• callbacks — if the dict contains any non-empty lists a :class:DeprecationWarning is emitted; the dict is then discarded. Use PTL :class:~pytorch_lightning.Callback objects passed via :func:~rfdetr.training.build_trainer instead.
• start_epoch — emits :class:DeprecationWarning and is dropped.
• do_benchmark — emits :class:DeprecationWarning and is dropped.

After training completes the underlying nn.Module is synced back onto self.model.model so that :meth:predict and :meth:export continue to work without reloading the checkpoint.

Raises:

Type	Description
ImportError	If training dependencies are not installed. Install with pip install "rfdetr[train,loggers]".
ValueError	If resolution is not a positive integer or is not divisible by patch_size * num_windows for the model variant.

Source code in src/rfdetr/detr.py

def train(self, **kwargs):
    """Train an RF-DETR model via the PyTorch Lightning stack.

    All keyword arguments are forwarded to :meth:`get_train_config` to build
    a :class:`~rfdetr.config.TrainConfig`.  Several kwargs are absorbed and
    handled specially so that existing call-sites do not break:

    * ``resolution`` — updates the model's input resolution by mutating
      :attr:`model_config.resolution` in place before the train config is
      built. This change persists on :attr:`model_config` after
      :meth:`train` returns. The value must be a positive integer divisible
      by ``patch_size * num_windows`` for the model variant; a
      :class:`ValueError` is raised otherwise.
      :attr:`model_config.positional_encoding_size` is also updated when
      the config derives it formulaically (``PE == resolution //
      patch_size``); configs with a pretrained-specific PE value (e.g.
      ``RFDETRBase`` uses DINOv2's PE=37 at 560 px) are left unchanged to
      preserve checkpoint compatibility.
    * ``device`` — normalized via :class:`torch.device` and mapped to PyTorch
      Lightning trainer arguments. ``"cpu"`` becomes ``accelerator="cpu"``;
      ``"cuda"`` and ``"cuda:N"`` become ``accelerator="gpu"`` and optionally
      ``devices=[N]``; ``"mps"`` becomes ``accelerator="mps"``. Other valid
      torch device types fall back to PTL auto-detection and emit a
      :class:`UserWarning`.
    * ``callbacks`` — if the dict contains any non-empty lists a
      :class:`DeprecationWarning` is emitted; the dict is then discarded.
      Use PTL :class:`~pytorch_lightning.Callback` objects passed via
      :func:`~rfdetr.training.build_trainer` instead.
    * ``start_epoch`` — emits :class:`DeprecationWarning` and is dropped.
    * ``do_benchmark`` — emits :class:`DeprecationWarning` and is dropped.

    After training completes the underlying ``nn.Module`` is synced back
    onto ``self.model.model`` so that :meth:`predict` and :meth:`export`
    continue to work without reloading the checkpoint.

    Raises:
        ImportError: If training dependencies are not installed. Install with
            ``pip install "rfdetr[train,loggers]"``.
        ValueError: If ``resolution`` is not a positive integer or is not
            divisible by ``patch_size * num_windows`` for the model variant.

    """
    # Both imports are grouped in a single try block because they both live in
    # the `rfdetr[train]` extras group — a missing `pytorch_lightning` (or any
    # other training-extras package) causes either import to fail, and the
    # remediation is identical: `pip install "rfdetr[train,loggers]"`.
    try:
        from rfdetr.training import RFDETRDataModule, RFDETRModelModule, build_trainer
        from rfdetr.training.auto_batch import resolve_auto_batch_config
    except ModuleNotFoundError as exc:
        # Preserve internal import errors so packaging/regression issues in
        # rfdetr.* are not misreported as missing optional extras.
        if exc.name and exc.name.startswith("rfdetr."):
            raise
        raise ImportError(
            "RF-DETR training dependencies are missing. "
            'Install them with `pip install "rfdetr[train,loggers]"` and try again.',
        ) from exc

    # Absorb legacy `callbacks` dict — warn if non-empty, then discard.
    callbacks_dict = kwargs.pop("callbacks", None)
    if callbacks_dict and any(callbacks_dict.values()):
        warnings.warn(
            "Custom callbacks dict is not forwarded to PTL. Use PTL Callback objects instead.",
            DeprecationWarning,
            stacklevel=2,
        )

    # Parse `device` kwarg and map it to PTL accelerator/devices.
    # Supports torch-style strings and torch.device (e.g. "cuda:1").
    _device = kwargs.pop("device", None)
    _accelerator, _devices = RFDETR._resolve_trainer_device_kwargs(_device)

    # Absorb legacy `start_epoch` — PTL resumes automatically via ckpt_path.
    if "start_epoch" in kwargs:
        warnings.warn(
            "`start_epoch` is deprecated and ignored; PTL resumes automatically via `resume`.",
            DeprecationWarning,
            stacklevel=2,
        )
        kwargs.pop("start_epoch")

    # Pop `do_benchmark`; benchmarking via `.train()` is deprecated.
    run_benchmark = bool(kwargs.pop("do_benchmark", False))
    if run_benchmark:
        warnings.warn(
            "`do_benchmark` in `.train()` is deprecated; use `rfdetr benchmark`.",
            DeprecationWarning,
            stacklevel=2,
        )

    # Apply resolution override to model_config before building the train config.
    # resolution is a ModelConfig field, not a TrainConfig field, so we pop it
    # here to avoid it being silently ignored by TrainConfig.
    _resolution = kwargs.pop("resolution", None)
    if _resolution is not None:
        if isinstance(_resolution, bool):
            raise ValueError("resolution must be a positive integer")
        try:
            _resolution = operator.index(_resolution)
        except TypeError as error:
            raise ValueError("resolution must be a positive integer") from error
        if _resolution <= 0:
            raise ValueError("resolution must be a positive integer")
        block_size = self.model_config.patch_size * self.model_config.num_windows
        if _resolution % block_size != 0:
            raise ValueError(
                f"resolution={_resolution} is not divisible by "
                f"patch_size ({self.model_config.patch_size}) * num_windows "
                f"({self.model_config.num_windows}) = {block_size}. "
                f"Choose a resolution that is a multiple of {block_size}."
            )
        # Smart PE update: only recompute positional_encoding_size when the
        # current config derives it formulaically (PE == resolution // patch_size).
        # Configs with a pretrained-specific PE (e.g. RFDETRBase uses DINOv2's
        # PE=37 at 518 px, training at 560 px) must not have PE silently changed
        # — doing so causes shape mismatches when loading pretrained checkpoints.
        _current_pe = self.model_config.positional_encoding_size
        _derived_pe = self.model_config.resolution // self.model_config.patch_size
        if _current_pe == _derived_pe:
            # Formula-derived: update PE proportionally to the new resolution.
            new_pe = _resolution // self.model_config.patch_size
            self.model_config.positional_encoding_size = new_pe
        else:
            # Pretrained-specific PE; leave it unchanged.
            new_pe = _current_pe
        self.model_config.resolution = _resolution

        # Keep the cached inference/export context in sync with model_config so
        # predict()/export()/deployment all see the same resolution metadata.
        if hasattr(self, "model") and self.model is not None:
            if hasattr(self.model, "resolution"):
                self.model.resolution = _resolution
            model_args = getattr(self.model, "args", None)
            if model_args is not None:
                if hasattr(model_args, "resolution"):
                    model_args.resolution = _resolution
                if hasattr(model_args, "positional_encoding_size"):
                    model_args.positional_encoding_size = new_pe
    config = self.get_train_config(**kwargs)
    if config.batch_size == "auto":
        # Auto-batch probing runs forward/backward on the actual model, which
        # must be on the target device (typically CUDA).  Lazy placement keeps
        # the model on CPU until first use — move it now.
        _ensure_model_on_device(self.model)
        auto_batch = resolve_auto_batch_config(
            model_context=self.model,
            model_config=self.model_config,
            train_config=config,
        )
        config.batch_size = auto_batch.safe_micro_batch
        config.grad_accum_steps = auto_batch.recommended_grad_accum_steps
        logger.info(
            "[auto-batch] resolved train config: batch_size=%s grad_accum_steps=%s effective_batch_size=%s",
            config.batch_size,
            config.grad_accum_steps,
            auto_batch.effective_batch_size,
        )
    self.model_config.model_name = type(self).__name__

    # Auto-detect num_classes from the training dataset and align model_config.
    # This must run before RFDETRModelModule is constructed so that weight loading
    # inside the module uses the correct (dataset-derived) class count.
    dataset_dir = getattr(config, "dataset_dir", None)
    if dataset_dir:
        self._align_num_classes_from_dataset(dataset_dir)

    module = RFDETRModelModule(self.model_config, config)
    datamodule = RFDETRDataModule(self.model_config, config)

    # Guard with LOCAL_RANK env var rather than is_main_process() because torch.distributed
    # is not yet initialized here (it is set up inside trainer.fit()).  In Lightning DDP
    # subprocesses, LOCAL_RANK is set by the launcher before the subprocess calls train(),
    # so this correctly identifies rank 0 even before dist.init_process_group() runs.
    if config.save_dataset_grids and os.environ.get("LOCAL_RANK", "0") == "0":
        try:
            from rfdetr.datasets.save_grids import DatasetGridSaver

            datamodule.setup("fit")
            grids_output_dir = Path(config.output_dir) / "dataset_grids"
            DatasetGridSaver(datamodule.train_dataloader(), grids_output_dir, dataset_type="train").save_grid()
            DatasetGridSaver(datamodule.val_dataloader(), grids_output_dir, dataset_type="val").save_grid()
        except Exception:
            logger.warning(
                "Failed to save dataset grids; training will continue without them.",
                exc_info=True,
            )

    trainer_kwargs = {"accelerator": _accelerator}
    if _devices is not None:
        trainer_kwargs["devices"] = _devices
    trainer = build_trainer(config, self.model_config, **trainer_kwargs)
    trainer.fit(module, datamodule, ckpt_path=config.resume or None)

    # Sync the trained weights back so predict() / export() see the updated model.
    self.model.model = module.model
    # Sync class names: prefer explicit config.class_names, otherwise fall back to dataset (#509).
    config_class_names = getattr(config, "class_names", None)
    if config_class_names is not None:
        self.model.class_names = config_class_names
    else:
        dataset_class_names = getattr(datamodule, "class_names", None)
        if dataset_class_names is not None:
            self.model.class_names = dataset_class_names

    # Save complete training configuration to disk for reproducibility.
    # Guard to main process only to avoid races in distributed/multi-GPU training.
    if is_main_process():
        complete_config = {
            "train_config": config.model_dump(),
            "model_config": self.model_config.model_dump(),
            "model_config_type": self.model_config.__class__.__name__,
            "class_names": self.model.class_names,
            "num_classes": len(self.model.class_names) if self.model.class_names else 0,
        }
        try:
            os.makedirs(config.output_dir, exist_ok=True)
            with open(os.path.join(config.output_dir, "training_config.json"), "w") as f:
                json.dump(complete_config, f, indent=2, default=str)
        except OSError as exc:
            logger.warning("Could not save training_config.json to %s: %s", config.output_dir, exc)