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ultralytics.nn.tasks.BaseModel

Basi: Module

La classe BaseModel serve come classe di base per tutti i modelli della famiglia Ultralytics YOLO .

Codice sorgente in ultralytics/nn/tasks.py
class BaseModel(nn.Module):
    """The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family."""

    def forward(self, x, *args, **kwargs):
        """
        Forward pass of the model on a single scale. Wrapper for `_forward_once` method.

        Args:
            x (torch.Tensor | dict): The input image tensor or a dict including image tensor and gt labels.

        Returns:
            (torch.Tensor): The output of the network.
        """
        if isinstance(x, dict):  # for cases of training and validating while training.
            return self.loss(x, *args, **kwargs)
        return self.predict(x, *args, **kwargs)

    def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
        """
        Perform a forward pass through the network.

        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool):  Print the computation time of each layer if True, defaults to False.
            visualize (bool): Save the feature maps of the model if True, defaults to False.
            augment (bool): Augment image during prediction, defaults to False.
            embed (list, optional): A list of feature vectors/embeddings to return.

        Returns:
            (torch.Tensor): The last output of the model.
        """
        if augment:
            return self._predict_augment(x)
        return self._predict_once(x, profile, visualize, embed)

    def _predict_once(self, x, profile=False, visualize=False, embed=None):
        """
        Perform a forward pass through the network.

        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool):  Print the computation time of each layer if True, defaults to False.
            visualize (bool): Save the feature maps of the model if True, defaults to False.
            embed (list, optional): A list of feature vectors/embeddings to return.

        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt, embeddings = [], [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
            if embed and m.i in embed:
                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max(embed):
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

    def _predict_augment(self, x):
        """Perform augmentations on input image x and return augmented inference."""
        LOGGER.warning(
            f"WARNING ⚠️ {self.__class__.__name__} does not support augmented inference yet. "
            f"Reverting to single-scale inference instead."
        )
        return self._predict_once(x)

    def _profile_one_layer(self, m, x, dt):
        """
        Profile the computation time and FLOPs of a single layer of the model on a given input. Appends the results to
        the provided list.

        Args:
            m (nn.Module): The layer to be profiled.
            x (torch.Tensor): The input data to the layer.
            dt (list): A list to store the computation time of the layer.

        Returns:
            None
        """
        c = m == self.model[-1] and isinstance(x, list)  # is final layer list, copy input as inplace fix
        flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0  # GFLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
        LOGGER.info(f"{dt[-1]:10.2f} {flops:10.2f} {m.np:10.0f}  {m.type}")
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def fuse(self, verbose=True):
        """
        Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
        computation efficiency.

        Returns:
            (nn.Module): The fused model is returned.
        """
        if not self.is_fused():
            for m in self.model.modules():
                if isinstance(m, (Conv, Conv2, DWConv)) and hasattr(m, "bn"):
                    if isinstance(m, Conv2):
                        m.fuse_convs()
                    m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                    delattr(m, "bn")  # remove batchnorm
                    m.forward = m.forward_fuse  # update forward
                if isinstance(m, ConvTranspose) and hasattr(m, "bn"):
                    m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
                    delattr(m, "bn")  # remove batchnorm
                    m.forward = m.forward_fuse  # update forward
                if isinstance(m, RepConv):
                    m.fuse_convs()
                    m.forward = m.forward_fuse  # update forward
            self.info(verbose=verbose)

        return self

    def is_fused(self, thresh=10):
        """
        Check if the model has less than a certain threshold of BatchNorm layers.

        Args:
            thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.

        Returns:
            (bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
        """
        bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k)  # normalization layers, i.e. BatchNorm2d()
        return sum(isinstance(v, bn) for v in self.modules()) < thresh  # True if < 'thresh' BatchNorm layers in model

    def info(self, detailed=False, verbose=True, imgsz=640):
        """
        Prints model information.

        Args:
            detailed (bool): if True, prints out detailed information about the model. Defaults to False
            verbose (bool): if True, prints out the model information. Defaults to False
            imgsz (int): the size of the image that the model will be trained on. Defaults to 640
        """
        return model_info(self, detailed=detailed, verbose=verbose, imgsz=imgsz)

    def _apply(self, fn):
        """
        Applies a function to all the tensors in the model that are not parameters or registered buffers.

        Args:
            fn (function): the function to apply to the model

        Returns:
            (BaseModel): An updated BaseModel object.
        """
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):  # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
            m.stride = fn(m.stride)
            m.anchors = fn(m.anchors)
            m.strides = fn(m.strides)
        return self

    def load(self, weights, verbose=True):
        """
        Load the weights into the model.

        Args:
            weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
            verbose (bool, optional): Whether to log the transfer progress. Defaults to True.
        """
        model = weights["model"] if isinstance(weights, dict) else weights  # torchvision models are not dicts
        csd = model.float().state_dict()  # checkpoint state_dict as FP32
        csd = intersect_dicts(csd, self.state_dict())  # intersect
        self.load_state_dict(csd, strict=False)  # load
        if verbose:
            LOGGER.info(f"Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights")

    def loss(self, batch, preds=None):
        """
        Compute loss.

        Args:
            batch (dict): Batch to compute loss on
            preds (torch.Tensor | List[torch.Tensor]): Predictions.
        """
        if not hasattr(self, "criterion"):
            self.criterion = self.init_criterion()

        preds = self.forward(batch["img"]) if preds is None else preds
        return self.criterion(preds, batch)

    def init_criterion(self):
        """Initialize the loss criterion for the BaseModel."""
        raise NotImplementedError("compute_loss() needs to be implemented by task heads")

forward(x, *args, **kwargs)

Passaggio in avanti del modello su una singola scala. Wrapper per _forward_once metodo.

Parametri:

Nome Tipo Descrizione Predefinito
x Tensor | dict

L'immagine di input tensor o un dict che include l'immagine tensor e le etichette gt.

richiesto

Restituzione:

Tipo Descrizione
Tensor

L'uscita della rete.

Codice sorgente in ultralytics/nn/tasks.py
def forward(self, x, *args, **kwargs):
    """
    Forward pass of the model on a single scale. Wrapper for `_forward_once` method.

    Args:
        x (torch.Tensor | dict): The input image tensor or a dict including image tensor and gt labels.

    Returns:
        (torch.Tensor): The output of the network.
    """
    if isinstance(x, dict):  # for cases of training and validating while training.
        return self.loss(x, *args, **kwargs)
    return self.predict(x, *args, **kwargs)

fuse(verbose=True)

Fusibile il Conv2d() e BatchNorm2d() del modello in un unico strato, al fine di migliorare l'efficienza di calcolo. efficienza di calcolo.

Restituzione:

Tipo Descrizione
Module

Il modello fuso viene restituito.

Codice sorgente in ultralytics/nn/tasks.py
def fuse(self, verbose=True):
    """
    Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
    computation efficiency.

    Returns:
        (nn.Module): The fused model is returned.
    """
    if not self.is_fused():
        for m in self.model.modules():
            if isinstance(m, (Conv, Conv2, DWConv)) and hasattr(m, "bn"):
                if isinstance(m, Conv2):
                    m.fuse_convs()
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, "bn")  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
            if isinstance(m, ConvTranspose) and hasattr(m, "bn"):
                m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
                delattr(m, "bn")  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
            if isinstance(m, RepConv):
                m.fuse_convs()
                m.forward = m.forward_fuse  # update forward
        self.info(verbose=verbose)

    return self

info(detailed=False, verbose=True, imgsz=640)

Stampa le informazioni sul modello.

Parametri:

Nome Tipo Descrizione Predefinito
detailed bool

se True, stampa informazioni dettagliate sul modello. L'impostazione predefinita è False

False
verbose bool

se True, stampa le informazioni sul modello. L'impostazione predefinita è False

True
imgsz int

la dimensione dell'immagine su cui verrà addestrato il modello. Per impostazione predefinita è 640

640
Codice sorgente in ultralytics/nn/tasks.py
def info(self, detailed=False, verbose=True, imgsz=640):
    """
    Prints model information.

    Args:
        detailed (bool): if True, prints out detailed information about the model. Defaults to False
        verbose (bool): if True, prints out the model information. Defaults to False
        imgsz (int): the size of the image that the model will be trained on. Defaults to 640
    """
    return model_info(self, detailed=detailed, verbose=verbose, imgsz=imgsz)

init_criterion()

Inizializza il criterio di perdita per il Modello Base.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the BaseModel."""
    raise NotImplementedError("compute_loss() needs to be implemented by task heads")

is_fused(thresh=10)

Controlla se il modello ha meno di una certa soglia di livelli BatchNorm.

Parametri:

Nome Tipo Descrizione Predefinito
thresh int

Il numero di soglia dei livelli BatchNorm. Il valore predefinito è 10.

10

Restituzione:

Tipo Descrizione
bool

Vero se il numero di livelli BatchNorm nel modello è inferiore alla soglia, Falso altrimenti.

Codice sorgente in ultralytics/nn/tasks.py
def is_fused(self, thresh=10):
    """
    Check if the model has less than a certain threshold of BatchNorm layers.

    Args:
        thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.

    Returns:
        (bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
    """
    bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k)  # normalization layers, i.e. BatchNorm2d()
    return sum(isinstance(v, bn) for v in self.modules()) < thresh  # True if < 'thresh' BatchNorm layers in model

load(weights, verbose=True)

Carica i pesi nel modello.

Parametri:

Nome Tipo Descrizione Predefinito
weights dict | Module

I pesi pre-addestrati da caricare.

richiesto
verbose bool

Se registrare l'avanzamento del trasferimento. Il valore predefinito è Vero.

True
Codice sorgente in ultralytics/nn/tasks.py
def load(self, weights, verbose=True):
    """
    Load the weights into the model.

    Args:
        weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
        verbose (bool, optional): Whether to log the transfer progress. Defaults to True.
    """
    model = weights["model"] if isinstance(weights, dict) else weights  # torchvision models are not dicts
    csd = model.float().state_dict()  # checkpoint state_dict as FP32
    csd = intersect_dicts(csd, self.state_dict())  # intersect
    self.load_state_dict(csd, strict=False)  # load
    if verbose:
        LOGGER.info(f"Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights")

loss(batch, preds=None)

Calcolo della perdita.

Parametri:

Nome Tipo Descrizione Predefinito
batch dict

Batch per calcolare la perdita su

richiesto
preds Tensor | List[Tensor]

Previsioni.

None
Codice sorgente in ultralytics/nn/tasks.py
def loss(self, batch, preds=None):
    """
    Compute loss.

    Args:
        batch (dict): Batch to compute loss on
        preds (torch.Tensor | List[torch.Tensor]): Predictions.
    """
    if not hasattr(self, "criterion"):
        self.criterion = self.init_criterion()

    preds = self.forward(batch["img"]) if preds is None else preds
    return self.criterion(preds, batch)

predict(x, profile=False, visualize=False, augment=False, embed=None)

Esegui un passaggio in avanti attraverso la rete.

Parametri:

Nome Tipo Descrizione Predefinito
x Tensor

L'ingresso tensor al modello.

richiesto
profile bool

Stampa il tempo di calcolo di ogni livello se Vero, per impostazione predefinita è Falso.

False
visualize bool

Salva le mappe delle caratteristiche del modello se Vero, per impostazione predefinita è Falso.

False
augment bool

Aumenta l'immagine durante la predizione; l'impostazione predefinita è False.

False
embed list

Un elenco di vettori di caratteristiche/embeddings da restituire.

None

Restituzione:

Tipo Descrizione
Tensor

L'ultimo output del modello.

Codice sorgente in ultralytics/nn/tasks.py
def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
    """
    Perform a forward pass through the network.

    Args:
        x (torch.Tensor): The input tensor to the model.
        profile (bool):  Print the computation time of each layer if True, defaults to False.
        visualize (bool): Save the feature maps of the model if True, defaults to False.
        augment (bool): Augment image during prediction, defaults to False.
        embed (list, optional): A list of feature vectors/embeddings to return.

    Returns:
        (torch.Tensor): The last output of the model.
    """
    if augment:
        return self._predict_augment(x)
    return self._predict_once(x, profile, visualize, embed)



ultralytics.nn.tasks.DetectionModel

Basi: BaseModel

YOLOv8 modello di rilevamento.

Codice sorgente in ultralytics/nn/tasks.py
class DetectionModel(BaseModel):
    """YOLOv8 detection model."""

    def __init__(self, cfg="yolov8n.yaml", ch=3, nc=None, verbose=True):  # model, input channels, number of classes
        """Initialize the YOLOv8 detection model with the given config and parameters."""
        super().__init__()
        self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
        if self.yaml["backbone"][0][2] == "Silence":
            LOGGER.warning(
                "WARNING ⚠️ YOLOv9 `Silence` module is deprecated in favor of nn.Identity. "
                "Please delete local *.pt file and re-download the latest model checkpoint."
            )
            self.yaml["backbone"][0][2] = "nn.Identity"

        # Define model
        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
        if nc and nc != self.yaml["nc"]:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml["nc"] = nc  # override YAML value
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
        self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict
        self.inplace = self.yaml.get("inplace", True)

        # Build strides
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):  # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
            s = 256  # 2x min stride
            m.inplace = self.inplace

            def _forward(x):
                """Performs a forward pass through the model, handling different Detect subclass types accordingly."""
                return self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)

            m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))])  # forward
            self.stride = m.stride
            m.bias_init()  # only run once
        else:
            self.stride = torch.Tensor([32])  # default stride for i.e. RTDETR

        # Init weights, biases
        initialize_weights(self)
        if verbose:
            self.info()
            LOGGER.info("")

    def _predict_augment(self, x):
        """Perform augmentations on input image x and return augmented inference and train outputs."""
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud, 3-lr)
        y = []  # outputs
        for si, fi in zip(s, f):
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = super().predict(xi)[0]  # forward
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, -1), None  # augmented inference, train

    @staticmethod
    def _descale_pred(p, flips, scale, img_size, dim=1):
        """De-scale predictions following augmented inference (inverse operation)."""
        p[:, :4] /= scale  # de-scale
        x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
        if flips == 2:
            y = img_size[0] - y  # de-flip ud
        elif flips == 3:
            x = img_size[1] - x  # de-flip lr
        return torch.cat((x, y, wh, cls), dim)

    def _clip_augmented(self, y):
        """Clip YOLO augmented inference tails."""
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4**x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[-1] // g) * sum(4**x for x in range(e))  # indices
        y[0] = y[0][..., :-i]  # large
        i = (y[-1].shape[-1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][..., i:]  # small
        return y

    def init_criterion(self):
        """Initialize the loss criterion for the DetectionModel."""
        return v8DetectionLoss(self)

__init__(cfg='yolov8n.yaml', ch=3, nc=None, verbose=True)

Inizializza il modello di rilevamento di YOLOv8 con la configurazione e i parametri indicati.

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="yolov8n.yaml", ch=3, nc=None, verbose=True):  # model, input channels, number of classes
    """Initialize the YOLOv8 detection model with the given config and parameters."""
    super().__init__()
    self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
    if self.yaml["backbone"][0][2] == "Silence":
        LOGGER.warning(
            "WARNING ⚠️ YOLOv9 `Silence` module is deprecated in favor of nn.Identity. "
            "Please delete local *.pt file and re-download the latest model checkpoint."
        )
        self.yaml["backbone"][0][2] = "nn.Identity"

    # Define model
    ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
    if nc and nc != self.yaml["nc"]:
        LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
        self.yaml["nc"] = nc  # override YAML value
    self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
    self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict
    self.inplace = self.yaml.get("inplace", True)

    # Build strides
    m = self.model[-1]  # Detect()
    if isinstance(m, Detect):  # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
        s = 256  # 2x min stride
        m.inplace = self.inplace

        def _forward(x):
            """Performs a forward pass through the model, handling different Detect subclass types accordingly."""
            return self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)

        m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))])  # forward
        self.stride = m.stride
        m.bias_init()  # only run once
    else:
        self.stride = torch.Tensor([32])  # default stride for i.e. RTDETR

    # Init weights, biases
    initialize_weights(self)
    if verbose:
        self.info()
        LOGGER.info("")

init_criterion()

Inizializza il criterio di perdita per il Modello di rilevamento.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the DetectionModel."""
    return v8DetectionLoss(self)



ultralytics.nn.tasks.OBBModel

Basi: DetectionModel

YOLOv8 Modello Oriented Bounding Box (OBB).

Codice sorgente in ultralytics/nn/tasks.py
class OBBModel(DetectionModel):
    """YOLOv8 Oriented Bounding Box (OBB) model."""

    def __init__(self, cfg="yolov8n-obb.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOv8 OBB model with given config and parameters."""
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the model."""
        return v8OBBLoss(self)

__init__(cfg='yolov8n-obb.yaml', ch=3, nc=None, verbose=True)

Inizializza il modello YOLOv8 OBB con la configurazione e i parametri indicati.

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="yolov8n-obb.yaml", ch=3, nc=None, verbose=True):
    """Initialize YOLOv8 OBB model with given config and parameters."""
    super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

init_criterion()

Inizializza il criterio di perdita per il modello.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the model."""
    return v8OBBLoss(self)



ultralytics.nn.tasks.SegmentationModel

Basi: DetectionModel

YOLOv8 modello di segmentazione.

Codice sorgente in ultralytics/nn/tasks.py
class SegmentationModel(DetectionModel):
    """YOLOv8 segmentation model."""

    def __init__(self, cfg="yolov8n-seg.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOv8 segmentation model with given config and parameters."""
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the SegmentationModel."""
        return v8SegmentationLoss(self)

__init__(cfg='yolov8n-seg.yaml', ch=3, nc=None, verbose=True)

Inizializza il modello di segmentazione di YOLOv8 con la configurazione e i parametri indicati.

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="yolov8n-seg.yaml", ch=3, nc=None, verbose=True):
    """Initialize YOLOv8 segmentation model with given config and parameters."""
    super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

init_criterion()

Inizializza il criterio di perdita per il SegmentationModel.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the SegmentationModel."""
    return v8SegmentationLoss(self)



ultralytics.nn.tasks.PoseModel

Basi: DetectionModel

YOLOv8 modello di posa.

Codice sorgente in ultralytics/nn/tasks.py
class PoseModel(DetectionModel):
    """YOLOv8 pose model."""

    def __init__(self, cfg="yolov8n-pose.yaml", ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
        """Initialize YOLOv8 Pose model."""
        if not isinstance(cfg, dict):
            cfg = yaml_model_load(cfg)  # load model YAML
        if any(data_kpt_shape) and list(data_kpt_shape) != list(cfg["kpt_shape"]):
            LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")
            cfg["kpt_shape"] = data_kpt_shape
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the PoseModel."""
        return v8PoseLoss(self)

__init__(cfg='yolov8n-pose.yaml', ch=3, nc=None, data_kpt_shape=(None, None), verbose=True)

Inizializza il modello di posa YOLOv8 .

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="yolov8n-pose.yaml", ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
    """Initialize YOLOv8 Pose model."""
    if not isinstance(cfg, dict):
        cfg = yaml_model_load(cfg)  # load model YAML
    if any(data_kpt_shape) and list(data_kpt_shape) != list(cfg["kpt_shape"]):
        LOGGER.info(f"Overriding model.yaml kpt_shape={cfg['kpt_shape']} with kpt_shape={data_kpt_shape}")
        cfg["kpt_shape"] = data_kpt_shape
    super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

init_criterion()

Inizializza il criterio di perdita per il PoseModel.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the PoseModel."""
    return v8PoseLoss(self)



ultralytics.nn.tasks.ClassificationModel

Basi: BaseModel

YOLOv8 modello di classificazione.

Codice sorgente in ultralytics/nn/tasks.py
class ClassificationModel(BaseModel):
    """YOLOv8 classification model."""

    def __init__(self, cfg="yolov8n-cls.yaml", ch=3, nc=None, verbose=True):
        """Init ClassificationModel with YAML, channels, number of classes, verbose flag."""
        super().__init__()
        self._from_yaml(cfg, ch, nc, verbose)

    def _from_yaml(self, cfg, ch, nc, verbose):
        """Set YOLOv8 model configurations and define the model architecture."""
        self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict

        # Define model
        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
        if nc and nc != self.yaml["nc"]:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml["nc"] = nc  # override YAML value
        elif not nc and not self.yaml.get("nc", None):
            raise ValueError("nc not specified. Must specify nc in model.yaml or function arguments.")
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose)  # model, savelist
        self.stride = torch.Tensor([1])  # no stride constraints
        self.names = {i: f"{i}" for i in range(self.yaml["nc"])}  # default names dict
        self.info()

    @staticmethod
    def reshape_outputs(model, nc):
        """Update a TorchVision classification model to class count 'n' if required."""
        name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1]  # last module
        if isinstance(m, Classify):  # YOLO Classify() head
            if m.linear.out_features != nc:
                m.linear = nn.Linear(m.linear.in_features, nc)
        elif isinstance(m, nn.Linear):  # ResNet, EfficientNet
            if m.out_features != nc:
                setattr(model, name, nn.Linear(m.in_features, nc))
        elif isinstance(m, nn.Sequential):
            types = [type(x) for x in m]
            if nn.Linear in types:
                i = len(types) - 1 - types[::-1].index(nn.Linear)  # last nn.Linear index
                if m[i].out_features != nc:
                    m[i] = nn.Linear(m[i].in_features, nc)
            elif nn.Conv2d in types:
                i = len(types) - 1 - types[::-1].index(nn.Conv2d)  # last nn.Conv2d index
                if m[i].out_channels != nc:
                    m[i] = nn.Conv2d(m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)

    def init_criterion(self):
        """Initialize the loss criterion for the ClassificationModel."""
        return v8ClassificationLoss()

__init__(cfg='yolov8n-cls.yaml', ch=3, nc=None, verbose=True)

Intraprende il ClassificationModel con YAML, canali, numero di classi, flag verbose.

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="yolov8n-cls.yaml", ch=3, nc=None, verbose=True):
    """Init ClassificationModel with YAML, channels, number of classes, verbose flag."""
    super().__init__()
    self._from_yaml(cfg, ch, nc, verbose)

init_criterion()

Inizializza il criterio di perdita per il modello di classificazione.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the ClassificationModel."""
    return v8ClassificationLoss()

reshape_outputs(model, nc) staticmethod

Aggiorna un modello di classificazione TorchVision al numero di classi 'n', se necessario.

Codice sorgente in ultralytics/nn/tasks.py
@staticmethod
def reshape_outputs(model, nc):
    """Update a TorchVision classification model to class count 'n' if required."""
    name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1]  # last module
    if isinstance(m, Classify):  # YOLO Classify() head
        if m.linear.out_features != nc:
            m.linear = nn.Linear(m.linear.in_features, nc)
    elif isinstance(m, nn.Linear):  # ResNet, EfficientNet
        if m.out_features != nc:
            setattr(model, name, nn.Linear(m.in_features, nc))
    elif isinstance(m, nn.Sequential):
        types = [type(x) for x in m]
        if nn.Linear in types:
            i = len(types) - 1 - types[::-1].index(nn.Linear)  # last nn.Linear index
            if m[i].out_features != nc:
                m[i] = nn.Linear(m[i].in_features, nc)
        elif nn.Conv2d in types:
            i = len(types) - 1 - types[::-1].index(nn.Conv2d)  # last nn.Conv2d index
            if m[i].out_channels != nc:
                m[i] = nn.Conv2d(m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)



ultralytics.nn.tasks.RTDETRDetectionModel

Basi: DetectionModel

RTDETR (Real-time DEtection and Tracking using Transformers) Classe del modello di rilevamento.

Questa classe è responsabile della costruzione dell'architettura di RTDETR, della definizione delle funzioni di perdita e della facilitazione dei processi di i processi di formazione e di inferenza. RTDETR è un modello di rilevamento e tracciamento degli oggetti che si estende dalla classe di base DetectionModel.

Attributi:

Nome Tipo Descrizione
cfg str

Il percorso del file di configurazione o una stringa preimpostata. Il valore predefinito è 'rtdetr-l.yaml'.

ch int

Numero di canali di ingresso. Il valore predefinito è 3 (RGB).

nc int

Numero di classi per il rilevamento degli oggetti. Il valore predefinito è Nessuno.

verbose bool

Specifica se le statistiche di riepilogo vengono mostrate durante l'inizializzazione. Il valore predefinito è Vero.

Metodi:

Nome Descrizione
init_criterion

Inizializza il criterio utilizzato per il calcolo delle perdite.

loss

Calcola e restituisce la perdita durante l'allenamento.

predict

Esegue un passaggio in avanti attraverso la rete e restituisce l'output.

Codice sorgente in ultralytics/nn/tasks.py
class RTDETRDetectionModel(DetectionModel):
    """
    RTDETR (Real-time DEtection and Tracking using Transformers) Detection Model class.

    This class is responsible for constructing the RTDETR architecture, defining loss functions, and facilitating both
    the training and inference processes. RTDETR is an object detection and tracking model that extends from the
    DetectionModel base class.

    Attributes:
        cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'.
        ch (int): Number of input channels. Default is 3 (RGB).
        nc (int, optional): Number of classes for object detection. Default is None.
        verbose (bool): Specifies if summary statistics are shown during initialization. Default is True.

    Methods:
        init_criterion: Initializes the criterion used for loss calculation.
        loss: Computes and returns the loss during training.
        predict: Performs a forward pass through the network and returns the output.
    """

    def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
        """
        Initialize the RTDETRDetectionModel.

        Args:
            cfg (str): Configuration file name or path.
            ch (int): Number of input channels.
            nc (int, optional): Number of classes. Defaults to None.
            verbose (bool, optional): Print additional information during initialization. Defaults to True.
        """
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def init_criterion(self):
        """Initialize the loss criterion for the RTDETRDetectionModel."""
        from ultralytics.models.utils.loss import RTDETRDetectionLoss

        return RTDETRDetectionLoss(nc=self.nc, use_vfl=True)

    def loss(self, batch, preds=None):
        """
        Compute the loss for the given batch of data.

        Args:
            batch (dict): Dictionary containing image and label data.
            preds (torch.Tensor, optional): Precomputed model predictions. Defaults to None.

        Returns:
            (tuple): A tuple containing the total loss and main three losses in a tensor.
        """
        if not hasattr(self, "criterion"):
            self.criterion = self.init_criterion()

        img = batch["img"]
        # NOTE: preprocess gt_bbox and gt_labels to list.
        bs = len(img)
        batch_idx = batch["batch_idx"]
        gt_groups = [(batch_idx == i).sum().item() for i in range(bs)]
        targets = {
            "cls": batch["cls"].to(img.device, dtype=torch.long).view(-1),
            "bboxes": batch["bboxes"].to(device=img.device),
            "batch_idx": batch_idx.to(img.device, dtype=torch.long).view(-1),
            "gt_groups": gt_groups,
        }

        preds = self.predict(img, batch=targets) if preds is None else preds
        dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta = preds if self.training else preds[1]
        if dn_meta is None:
            dn_bboxes, dn_scores = None, None
        else:
            dn_bboxes, dec_bboxes = torch.split(dec_bboxes, dn_meta["dn_num_split"], dim=2)
            dn_scores, dec_scores = torch.split(dec_scores, dn_meta["dn_num_split"], dim=2)

        dec_bboxes = torch.cat([enc_bboxes.unsqueeze(0), dec_bboxes])  # (7, bs, 300, 4)
        dec_scores = torch.cat([enc_scores.unsqueeze(0), dec_scores])

        loss = self.criterion(
            (dec_bboxes, dec_scores), targets, dn_bboxes=dn_bboxes, dn_scores=dn_scores, dn_meta=dn_meta
        )
        # NOTE: There are like 12 losses in RTDETR, backward with all losses but only show the main three losses.
        return sum(loss.values()), torch.as_tensor(
            [loss[k].detach() for k in ["loss_giou", "loss_class", "loss_bbox"]], device=img.device
        )

    def predict(self, x, profile=False, visualize=False, batch=None, augment=False, embed=None):
        """
        Perform a forward pass through the model.

        Args:
            x (torch.Tensor): The input tensor.
            profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
            visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
            batch (dict, optional): Ground truth data for evaluation. Defaults to None.
            augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
            embed (list, optional): A list of feature vectors/embeddings to return.

        Returns:
            (torch.Tensor): Model's output tensor.
        """
        y, dt, embeddings = [], [], []  # outputs
        for m in self.model[:-1]:  # except the head part
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
            if embed and m.i in embed:
                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max(embed):
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        head = self.model[-1]
        x = head([y[j] for j in head.f], batch)  # head inference
        return x

__init__(cfg='rtdetr-l.yaml', ch=3, nc=None, verbose=True)

Inizializza il modello RTDETRDetectionModel.

Parametri:

Nome Tipo Descrizione Predefinito
cfg str

Nome o percorso del file di configurazione.

'rtdetr-l.yaml'
ch int

Numero di canali di ingresso.

3
nc int

Numero di classi. Il valore predefinito è Nessuno.

None
verbose bool

Stampa informazioni aggiuntive durante l'inizializzazione. Il valore predefinito è Vero.

True
Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
    """
    Initialize the RTDETRDetectionModel.

    Args:
        cfg (str): Configuration file name or path.
        ch (int): Number of input channels.
        nc (int, optional): Number of classes. Defaults to None.
        verbose (bool, optional): Print additional information during initialization. Defaults to True.
    """
    super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

init_criterion()

Inizializza il criterio di perdita per il modello RTDETRDetectionModel.

Codice sorgente in ultralytics/nn/tasks.py
def init_criterion(self):
    """Initialize the loss criterion for the RTDETRDetectionModel."""
    from ultralytics.models.utils.loss import RTDETRDetectionLoss

    return RTDETRDetectionLoss(nc=self.nc, use_vfl=True)

loss(batch, preds=None)

Calcola la perdita per il lotto di dati dato.

Parametri:

Nome Tipo Descrizione Predefinito
batch dict

Dizionario contenente i dati dell'immagine e dell'etichetta.

richiesto
preds Tensor

Previsioni del modello precalcolate. Il valore predefinito è Nessuno.

None

Restituzione:

Tipo Descrizione
tuple

Una tupla contenente la perdita totale e le tre perdite principali in un sito tensor.

Codice sorgente in ultralytics/nn/tasks.py
def loss(self, batch, preds=None):
    """
    Compute the loss for the given batch of data.

    Args:
        batch (dict): Dictionary containing image and label data.
        preds (torch.Tensor, optional): Precomputed model predictions. Defaults to None.

    Returns:
        (tuple): A tuple containing the total loss and main three losses in a tensor.
    """
    if not hasattr(self, "criterion"):
        self.criterion = self.init_criterion()

    img = batch["img"]
    # NOTE: preprocess gt_bbox and gt_labels to list.
    bs = len(img)
    batch_idx = batch["batch_idx"]
    gt_groups = [(batch_idx == i).sum().item() for i in range(bs)]
    targets = {
        "cls": batch["cls"].to(img.device, dtype=torch.long).view(-1),
        "bboxes": batch["bboxes"].to(device=img.device),
        "batch_idx": batch_idx.to(img.device, dtype=torch.long).view(-1),
        "gt_groups": gt_groups,
    }

    preds = self.predict(img, batch=targets) if preds is None else preds
    dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta = preds if self.training else preds[1]
    if dn_meta is None:
        dn_bboxes, dn_scores = None, None
    else:
        dn_bboxes, dec_bboxes = torch.split(dec_bboxes, dn_meta["dn_num_split"], dim=2)
        dn_scores, dec_scores = torch.split(dec_scores, dn_meta["dn_num_split"], dim=2)

    dec_bboxes = torch.cat([enc_bboxes.unsqueeze(0), dec_bboxes])  # (7, bs, 300, 4)
    dec_scores = torch.cat([enc_scores.unsqueeze(0), dec_scores])

    loss = self.criterion(
        (dec_bboxes, dec_scores), targets, dn_bboxes=dn_bboxes, dn_scores=dn_scores, dn_meta=dn_meta
    )
    # NOTE: There are like 12 losses in RTDETR, backward with all losses but only show the main three losses.
    return sum(loss.values()), torch.as_tensor(
        [loss[k].detach() for k in ["loss_giou", "loss_class", "loss_bbox"]], device=img.device
    )

predict(x, profile=False, visualize=False, batch=None, augment=False, embed=None)

Esegui un passaggio in avanti attraverso il modello.

Parametri:

Nome Tipo Descrizione Predefinito
x Tensor

L'ingresso tensor.

richiesto
profile bool

Se Vero, profila il tempo di calcolo per ogni livello. L'impostazione predefinita è False.

False
visualize bool

Se Vero, salva le mappe delle caratteristiche per la visualizzazione. L'impostazione predefinita è False.

False
batch dict

Dati di verità per la valutazione. Il valore predefinito è Nessuno.

None
augment bool

Se Vero, esegue l'incremento dei dati durante l'inferenza. L'impostazione predefinita è False.

False
embed list

Un elenco di vettori di caratteristiche/embeddings da restituire.

None

Restituzione:

Tipo Descrizione
Tensor

Uscita del modello tensor.

Codice sorgente in ultralytics/nn/tasks.py
def predict(self, x, profile=False, visualize=False, batch=None, augment=False, embed=None):
    """
    Perform a forward pass through the model.

    Args:
        x (torch.Tensor): The input tensor.
        profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
        visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
        batch (dict, optional): Ground truth data for evaluation. Defaults to None.
        augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
        embed (list, optional): A list of feature vectors/embeddings to return.

    Returns:
        (torch.Tensor): Model's output tensor.
    """
    y, dt, embeddings = [], [], []  # outputs
    for m in self.model[:-1]:  # except the head part
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        x = m(x)  # run
        y.append(x if m.i in self.save else None)  # save output
        if visualize:
            feature_visualization(x, m.type, m.i, save_dir=visualize)
        if embed and m.i in embed:
            embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max(embed):
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    head = self.model[-1]
    x = head([y[j] for j in head.f], batch)  # head inference
    return x



ultralytics.nn.tasks.WorldModel

Basi: DetectionModel

YOLOv8 Modello mondiale.

Codice sorgente in ultralytics/nn/tasks.py
class WorldModel(DetectionModel):
    """YOLOv8 World Model."""

    def __init__(self, cfg="yolov8s-world.yaml", ch=3, nc=None, verbose=True):
        """Initialize YOLOv8 world model with given config and parameters."""
        self.txt_feats = torch.randn(1, nc or 80, 512)  # features placeholder
        self.clip_model = None  # CLIP model placeholder
        super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

    def set_classes(self, text, batch=80, cache_clip_model=True):
        """Set classes in advance so that model could do offline-inference without clip model."""
        try:
            import clip
        except ImportError:
            check_requirements("git+https://github.com/ultralytics/CLIP.git")
            import clip

        if (
            not getattr(self, "clip_model", None) and cache_clip_model
        ):  # for backwards compatibility of models lacking clip_model attribute
            self.clip_model = clip.load("ViT-B/32")[0]
        model = self.clip_model if cache_clip_model else clip.load("ViT-B/32")[0]
        device = next(model.parameters()).device
        text_token = clip.tokenize(text).to(device)
        txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
        txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
        txt_feats = txt_feats / txt_feats.norm(p=2, dim=-1, keepdim=True)
        self.txt_feats = txt_feats.reshape(-1, len(text), txt_feats.shape[-1])
        self.model[-1].nc = len(text)

    def predict(self, x, profile=False, visualize=False, txt_feats=None, augment=False, embed=None):
        """
        Perform a forward pass through the model.

        Args:
            x (torch.Tensor): The input tensor.
            profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
            visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
            txt_feats (torch.Tensor): The text features, use it if it's given. Defaults to None.
            augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
            embed (list, optional): A list of feature vectors/embeddings to return.

        Returns:
            (torch.Tensor): Model's output tensor.
        """
        txt_feats = (self.txt_feats if txt_feats is None else txt_feats).to(device=x.device, dtype=x.dtype)
        if len(txt_feats) != len(x):
            txt_feats = txt_feats.repeat(len(x), 1, 1)
        ori_txt_feats = txt_feats.clone()
        y, dt, embeddings = [], [], []  # outputs
        for m in self.model:  # except the head part
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            if isinstance(m, C2fAttn):
                x = m(x, txt_feats)
            elif isinstance(m, WorldDetect):
                x = m(x, ori_txt_feats)
            elif isinstance(m, ImagePoolingAttn):
                txt_feats = m(x, txt_feats)
            else:
                x = m(x)  # run

            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
            if embed and m.i in embed:
                embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
                if m.i == max(embed):
                    return torch.unbind(torch.cat(embeddings, 1), dim=0)
        return x

    def loss(self, batch, preds=None):
        """
        Compute loss.

        Args:
            batch (dict): Batch to compute loss on.
            preds (torch.Tensor | List[torch.Tensor]): Predictions.
        """
        if not hasattr(self, "criterion"):
            self.criterion = self.init_criterion()

        if preds is None:
            preds = self.forward(batch["img"], txt_feats=batch["txt_feats"])
        return self.criterion(preds, batch)

__init__(cfg='yolov8s-world.yaml', ch=3, nc=None, verbose=True)

Inizializza il modello del mondo di YOLOv8 con la configurazione e i parametri indicati.

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self, cfg="yolov8s-world.yaml", ch=3, nc=None, verbose=True):
    """Initialize YOLOv8 world model with given config and parameters."""
    self.txt_feats = torch.randn(1, nc or 80, 512)  # features placeholder
    self.clip_model = None  # CLIP model placeholder
    super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)

loss(batch, preds=None)

Calcolo della perdita.

Parametri:

Nome Tipo Descrizione Predefinito
batch dict

Lotto su cui calcolare la perdita.

richiesto
preds Tensor | List[Tensor]

Previsioni.

None
Codice sorgente in ultralytics/nn/tasks.py
def loss(self, batch, preds=None):
    """
    Compute loss.

    Args:
        batch (dict): Batch to compute loss on.
        preds (torch.Tensor | List[torch.Tensor]): Predictions.
    """
    if not hasattr(self, "criterion"):
        self.criterion = self.init_criterion()

    if preds is None:
        preds = self.forward(batch["img"], txt_feats=batch["txt_feats"])
    return self.criterion(preds, batch)

predict(x, profile=False, visualize=False, txt_feats=None, augment=False, embed=None)

Esegui un passaggio in avanti attraverso il modello.

Parametri:

Nome Tipo Descrizione Predefinito
x Tensor

L'ingresso tensor.

richiesto
profile bool

Se Vero, profila il tempo di calcolo per ogni livello. L'impostazione predefinita è False.

False
visualize bool

Se Vero, salva le mappe delle caratteristiche per la visualizzazione. L'impostazione predefinita è False.

False
txt_feats Tensor

Le caratteristiche del testo, usale se sono date. Il valore predefinito è Nessuno.

None
augment bool

Se Vero, esegue l'incremento dei dati durante l'inferenza. L'impostazione predefinita è False.

False
embed list

Un elenco di vettori di caratteristiche/embeddings da restituire.

None

Restituzione:

Tipo Descrizione
Tensor

Uscita del modello tensor.

Codice sorgente in ultralytics/nn/tasks.py
def predict(self, x, profile=False, visualize=False, txt_feats=None, augment=False, embed=None):
    """
    Perform a forward pass through the model.

    Args:
        x (torch.Tensor): The input tensor.
        profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
        visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
        txt_feats (torch.Tensor): The text features, use it if it's given. Defaults to None.
        augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
        embed (list, optional): A list of feature vectors/embeddings to return.

    Returns:
        (torch.Tensor): Model's output tensor.
    """
    txt_feats = (self.txt_feats if txt_feats is None else txt_feats).to(device=x.device, dtype=x.dtype)
    if len(txt_feats) != len(x):
        txt_feats = txt_feats.repeat(len(x), 1, 1)
    ori_txt_feats = txt_feats.clone()
    y, dt, embeddings = [], [], []  # outputs
    for m in self.model:  # except the head part
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        if isinstance(m, C2fAttn):
            x = m(x, txt_feats)
        elif isinstance(m, WorldDetect):
            x = m(x, ori_txt_feats)
        elif isinstance(m, ImagePoolingAttn):
            txt_feats = m(x, txt_feats)
        else:
            x = m(x)  # run

        y.append(x if m.i in self.save else None)  # save output
        if visualize:
            feature_visualization(x, m.type, m.i, save_dir=visualize)
        if embed and m.i in embed:
            embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flatten
            if m.i == max(embed):
                return torch.unbind(torch.cat(embeddings, 1), dim=0)
    return x

set_classes(text, batch=80, cache_clip_model=True)

Impostare le classi in anticipo in modo che il modello possa fare l'inferenza offline senza il modello di clip.

Codice sorgente in ultralytics/nn/tasks.py
def set_classes(self, text, batch=80, cache_clip_model=True):
    """Set classes in advance so that model could do offline-inference without clip model."""
    try:
        import clip
    except ImportError:
        check_requirements("git+https://github.com/ultralytics/CLIP.git")
        import clip

    if (
        not getattr(self, "clip_model", None) and cache_clip_model
    ):  # for backwards compatibility of models lacking clip_model attribute
        self.clip_model = clip.load("ViT-B/32")[0]
    model = self.clip_model if cache_clip_model else clip.load("ViT-B/32")[0]
    device = next(model.parameters()).device
    text_token = clip.tokenize(text).to(device)
    txt_feats = [model.encode_text(token).detach() for token in text_token.split(batch)]
    txt_feats = txt_feats[0] if len(txt_feats) == 1 else torch.cat(txt_feats, dim=0)
    txt_feats = txt_feats / txt_feats.norm(p=2, dim=-1, keepdim=True)
    self.txt_feats = txt_feats.reshape(-1, len(text), txt_feats.shape[-1])
    self.model[-1].nc = len(text)



ultralytics.nn.tasks.Ensemble

Basi: ModuleList

Ensemble di modelli.

Codice sorgente in ultralytics/nn/tasks.py
class Ensemble(nn.ModuleList):
    """Ensemble of models."""

    def __init__(self):
        """Initialize an ensemble of models."""
        super().__init__()

    def forward(self, x, augment=False, profile=False, visualize=False):
        """Function generates the YOLO network's final layer."""
        y = [module(x, augment, profile, visualize)[0] for module in self]
        # y = torch.stack(y).max(0)[0]  # max ensemble
        # y = torch.stack(y).mean(0)  # mean ensemble
        y = torch.cat(y, 2)  # nms ensemble, y shape(B, HW, C)
        return y, None  # inference, train output

__init__()

Inizializza un insieme di modelli.

Codice sorgente in ultralytics/nn/tasks.py
def __init__(self):
    """Initialize an ensemble of models."""
    super().__init__()

forward(x, augment=False, profile=False, visualize=False)

La funzione genera il livello finale della rete YOLO .

Codice sorgente in ultralytics/nn/tasks.py
def forward(self, x, augment=False, profile=False, visualize=False):
    """Function generates the YOLO network's final layer."""
    y = [module(x, augment, profile, visualize)[0] for module in self]
    # y = torch.stack(y).max(0)[0]  # max ensemble
    # y = torch.stack(y).mean(0)  # mean ensemble
    y = torch.cat(y, 2)  # nms ensemble, y shape(B, HW, C)
    return y, None  # inference, train output



ultralytics.nn.tasks.temporary_modules(modules={}, attributes={})

Gestore del contesto per aggiungere o modificare temporaneamente i moduli nella cache dei moduli di Python(sys.modules).

Questa funzione può essere utilizzata per modificare i percorsi dei moduli in fase di esecuzione. È utile in caso di rifattorizzazione del codice, in cui hai spostato un modulo da una posizione a un'altra, ma vuoi comunque supportare i vecchi percorsi di importazione per la retrocompatibilità.

Parametri:

Nome Tipo Descrizione Predefinito
modules dict

Un dizionario che mappa i percorsi dei vecchi moduli con quelli nuovi.

{}
attributes dict

A dictionary mapping old module attributes to new module attributes.

{}
Esempio
with temporary_modules({'old.module.path': 'new.module.path'}, {'old.module.attribute': 'new.module.attribute'}):
    import old.module.path  # this will now import new.module.path
    from old.module import attribute  # this will now import new.module.attribute
Nota

Le modifiche sono attive solo all'interno del gestore del contesto e vengono annullate una volta che il gestore del contesto esce. Tieni presente che la manipolazione diretta di sys.modules può portare a risultati imprevedibili, soprattutto in applicazioni o librerie di grandi dimensioni. applicazioni o librerie più grandi. Usa questa funzione con cautela.

Codice sorgente in ultralytics/nn/tasks.py
@contextlib.contextmanager
def temporary_modules(modules={}, attributes={}):
    """
    Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`).

    This function can be used to change the module paths during runtime. It's useful when refactoring code,
    where you've moved a module from one location to another, but you still want to support the old import
    paths for backwards compatibility.

    Args:
        modules (dict, optional): A dictionary mapping old module paths to new module paths.
        attributes (dict, optional): A dictionary mapping old module attributes to new module attributes.

    Example:
        ```python
        with temporary_modules({'old.module.path': 'new.module.path'}, {'old.module.attribute': 'new.module.attribute'}):
            import old.module.path  # this will now import new.module.path
            from old.module import attribute  # this will now import new.module.attribute
        ```

    Note:
        The changes are only in effect inside the context manager and are undone once the context manager exits.
        Be aware that directly manipulating `sys.modules` can lead to unpredictable results, especially in larger
        applications or libraries. Use this function with caution.
    """

    import importlib
    import sys

    try:
        # Set attributes in sys.modules under their old name
        for old, new in attributes.items():
            old_module, old_attr = old.rsplit(".", 1)
            new_module, new_attr = new.rsplit(".", 1)
            setattr(
                importlib.import_module(old_module),
                old_attr,
                getattr(importlib.import_module(new_module), new_attr),
            )

        # Set modules in sys.modules under their old name
        for old, new in modules.items():
            sys.modules[old] = importlib.import_module(new)

        yield
    finally:
        # Remove the temporary module paths
        for old in modules:
            if old in sys.modules:
                del sys.modules[old]



ultralytics.nn.tasks.torch_safe_load(weight)

Questa funzione tenta di caricare un modello PyTorch con la funzione torch.load(). Se viene sollevato un ModuleNotFoundError, cattura l'errore, registra un messaggio di avvertimento e tenta di installare il modulo mancante tramite la funzione check_requirements(). Dopo l'installazione, la funzione tenta nuovamente di caricare il modello utilizzando torch.load().

Parametri:

Nome Tipo Descrizione Predefinito
weight str

Il percorso del file del modello PyTorch .

richiesto

Restituzione:

Tipo Descrizione
dict

Il modello PyTorch caricato.

Codice sorgente in ultralytics/nn/tasks.py
def torch_safe_load(weight):
    """
    This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised,
    it catches the error, logs a warning message, and attempts to install the missing module via the
    check_requirements() function. After installation, the function again attempts to load the model using torch.load().

    Args:
        weight (str): The file path of the PyTorch model.

    Returns:
        (dict): The loaded PyTorch model.
    """
    from ultralytics.utils.downloads import attempt_download_asset

    check_suffix(file=weight, suffix=".pt")
    file = attempt_download_asset(weight)  # search online if missing locally
    try:
        with temporary_modules(
            modules={
                "ultralytics.yolo.utils": "ultralytics.utils",
                "ultralytics.yolo.v8": "ultralytics.models.yolo",
                "ultralytics.yolo.data": "ultralytics.data",
            },
            attributes={
                "ultralytics.nn.modules.block.Silence": "torch.nn.Identity",
            },
        ):  # for legacy 8.0 Classify and Pose models
            ckpt = torch.load(file, map_location="cpu")

    except ModuleNotFoundError as e:  # e.name is missing module name
        if e.name == "models":
            raise TypeError(
                emojis(
                    f"ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained "
                    f"with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with "
                    f"YOLOv8 at https://github.com/ultralytics/ultralytics."
                    f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
                    f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'"
                )
            ) from e
        LOGGER.warning(
            f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements."
            f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
            f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
            f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'"
        )
        check_requirements(e.name)  # install missing module
        ckpt = torch.load(file, map_location="cpu")

    if not isinstance(ckpt, dict):
        # File is likely a YOLO instance saved with i.e. torch.save(model, "saved_model.pt")
        LOGGER.warning(
            f"WARNING ⚠️ The file '{weight}' appears to be improperly saved or formatted. "
            f"For optimal results, use model.save('filename.pt') to correctly save YOLO models."
        )
        ckpt = {"model": ckpt.model}

    return ckpt, file  # load



ultralytics.nn.tasks.attempt_load_weights(weights, device=None, inplace=True, fuse=False)

Carica un insieme di modelli pesi=[a,b,c] o un singolo modello pesi=[a] o pesi=a.

Codice sorgente in ultralytics/nn/tasks.py
def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
    """Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a."""

    ensemble = Ensemble()
    for w in weights if isinstance(weights, list) else [weights]:
        ckpt, w = torch_safe_load(w)  # load ckpt
        args = {**DEFAULT_CFG_DICT, **ckpt["train_args"]} if "train_args" in ckpt else None  # combined args
        model = (ckpt.get("ema") or ckpt["model"]).to(device).float()  # FP32 model

        # Model compatibility updates
        model.args = args  # attach args to model
        model.pt_path = w  # attach *.pt file path to model
        model.task = guess_model_task(model)
        if not hasattr(model, "stride"):
            model.stride = torch.tensor([32.0])

        # Append
        ensemble.append(model.fuse().eval() if fuse and hasattr(model, "fuse") else model.eval())  # model in eval mode

    # Module updates
    for m in ensemble.modules():
        if hasattr(m, "inplace"):
            m.inplace = inplace
        elif isinstance(m, nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility

    # Return model
    if len(ensemble) == 1:
        return ensemble[-1]

    # Return ensemble
    LOGGER.info(f"Ensemble created with {weights}\n")
    for k in "names", "nc", "yaml":
        setattr(ensemble, k, getattr(ensemble[0], k))
    ensemble.stride = ensemble[int(torch.argmax(torch.tensor([m.stride.max() for m in ensemble])))].stride
    assert all(ensemble[0].nc == m.nc for m in ensemble), f"Models differ in class counts {[m.nc for m in ensemble]}"
    return ensemble



ultralytics.nn.tasks.attempt_load_one_weight(weight, device=None, inplace=True, fuse=False)

Carica i pesi di un singolo modello.

Codice sorgente in ultralytics/nn/tasks.py
def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
    """Loads a single model weights."""
    ckpt, weight = torch_safe_load(weight)  # load ckpt
    args = {**DEFAULT_CFG_DICT, **(ckpt.get("train_args", {}))}  # combine model and default args, preferring model args
    model = (ckpt.get("ema") or ckpt["model"]).to(device).float()  # FP32 model

    # Model compatibility updates
    model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # attach args to model
    model.pt_path = weight  # attach *.pt file path to model
    model.task = guess_model_task(model)
    if not hasattr(model, "stride"):
        model.stride = torch.tensor([32.0])

    model = model.fuse().eval() if fuse and hasattr(model, "fuse") else model.eval()  # model in eval mode

    # Module updates
    for m in model.modules():
        if hasattr(m, "inplace"):
            m.inplace = inplace
        elif isinstance(m, nn.Upsample) and not hasattr(m, "recompute_scale_factor"):
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility

    # Return model and ckpt
    return model, ckpt



ultralytics.nn.tasks.parse_model(d, ch, verbose=True)

Analizza un dizionario YOLO model.yaml in un modello PyTorch .

Codice sorgente in ultralytics/nn/tasks.py
def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
    """Parse a YOLO model.yaml dictionary into a PyTorch model."""
    import ast

    # Args
    max_channels = float("inf")
    nc, act, scales = (d.get(x) for x in ("nc", "activation", "scales"))
    depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
    if scales:
        scale = d.get("scale")
        if not scale:
            scale = tuple(scales.keys())[0]
            LOGGER.warning(f"WARNING ⚠️ no model scale passed. Assuming scale='{scale}'.")
        depth, width, max_channels = scales[scale]

    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        if verbose:
            LOGGER.info(f"{colorstr('activation:')} {act}")  # print

    if verbose:
        LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
    ch = [ch]
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
        m = getattr(torch.nn, m[3:]) if "nn." in m else globals()[m]  # get module
        for j, a in enumerate(args):
            if isinstance(a, str):
                with contextlib.suppress(ValueError):
                    args[j] = locals()[a] if a in locals() else ast.literal_eval(a)

        n = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gain
        if m in {
            Classify,
            Conv,
            ConvTranspose,
            GhostConv,
            Bottleneck,
            GhostBottleneck,
            SPP,
            SPPF,
            DWConv,
            Focus,
            BottleneckCSP,
            C1,
            C2,
            C2f,
            RepNCSPELAN4,
            ELAN1,
            ADown,
            AConv,
            SPPELAN,
            C2fAttn,
            C3,
            C3TR,
            C3Ghost,
            nn.ConvTranspose2d,
            DWConvTranspose2d,
            C3x,
            RepC3,
        }:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            if m is C2fAttn:
                args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)  # embed channels
                args[2] = int(
                    max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
                )  # num heads

            args = [c1, c2, *args[1:]]
            if m in {BottleneckCSP, C1, C2, C2f, C2fAttn, C3, C3TR, C3Ghost, C3x, RepC3}:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is AIFI:
            args = [ch[f], *args]
        elif m in {HGStem, HGBlock}:
            c1, cm, c2 = ch[f], args[0], args[1]
            args = [c1, cm, c2, *args[2:]]
            if m is HGBlock:
                args.insert(4, n)  # number of repeats
                n = 1
        elif m is ResNetLayer:
            c2 = args[1] if args[3] else args[1] * 4
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn}:
            args.append([ch[x] for x in f])
            if m is Segment:
                args[2] = make_divisible(min(args[2], max_channels) * width, 8)
        elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1
            args.insert(1, [ch[x] for x in f])
        elif m is CBLinear:
            c2 = args[0]
            c1 = ch[f]
            args = [c1, c2, *args[1:]]
        elif m is CBFuse:
            c2 = ch[f[-1]]
        else:
            c2 = ch[f]

        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace("__main__.", "")  # module type
        m.np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type = i, f, t  # attach index, 'from' index, type
        if verbose:
            LOGGER.info(f"{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f}  {t:<45}{str(args):<30}")  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)



ultralytics.nn.tasks.yaml_model_load(path)

Carica un modello YOLOv8 da un file YAML.

Codice sorgente in ultralytics/nn/tasks.py
def yaml_model_load(path):
    """Load a YOLOv8 model from a YAML file."""
    import re

    path = Path(path)
    if path.stem in (f"yolov{d}{x}6" for x in "nsmlx" for d in (5, 8)):
        new_stem = re.sub(r"(\d+)([nslmx])6(.+)?$", r"\1\2-p6\3", path.stem)
        LOGGER.warning(f"WARNING ⚠️ Ultralytics YOLO P6 models now use -p6 suffix. Renaming {path.stem} to {new_stem}.")
        path = path.with_name(new_stem + path.suffix)

    unified_path = re.sub(r"(\d+)([nslmx])(.+)?$", r"\1\3", str(path))  # i.e. yolov8x.yaml -> yolov8.yaml
    yaml_file = check_yaml(unified_path, hard=False) or check_yaml(path)
    d = yaml_load(yaml_file)  # model dict
    d["scale"] = guess_model_scale(path)
    d["yaml_file"] = str(path)
    return d



ultralytics.nn.tasks.guess_model_scale(model_path)

Prende in input il percorso del file YAML di un modello YOLO ed estrae il carattere size della scala del modello. La funzione utilizza la corrispondenza delle espressioni regolari per trovare il modello della scala del modello nel nome del file YAML, che è indicato da n, s, m, l o x. La funzione restituisce il carattere di dimensione della scala del modello come stringa.

Parametri:

Nome Tipo Descrizione Predefinito
model_path str | Path

Il percorso del file YAML del modello YOLO .

richiesto

Restituzione:

Tipo Descrizione
str

Il carattere di grandezza della scala del modello, che può essere n, s, m, l o x.

Codice sorgente in ultralytics/nn/tasks.py
def guess_model_scale(model_path):
    """
    Takes a path to a YOLO model's YAML file as input and extracts the size character of the model's scale. The function
    uses regular expression matching to find the pattern of the model scale in the YAML file name, which is denoted by
    n, s, m, l, or x. The function returns the size character of the model scale as a string.

    Args:
        model_path (str | Path): The path to the YOLO model's YAML file.

    Returns:
        (str): The size character of the model's scale, which can be n, s, m, l, or x.
    """
    with contextlib.suppress(AttributeError):
        import re

        return re.search(r"yolov\d+([nslmx])", Path(model_path).stem).group(1)  # n, s, m, l, or x
    return ""



ultralytics.nn.tasks.guess_model_task(model)

Indovina il compito di un modello PyTorch dalla sua architettura o configurazione.

Parametri:

Nome Tipo Descrizione Predefinito
model Module | dict

PyTorch modello o configurazione del modello in formato YAML.

richiesto

Restituzione:

Tipo Descrizione
str

Compito del modello ('rilevare', 'segmentare', 'classificare', 'posare').

Aumenta:

Tipo Descrizione
SyntaxError

Se non è possibile determinare il compito del modello.

Codice sorgente in ultralytics/nn/tasks.py
def guess_model_task(model):
    """
    Guess the task of a PyTorch model from its architecture or configuration.

    Args:
        model (nn.Module | dict): PyTorch model or model configuration in YAML format.

    Returns:
        (str): Task of the model ('detect', 'segment', 'classify', 'pose').

    Raises:
        SyntaxError: If the task of the model could not be determined.
    """

    def cfg2task(cfg):
        """Guess from YAML dictionary."""
        m = cfg["head"][-1][-2].lower()  # output module name
        if m in {"classify", "classifier", "cls", "fc"}:
            return "classify"
        if m == "detect":
            return "detect"
        if m == "segment":
            return "segment"
        if m == "pose":
            return "pose"
        if m == "obb":
            return "obb"

    # Guess from model cfg
    if isinstance(model, dict):
        with contextlib.suppress(Exception):
            return cfg2task(model)

    # Guess from PyTorch model
    if isinstance(model, nn.Module):  # PyTorch model
        for x in "model.args", "model.model.args", "model.model.model.args":
            with contextlib.suppress(Exception):
                return eval(x)["task"]
        for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
            with contextlib.suppress(Exception):
                return cfg2task(eval(x))

        for m in model.modules():
            if isinstance(m, Segment):
                return "segment"
            elif isinstance(m, Classify):
                return "classify"
            elif isinstance(m, Pose):
                return "pose"
            elif isinstance(m, OBB):
                return "obb"
            elif isinstance(m, (Detect, WorldDetect)):
                return "detect"

    # Guess from model filename
    if isinstance(model, (str, Path)):
        model = Path(model)
        if "-seg" in model.stem or "segment" in model.parts:
            return "segment"
        elif "-cls" in model.stem or "classify" in model.parts:
            return "classify"
        elif "-pose" in model.stem or "pose" in model.parts:
            return "pose"
        elif "-obb" in model.stem or "obb" in model.parts:
            return "obb"
        elif "detect" in model.parts:
            return "detect"

    # Unable to determine task from model
    LOGGER.warning(
        "WARNING ⚠️ Unable to automatically guess model task, assuming 'task=detect'. "
        "Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'."
    )
    return "detect"  # assume detect





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (8), Burhan-Q (1), Laughing-q (1)