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

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ultralytics.models.yolo.segment.train.SegmentationTrainer 

SegmentationTrainer(cfg=DEFAULT_CFG, overrides=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. Defaults to DEFAULT_CFG.

 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="yolov8n-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=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. Defaults to DEFAULT_CFG.
        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="yolov8n-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=None, weights=None, verbose=True)

Initialize and return a SegmentationModel with specified configuration and weights.

Parameters:

Name Type Description Default
cfg dict | str | None

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

 None
weights str | Path | None

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="yolov8n-seg.yaml")
>>> model = trainer.get_model(weights="yolov8n-seg.pt", verbose=False)
Source code in ultralytics/models/yolo/segment/train.py 
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def get_model(self, cfg=None, weights=None, verbose=True):
    """
    Initialize and return a SegmentationModel with specified configuration and weights.

    Args:
        cfg (dict | str | None): Model configuration. Can be a dictionary, a path to a YAML file, or None.
        weights (str | Path | None): 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="yolov8n-seg.yaml")
        >>> model = trainer.get_model(weights="yolov8n-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()

Plots training/val metrics.

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

plot_training_samples 

plot_training_samples(batch, ni)

Plot training sample images with labels, bounding boxes, and masks.

This method creates a visualization of training batch images with their corresponding labels, bounding boxes, and segmentation masks, saving the result to a file for inspection and debugging.

Parameters:

Name Type Description Default
batch dict

Dictionary containing batch data with the following keys: 'img': Images tensor 'batch_idx': Batch indices for each box 'cls': Class labels tensor (squeezed to remove last dimension) 'bboxes': Bounding box coordinates tensor 'masks': Segmentation masks tensor 'im_file': List of image file paths

 required
ni int

Current training iteration number, used for naming the output file.

 required

Examples:

>>> trainer = SegmentationTrainer()
>>> batch = {
...     "img": torch.rand(16, 3, 640, 640),
...     "batch_idx": torch.zeros(16),
...     "cls": torch.randint(0, 80, (16, 1)),
...     "bboxes": torch.rand(16, 4),
...     "masks": torch.rand(16, 640, 640),
...     "im_file": ["image1.jpg", "image2.jpg"],
... }
>>> trainer.plot_training_samples(batch, ni=5)
Source code in ultralytics/models/yolo/segment/train.py 
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def plot_training_samples(self, batch, ni):
    """
    Plot training sample images with labels, bounding boxes, and masks.

    This method creates a visualization of training batch images with their corresponding labels, bounding boxes,
    and segmentation masks, saving the result to a file for inspection and debugging.

    Args:
        batch (dict): Dictionary containing batch data with the following keys:
            'img': Images tensor
            'batch_idx': Batch indices for each box
            'cls': Class labels tensor (squeezed to remove last dimension)
            'bboxes': Bounding box coordinates tensor
            'masks': Segmentation masks tensor
            'im_file': List of image file paths
        ni (int): Current training iteration number, used for naming the output file.

    Examples:
        >>> trainer = SegmentationTrainer()
        >>> batch = {
        ...     "img": torch.rand(16, 3, 640, 640),
        ...     "batch_idx": torch.zeros(16),
        ...     "cls": torch.randint(0, 80, (16, 1)),
        ...     "bboxes": torch.rand(16, 4),
        ...     "masks": torch.rand(16, 640, 640),
        ...     "im_file": ["image1.jpg", "image2.jpg"],
        ... }
        >>> trainer.plot_training_samples(batch, ni=5)
    """
    plot_images(
        batch["img"],
        batch["batch_idx"],
        batch["cls"].squeeze(-1),
        batch["bboxes"],
        masks=batch["masks"],
        paths=batch["im_file"],
        fname=self.save_dir / f"train_batch{ni}.jpg",
        on_plot=self.on_plot,
    )





📅 Created 1 year ago ✏️ Updated 7 months ago
jk4e glenn-jocher Burhan-Q