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Reference for ultralytics/models/yolo/segment/train.py

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/train.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.segment.train.SegmentationTrainer 

SegmentationTrainer(
    cfg=DEFAULT_CFG, overrides: Optional[Dict] = None, _callbacks=None
)

Bases: DetectionTrainer

A class extending the DetectionTrainer class for training based on a segmentation model.

This trainer specializes in handling segmentation tasks, extending the detection trainer with segmentation-specific functionality including model initialization, validation, and visualization.

Attributes:

Name Type Description
loss_names Tuple[str]

Names of the loss components used during training.

Examples:

>>> from ultralytics.models.yolo.segment import SegmentationTrainer
>>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml", epochs=3)
>>> trainer = SegmentationTrainer(overrides=args)
>>> trainer.train()

This initializes a trainer for segmentation tasks, extending the detection trainer with segmentation-specific functionality. It sets the task to 'segment' and prepares the trainer for training segmentation models.

Parameters:

Name Type Description Default
cfg dict

Configuration dictionary with default training settings.

 DEFAULT_CFG
overrides dict

Dictionary of parameter overrides for the default configuration.

 None
_callbacks list

List of callback functions to be executed during training.

 None

Examples:

>>> from ultralytics.models.yolo.segment import SegmentationTrainer
>>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml", epochs=3)
>>> trainer = SegmentationTrainer(overrides=args)
>>> trainer.train()
Source code in ultralytics/models/yolo/segment/train.py 
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def __init__(self, cfg=DEFAULT_CFG, overrides: Optional[Dict] = None, _callbacks=None):
    """
    Initialize a SegmentationTrainer object.

    This initializes a trainer for segmentation tasks, extending the detection trainer with segmentation-specific
    functionality. It sets the task to 'segment' and prepares the trainer for training segmentation models.

    Args:
        cfg (dict): Configuration dictionary with default training settings.
        overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
        _callbacks (list, optional): List of callback functions to be executed during training.

    Examples:
        >>> from ultralytics.models.yolo.segment import SegmentationTrainer
        >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml", epochs=3)
        >>> trainer = SegmentationTrainer(overrides=args)
        >>> trainer.train()
    """
    if overrides is None:
        overrides = {}
    overrides["task"] = "segment"
    super().__init__(cfg, overrides, _callbacks)

get_model 

get_model(
    cfg: Optional[Union[Dict, str]] = None,
    weights: Optional[Union[str, Path]] = None,
    verbose: bool = True,
)

Initialize and return a SegmentationModel with specified configuration and weights.

Parameters:

Name Type Description Default
cfg dict | str

Model configuration. Can be a dictionary, a path to a YAML file, or None.

 None
weights str | Path

Path to pretrained weights file.

 None
verbose bool

Whether to display model information during initialization.

 True

Returns:

Type Description
SegmentationModel

Initialized segmentation model with loaded weights if specified.

Examples:

>>> trainer = SegmentationTrainer()
>>> model = trainer.get_model(cfg="yolo11n-seg.yaml")
>>> model = trainer.get_model(weights="yolo11n-seg.pt", verbose=False)
Source code in ultralytics/models/yolo/segment/train.py 
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def get_model(
    self, cfg: Optional[Union[Dict, str]] = None, weights: Optional[Union[str, Path]] = None, verbose: bool = True
):
    """
    Initialize and return a SegmentationModel with specified configuration and weights.

    Args:
        cfg (dict | str, optional): Model configuration. Can be a dictionary, a path to a YAML file, or None.
        weights (str | Path, optional): Path to pretrained weights file.
        verbose (bool): Whether to display model information during initialization.

    Returns:
        (SegmentationModel): Initialized segmentation model with loaded weights if specified.

    Examples:
        >>> trainer = SegmentationTrainer()
        >>> model = trainer.get_model(cfg="yolo11n-seg.yaml")
        >>> model = trainer.get_model(weights="yolo11n-seg.pt", verbose=False)
    """
    model = SegmentationModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
    if weights:
        model.load(weights)

    return model

get_validator 

get_validator()

Return an instance of SegmentationValidator for validation of YOLO model.

Source code in ultralytics/models/yolo/segment/train.py 
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def get_validator(self):
    """Return an instance of SegmentationValidator for validation of YOLO model."""
    self.loss_names = "box_loss", "seg_loss", "cls_loss", "dfl_loss"
    return yolo.segment.SegmentationValidator(
        self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
    )

plot_metrics 

plot_metrics()

Plot training/validation metrics.

Source code in ultralytics/models/yolo/segment/train.py 
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def plot_metrics(self):
    """Plot training/validation metrics."""
    plot_results(file=self.csv, segment=True, on_plot=self.on_plot)  # save results.png





📅 Created 1 year ago ✏️ Updated 11 months ago
glenn-jocher jk4e Burhan-Q