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

Note

Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/train.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.segment.train.SegmentationTrainer

Bases: DetectionTrainer

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

Example
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
class SegmentationTrainer(yolo.detect.DetectionTrainer):
    """
    A class extending the DetectionTrainer class for training based on a segmentation model.

    Example:
        ```python
        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()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initialize a SegmentationTrainer object with given arguments."""
        if overrides is None:
            overrides = {}
        overrides['task'] = 'segment'
        super().__init__(cfg, overrides, _callbacks)

    def get_model(self, cfg=None, weights=None, verbose=True):
        """Return SegmentationModel initialized with specified config and weights."""
        model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
        if weights:
            model.load(weights)

        return model

    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))

    def plot_training_samples(self, batch, ni):
        """Creates a plot of training sample images with labels and box coordinates."""
        plot_images(batch['img'],
                    batch['batch_idx'],
                    batch['cls'].squeeze(-1),
                    batch['bboxes'],
                    batch['masks'],
                    paths=batch['im_file'],
                    fname=self.save_dir / f'train_batch{ni}.jpg',
                    on_plot=self.on_plot)

    def plot_metrics(self):
        """Plots training/val metrics."""
        plot_results(file=self.csv, segment=True, on_plot=self.on_plot)  # save results.png

__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Initialize a SegmentationTrainer object with given arguments.

Source code in ultralytics/models/yolo/segment/train.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initialize a SegmentationTrainer object with given arguments."""
    if overrides is None:
        overrides = {}
    overrides['task'] = 'segment'
    super().__init__(cfg, overrides, _callbacks)

get_model(cfg=None, weights=None, verbose=True)

Return SegmentationModel initialized with specified config and weights.

Source code in ultralytics/models/yolo/segment/train.py
def get_model(self, cfg=None, weights=None, verbose=True):
    """Return SegmentationModel initialized with specified config and weights."""
    model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
    if weights:
        model.load(weights)

    return model

get_validator()

Return an instance of SegmentationValidator for validation of YOLO model.

Source code in ultralytics/models/yolo/segment/train.py
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))

plot_metrics()

Plots training/val metrics.

Source code in ultralytics/models/yolo/segment/train.py
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(batch, ni)

Creates a plot of training sample images with labels and box coordinates.

Source code in ultralytics/models/yolo/segment/train.py
def plot_training_samples(self, batch, ni):
    """Creates a plot of training sample images with labels and box coordinates."""
    plot_images(batch['img'],
                batch['batch_idx'],
                batch['cls'].squeeze(-1),
                batch['bboxes'],
                batch['masks'],
                paths=batch['im_file'],
                fname=self.save_dir / f'train_batch{ni}.jpg',
                on_plot=self.on_plot)




Created 2023-07-16, Updated 2023-08-21
Authors: glenn-jocher (6), Laughing-q (1)