Reference for ultralytics/engine/model.py

def __call__(
    self,
    source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
    stream: bool = False,
    **kwargs,
) -> list:
    """
    Alias for the predict method, enabling the model instance to be callable for predictions.

    This method simplifies the process of making predictions by allowing the model instance to be called
    directly with the required arguments.

    Args:
        source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source of
            the image(s) to make predictions on. Can be a file path, URL, PIL image, numpy array, PyTorch
            tensor, or a list/tuple of these.
        stream (bool): If True, treat the input source as a continuous stream for predictions.
        **kwargs (Any): Additional keyword arguments to configure the prediction process.

    Returns:
        (List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
            Results object.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model("https://ultralytics.com/images/bus.jpg")
        >>> for r in results:
        ...     print(f"Detected {len(r)} objects in image")
    """
    return self.predict(source, stream, **kwargs)

def add_callback(self, event: str, func) -> None:
    """
    Adds a callback function for a specified event.

    This method allows registering custom callback functions that are triggered on specific events during
    model operations such as training or inference. Callbacks provide a way to extend and customize the
    behavior of the model at various stages of its lifecycle.

    Args:
        event (str): The name of the event to attach the callback to. Must be a valid event name recognized
            by the Ultralytics framework.
        func (Callable): The callback function to be registered. This function will be called when the
            specified event occurs.

    Raises:
        ValueError: If the event name is not recognized or is invalid.

    Examples:
        >>> def on_train_start(trainer):
        ...     print("Training is starting!")
        >>> model = YOLO("yolo11n.pt")
        >>> model.add_callback("on_train_start", on_train_start)
        >>> model.train(data="coco8.yaml", epochs=1)
    """
    self.callbacks[event].append(func)

def benchmark(
    self,
    **kwargs,
):
    """
    Benchmarks the model across various export formats to evaluate performance.

    This method assesses the model's performance in different export formats, such as ONNX, TorchScript, etc.
    It uses the 'benchmark' function from the ultralytics.utils.benchmarks module. The benchmarking is
    configured using a combination of default configuration values, model-specific arguments, method-specific
    defaults, and any additional user-provided keyword arguments.

    Args:
        **kwargs (Any): Arbitrary keyword arguments to customize the benchmarking process. These are combined with
            default configurations, model-specific arguments, and method defaults. Common options include:
            - data (str): Path to the dataset for benchmarking.
            - imgsz (int | List[int]): Image size for benchmarking.
            - half (bool): Whether to use half-precision (FP16) mode.
            - int8 (bool): Whether to use int8 precision mode.
            - device (str): Device to run the benchmark on (e.g., 'cpu', 'cuda').
            - verbose (bool): Whether to print detailed benchmark information.

    Returns:
        (Dict): A dictionary containing the results of the benchmarking process, including metrics for
            different export formats.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model.benchmark(data="coco8.yaml", imgsz=640, half=True)
        >>> print(results)
    """
    self._check_is_pytorch_model()
    from ultralytics.utils.benchmarks import benchmark

    custom = {"verbose": False}  # method defaults
    args = {**DEFAULT_CFG_DICT, **self.model.args, **custom, **kwargs, "mode": "benchmark"}
    return benchmark(
        model=self,
        data=kwargs.get("data"),  # if no 'data' argument passed set data=None for default datasets
        imgsz=args["imgsz"],
        half=args["half"],
        int8=args["int8"],
        device=args["device"],
        verbose=kwargs.get("verbose"),
    )

def clear_callback(self, event: str) -> None:
    """
    Clears all callback functions registered for a specified event.

    This method removes all custom and default callback functions associated with the given event.
    It resets the callback list for the specified event to an empty list, effectively removing all
    registered callbacks for that event.

    Args:
        event (str): The name of the event for which to clear the callbacks. This should be a valid event name
            recognized by the Ultralytics callback system.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> model.add_callback("on_train_start", lambda: print("Training started"))
        >>> model.clear_callback("on_train_start")
        >>> # All callbacks for 'on_train_start' are now removed

    Notes:
        - This method affects both custom callbacks added by the user and default callbacks
          provided by the Ultralytics framework.
        - After calling this method, no callbacks will be executed for the specified event
          until new ones are added.
        - Use with caution as it removes all callbacks, including essential ones that might
          be required for proper functioning of certain operations.
    """
    self.callbacks[event] = []

def embed(
    self,
    source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
    stream: bool = False,
    **kwargs,
) -> list:
    """
    Generates image embeddings based on the provided source.

    This method is a wrapper around the 'predict()' method, focusing on generating embeddings from an image
    source. It allows customization of the embedding process through various keyword arguments.

    Args:
        source (str | Path | int | List | Tuple | np.ndarray | torch.Tensor): The source of the image for
            generating embeddings. Can be a file path, URL, PIL image, numpy array, etc.
        stream (bool): If True, predictions are streamed.
        **kwargs (Any): Additional keyword arguments for configuring the embedding process.

    Returns:
        (List[torch.Tensor]): A list containing the image embeddings.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> image = "https://ultralytics.com/images/bus.jpg"
        >>> embeddings = model.embed(image)
        >>> print(embeddings[0].shape)
    """
    if not kwargs.get("embed"):
        kwargs["embed"] = [len(self.model.model) - 2]  # embed second-to-last layer if no indices passed
    return self.predict(source, stream, **kwargs)

def export(
    self,
    **kwargs,
) -> str:
    """
    Exports the model to a different format suitable for deployment.

    This method facilitates the export of the model to various formats (e.g., ONNX, TorchScript) for deployment
    purposes. It uses the 'Exporter' class for the export process, combining model-specific overrides, method
    defaults, and any additional arguments provided.

    Args:
        **kwargs (Dict): Arbitrary keyword arguments to customize the export process. These are combined with
            the model's overrides and method defaults. Common arguments include:
            format (str): Export format (e.g., 'onnx', 'engine', 'coreml').
            half (bool): Export model in half-precision.
            int8 (bool): Export model in int8 precision.
            device (str): Device to run the export on.
            workspace (int): Maximum memory workspace size for TensorRT engines.
            nms (bool): Add Non-Maximum Suppression (NMS) module to model.
            simplify (bool): Simplify ONNX model.

    Returns:
        (str): The path to the exported model file.

    Raises:
        AssertionError: If the model is not a PyTorch model.
        ValueError: If an unsupported export format is specified.
        RuntimeError: If the export process fails due to errors.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> model.export(format="onnx", dynamic=True, simplify=True)
        'path/to/exported/model.onnx'
    """
    self._check_is_pytorch_model()
    from .exporter import Exporter

    custom = {
        "imgsz": self.model.args["imgsz"],
        "batch": 1,
        "data": None,
        "device": None,  # reset to avoid multi-GPU errors
        "verbose": False,
    }  # method defaults
    args = {**self.overrides, **custom, **kwargs, "mode": "export"}  # highest priority args on the right
    return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)

def fuse(self):
    """
    Fuses Conv2d and BatchNorm2d layers in the model for optimized inference.

    This method iterates through the model's modules and fuses consecutive Conv2d and BatchNorm2d layers
    into a single layer. This fusion can significantly improve inference speed by reducing the number of
    operations and memory accesses required during forward passes.

    The fusion process typically involves folding the BatchNorm2d parameters (mean, variance, weight, and
    bias) into the preceding Conv2d layer's weights and biases. This results in a single Conv2d layer that
    performs both convolution and normalization in one step.

    Raises:
        TypeError: If the model is not a PyTorch nn.Module.

    Examples:
        >>> model = Model("yolo11n.pt")
        >>> model.fuse()
        >>> # Model is now fused and ready for optimized inference
    """
    self._check_is_pytorch_model()
    self.model.fuse()

def info(self, detailed: bool = False, verbose: bool = True):
    """
    Logs or returns model information.

    This method provides an overview or detailed information about the model, depending on the arguments
    passed. It can control the verbosity of the output and return the information as a list.

    Args:
        detailed (bool): If True, shows detailed information about the model layers and parameters.
        verbose (bool): If True, prints the information. If False, returns the information as a list.

    Returns:
        (List[str]): A list of strings containing various types of information about the model, including
            model summary, layer details, and parameter counts. Empty if verbose is True.

    Raises:
        TypeError: If the model is not a PyTorch model.

    Examples:
        >>> model = Model("yolo11n.pt")
        >>> model.info()  # Prints model summary
        >>> info_list = model.info(detailed=True, verbose=False)  # Returns detailed info as a list
    """
    self._check_is_pytorch_model()
    return self.model.info(detailed=detailed, verbose=verbose)

@staticmethod
def is_hub_model(model: str) -> bool:
    """
    Check if the provided model is an Ultralytics HUB model.

    This static method determines whether the given model string represents a valid Ultralytics HUB model
    identifier.

    Args:
        model (str): The model string to check.

    Returns:
        (bool): True if the model is a valid Ultralytics HUB model, False otherwise.

    Examples:
        >>> Model.is_hub_model("https://hub.ultralytics.com/models/MODEL")
        True
        >>> Model.is_hub_model("yolo11n.pt")
        False
    """
    return model.startswith(f"{HUB_WEB_ROOT}/models/")

@staticmethod
def is_triton_model(model: str) -> bool:
    """
    Checks if the given model string is a Triton Server URL.

    This static method determines whether the provided model string represents a valid Triton Server URL by
    parsing its components using urllib.parse.urlsplit().

    Args:
        model (str): The model string to be checked.

    Returns:
        (bool): True if the model string is a valid Triton Server URL, False otherwise.

    Examples:
        >>> Model.is_triton_model("http://localhost:8000/v2/models/yolov8n")
        True
        >>> Model.is_triton_model("yolo11n.pt")
        False
    """
    from urllib.parse import urlsplit

    url = urlsplit(model)
    return url.netloc and url.path and url.scheme in {"http", "grpc"}

def load(self, weights: Union[str, Path] = "yolo11n.pt") -> "Model":
    """
    Loads parameters from the specified weights file into the model.

    This method supports loading weights from a file or directly from a weights object. It matches parameters by
    name and shape and transfers them to the model.

    Args:
        weights (Union[str, Path]): Path to the weights file or a weights object.

    Returns:
        (Model): The instance of the class with loaded weights.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = Model()
        >>> model.load("yolo11n.pt")
        >>> model.load(Path("path/to/weights.pt"))
    """
    self._check_is_pytorch_model()
    if isinstance(weights, (str, Path)):
        self.overrides["pretrained"] = weights  # remember the weights for DDP training
        weights, self.ckpt = attempt_load_one_weight(weights)
    self.model.load(weights)
    return self

def predict(
    self,
    source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
    stream: bool = False,
    predictor=None,
    **kwargs,
) -> List[Results]:
    """
    Performs predictions on the given image source using the YOLO model.

    This method facilitates the prediction process, allowing various configurations through keyword arguments.
    It supports predictions with custom predictors or the default predictor method. The method handles different
    types of image sources and can operate in a streaming mode.

    Args:
        source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source
            of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL
            images, numpy arrays, and torch tensors.
        stream (bool): If True, treats the input source as a continuous stream for predictions.
        predictor (BasePredictor | None): An instance of a custom predictor class for making predictions.
            If None, the method uses a default predictor.
        **kwargs (Any): Additional keyword arguments for configuring the prediction process.

    Returns:
        (List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
            Results object.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model.predict(source="path/to/image.jpg", conf=0.25)
        >>> for r in results:
        ...     print(r.boxes.data)  # print detection bounding boxes

    Notes:
        - If 'source' is not provided, it defaults to the ASSETS constant with a warning.
        - The method sets up a new predictor if not already present and updates its arguments with each call.
        - For SAM-type models, 'prompts' can be passed as a keyword argument.
    """
    if source is None:
        source = ASSETS
        LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")

    is_cli = (ARGV[0].endswith("yolo") or ARGV[0].endswith("ultralytics")) and any(
        x in ARGV for x in ("predict", "track", "mode=predict", "mode=track")
    )

    custom = {"conf": 0.25, "batch": 1, "save": is_cli, "mode": "predict"}  # method defaults
    args = {**self.overrides, **custom, **kwargs}  # highest priority args on the right
    prompts = args.pop("prompts", None)  # for SAM-type models

    if not self.predictor:
        self.predictor = (predictor or self._smart_load("predictor"))(overrides=args, _callbacks=self.callbacks)
        self.predictor.setup_model(model=self.model, verbose=is_cli)
    else:  # only update args if predictor is already setup
        self.predictor.args = get_cfg(self.predictor.args, args)
        if "project" in args or "name" in args:
            self.predictor.save_dir = get_save_dir(self.predictor.args)
    if prompts and hasattr(self.predictor, "set_prompts"):  # for SAM-type models
        self.predictor.set_prompts(prompts)
    return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)

def reset_callbacks(self) -> None:
    """
    Resets all callbacks to their default functions.

    This method reinstates the default callback functions for all events, removing any custom callbacks that were
    previously added. It iterates through all default callback events and replaces the current callbacks with the
    default ones.

    The default callbacks are defined in the 'callbacks.default_callbacks' dictionary, which contains predefined
    functions for various events in the model's lifecycle, such as on_train_start, on_epoch_end, etc.

    This method is useful when you want to revert to the original set of callbacks after making custom
    modifications, ensuring consistent behavior across different runs or experiments.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> model.add_callback("on_train_start", custom_function)
        >>> model.reset_callbacks()
        # All callbacks are now reset to their default functions
    """
    for event in callbacks.default_callbacks.keys():
        self.callbacks[event] = [callbacks.default_callbacks[event][0]]

def reset_weights(self) -> "Model":
    """
    Resets the model's weights to their initial state.

    This method iterates through all modules in the model and resets their parameters if they have a
    'reset_parameters' method. It also ensures that all parameters have 'requires_grad' set to True,
    enabling them to be updated during training.

    Returns:
        (Model): The instance of the class with reset weights.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = Model("yolo11n.pt")
        >>> model.reset_weights()
    """
    self._check_is_pytorch_model()
    for m in self.model.modules():
        if hasattr(m, "reset_parameters"):
            m.reset_parameters()
    for p in self.model.parameters():
        p.requires_grad = True
    return self

def save(self, filename: Union[str, Path] = "saved_model.pt") -> None:
    """
    Saves the current model state to a file.

    This method exports the model's checkpoint (ckpt) to the specified filename. It includes metadata such as
    the date, Ultralytics version, license information, and a link to the documentation.

    Args:
        filename (Union[str, Path]): The name of the file to save the model to.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = Model("yolo11n.pt")
        >>> model.save("my_model.pt")
    """
    self._check_is_pytorch_model()
    from copy import deepcopy
    from datetime import datetime

    from ultralytics import __version__

    updates = {
        "model": deepcopy(self.model).half() if isinstance(self.model, nn.Module) else self.model,
        "date": datetime.now().isoformat(),
        "version": __version__,
        "license": "AGPL-3.0 License (https://ultralytics.com/license)",
        "docs": "https://docs.ultralytics.com",
    }
    torch.save({**self.ckpt, **updates}, filename)

def track(
    self,
    source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
    stream: bool = False,
    persist: bool = False,
    **kwargs,
) -> List[Results]:
    """
    Conducts object tracking on the specified input source using the registered trackers.

    This method performs object tracking using the model's predictors and optionally registered trackers. It handles
    various input sources such as file paths or video streams, and supports customization through keyword arguments.
    The method registers trackers if not already present and can persist them between calls.

    Args:
        source (Union[str, Path, int, List, Tuple, np.ndarray, torch.Tensor], optional): Input source for object
            tracking. Can be a file path, URL, or video stream.
        stream (bool): If True, treats the input source as a continuous video stream. Defaults to False.
        persist (bool): If True, persists trackers between different calls to this method. Defaults to False.
        **kwargs (Any): Additional keyword arguments for configuring the tracking process.

    Returns:
        (List[ultralytics.engine.results.Results]): A list of tracking results, each a Results object.

    Raises:
        AttributeError: If the predictor does not have registered trackers.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model.track(source="path/to/video.mp4", show=True)
        >>> for r in results:
        ...     print(r.boxes.id)  # print tracking IDs

    Notes:
        - This method sets a default confidence threshold of 0.1 for ByteTrack-based tracking.
        - The tracking mode is explicitly set in the keyword arguments.
        - Batch size is set to 1 for tracking in videos.
    """
    if not hasattr(self.predictor, "trackers"):
        from ultralytics.trackers import register_tracker

        register_tracker(self, persist)
    kwargs["conf"] = kwargs.get("conf") or 0.1  # ByteTrack-based method needs low confidence predictions as input
    kwargs["batch"] = kwargs.get("batch") or 1  # batch-size 1 for tracking in videos
    kwargs["mode"] = "track"
    return self.predict(source=source, stream=stream, **kwargs)

def train(
    self,
    trainer=None,
    **kwargs,
):
    """
    Trains the model using the specified dataset and training configuration.

    This method facilitates model training with a range of customizable settings. It supports training with a
    custom trainer or the default training approach. The method handles scenarios such as resuming training
    from a checkpoint, integrating with Ultralytics HUB, and updating model and configuration after training.

    When using Ultralytics HUB, if the session has a loaded model, the method prioritizes HUB training
    arguments and warns if local arguments are provided. It checks for pip updates and combines default
    configurations, method-specific defaults, and user-provided arguments to configure the training process.

    Args:
        trainer (BaseTrainer | None): Custom trainer instance for model training. If None, uses default.
        **kwargs (Any): Arbitrary keyword arguments for training configuration. Common options include:
            data (str): Path to dataset configuration file.
            epochs (int): Number of training epochs.
            batch_size (int): Batch size for training.
            imgsz (int): Input image size.
            device (str): Device to run training on (e.g., 'cuda', 'cpu').
            workers (int): Number of worker threads for data loading.
            optimizer (str): Optimizer to use for training.
            lr0 (float): Initial learning rate.
            patience (int): Epochs to wait for no observable improvement for early stopping of training.

    Returns:
        (Dict | None): Training metrics if available and training is successful; otherwise, None.

    Raises:
        AssertionError: If the model is not a PyTorch model.
        PermissionError: If there is a permission issue with the HUB session.
        ModuleNotFoundError: If the HUB SDK is not installed.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model.train(data="coco8.yaml", epochs=3)
    """
    self._check_is_pytorch_model()
    if hasattr(self.session, "model") and self.session.model.id:  # Ultralytics HUB session with loaded model
        if any(kwargs):
            LOGGER.warning("WARNING ⚠️ using HUB training arguments, ignoring local training arguments.")
        kwargs = self.session.train_args  # overwrite kwargs

    checks.check_pip_update_available()

    overrides = yaml_load(checks.check_yaml(kwargs["cfg"])) if kwargs.get("cfg") else self.overrides
    custom = {
        # NOTE: handle the case when 'cfg' includes 'data'.
        "data": overrides.get("data") or DEFAULT_CFG_DICT["data"] or TASK2DATA[self.task],
        "model": self.overrides["model"],
        "task": self.task,
    }  # method defaults
    args = {**overrides, **custom, **kwargs, "mode": "train"}  # highest priority args on the right
    if args.get("resume"):
        args["resume"] = self.ckpt_path

    self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks)
    if not args.get("resume"):  # manually set model only if not resuming
        self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
        self.model = self.trainer.model

    self.trainer.hub_session = self.session  # attach optional HUB session
    self.trainer.train()
    # Update model and cfg after training
    if RANK in {-1, 0}:
        ckpt = self.trainer.best if self.trainer.best.exists() else self.trainer.last
        self.model, _ = attempt_load_one_weight(ckpt)
        self.overrides = self.model.args
        self.metrics = getattr(self.trainer.validator, "metrics", None)  # TODO: no metrics returned by DDP
    return self.metrics

def tune(
    self,
    use_ray=False,
    iterations=10,
    *args,
    **kwargs,
):
    """
    Conducts hyperparameter tuning for the model, with an option to use Ray Tune.

    This method supports two modes of hyperparameter tuning: using Ray Tune or a custom tuning method.
    When Ray Tune is enabled, it leverages the 'run_ray_tune' function from the ultralytics.utils.tuner module.
    Otherwise, it uses the internal 'Tuner' class for tuning. The method combines default, overridden, and
    custom arguments to configure the tuning process.

    Args:
        use_ray (bool): If True, uses Ray Tune for hyperparameter tuning. Defaults to False.
        iterations (int): The number of tuning iterations to perform. Defaults to 10.
        *args (List): Variable length argument list for additional arguments.
        **kwargs (Dict): Arbitrary keyword arguments. These are combined with the model's overrides and defaults.

    Returns:
        (Dict): A dictionary containing the results of the hyperparameter search.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model.tune(use_ray=True, iterations=20)
        >>> print(results)
    """
    self._check_is_pytorch_model()
    if use_ray:
        from ultralytics.utils.tuner import run_ray_tune

        return run_ray_tune(self, max_samples=iterations, *args, **kwargs)
    else:
        from .tuner import Tuner

        custom = {}  # method defaults
        args = {**self.overrides, **custom, **kwargs, "mode": "train"}  # highest priority args on the right
        return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations)

def val(
    self,
    validator=None,
    **kwargs,
):
    """
    Validates the model using a specified dataset and validation configuration.

    This method facilitates the model validation process, allowing for customization through various settings. It
    supports validation with a custom validator or the default validation approach. The method combines default
    configurations, method-specific defaults, and user-provided arguments to configure the validation process.

    Args:
        validator (ultralytics.engine.validator.BaseValidator | None): An instance of a custom validator class for
            validating the model.
        **kwargs (Any): Arbitrary keyword arguments for customizing the validation process.

    Returns:
        (ultralytics.utils.metrics.DetMetrics): Validation metrics obtained from the validation process.

    Raises:
        AssertionError: If the model is not a PyTorch model.

    Examples:
        >>> model = YOLO("yolo11n.pt")
        >>> results = model.val(data="coco8.yaml", imgsz=640)
        >>> print(results.box.map)  # Print mAP50-95
    """
    custom = {"rect": True}  # method defaults
    args = {**self.overrides, **custom, **kwargs, "mode": "val"}  # highest priority args on the right

    validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks)
    validator(model=self.model)
    self.metrics = validator.metrics
    return validator.metrics