Reference for ultralytics/models/yolo/yoloe/train_seg.py

Bases: YOLOETrainer, SegmentationTrainer

              flowchart TD
              ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer[YOLOESegTrainer]
              ultralytics.models.yolo.yoloe.train.YOLOETrainer[YOLOETrainer]
              ultralytics.models.yolo.segment.train.SegmentationTrainer[SegmentationTrainer]
              ultralytics.models.yolo.detect.train.DetectionTrainer[DetectionTrainer]
              ultralytics.engine.trainer.BaseTrainer[BaseTrainer]

                              ultralytics.models.yolo.yoloe.train.YOLOETrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                


                ultralytics.models.yolo.segment.train.SegmentationTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.segment.train.SegmentationTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                




              click ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer"
              click ultralytics.models.yolo.yoloe.train.YOLOETrainer href "" "ultralytics.models.yolo.yoloe.train.YOLOETrainer"
              click ultralytics.models.yolo.segment.train.SegmentationTrainer href "" "ultralytics.models.yolo.segment.train.SegmentationTrainer"
              click ultralytics.models.yolo.detect.train.DetectionTrainer href "" "ultralytics.models.yolo.detect.train.DetectionTrainer"
              click ultralytics.engine.trainer.BaseTrainer href "" "ultralytics.engine.trainer.BaseTrainer"
            

Trainer class for YOLOE segmentation models.

This class combines YOLOETrainer and SegmentationTrainer to provide training functionality specifically for YOLOE segmentation models, enabling both object detection and instance segmentation capabilities.

Attributes:

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def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks=None):
    """Initialize the YOLOE Trainer with specified configurations.

    Args:
        cfg (dict): Configuration dictionary with default training settings from DEFAULT_CFG.
        overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
        _callbacks (list, optional): List of callback functions to be applied during training.
    """
    if overrides is None:
        overrides = {}
    assert not overrides.get("compile"), f"Training with 'model={overrides['model']}' requires 'compile=False'"
    overrides["overlap_mask"] = False
    super().__init__(cfg, overrides, _callbacks)

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

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def get_model(self, cfg=None, weights=None, verbose=True):
    """Return YOLOESegModel initialized with specified config and weights.

    Args:
        cfg (dict | str, optional): Model configuration dictionary or YAML file path.
        weights (str, optional): Path to pretrained weights file.
        verbose (bool): Whether to display model information.

    Returns:
        (YOLOESegModel): Initialized YOLOE segmentation model.
    """
    # NOTE: This `nc` here is the max number of different text samples in one image, rather than the actual `nc`.
    # NOTE: Following the official config, nc hard-coded to 80 for now.
    model = YOLOESegModel(
        cfg["yaml_file"] if isinstance(cfg, dict) else cfg,
        ch=self.data["channels"],
        nc=min(self.data["nc"], 80),
        verbose=verbose and RANK == -1,
    )
    if weights:
        model.load(weights)

    return model

get_validator()

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def get_validator(self):
    """Create and return a validator for YOLOE segmentation model evaluation.

    Returns:
        (YOLOESegValidator): Validator for YOLOE segmentation models.
    """
    self.loss_names = "box", "seg", "cls", "dfl"
    return YOLOESegValidator(
        self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
    )

Bases: SegmentationTrainer

              flowchart TD
              ultralytics.models.yolo.yoloe.train_seg.YOLOEPESegTrainer[YOLOEPESegTrainer]
              ultralytics.models.yolo.segment.train.SegmentationTrainer[SegmentationTrainer]
              ultralytics.models.yolo.detect.train.DetectionTrainer[DetectionTrainer]
              ultralytics.engine.trainer.BaseTrainer[BaseTrainer]

                              ultralytics.models.yolo.segment.train.SegmentationTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOEPESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.segment.train.SegmentationTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                




              click ultralytics.models.yolo.yoloe.train_seg.YOLOEPESegTrainer href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOEPESegTrainer"
              click ultralytics.models.yolo.segment.train.SegmentationTrainer href "" "ultralytics.models.yolo.segment.train.SegmentationTrainer"
              click ultralytics.models.yolo.detect.train.DetectionTrainer href "" "ultralytics.models.yolo.detect.train.DetectionTrainer"
              click ultralytics.engine.trainer.BaseTrainer href "" "ultralytics.engine.trainer.BaseTrainer"
            

Fine-tune YOLOESeg model in linear probing way.

This trainer specializes in fine-tuning YOLOESeg models using a linear probing approach, which involves freezing most of the model and only training specific layers for efficient adaptation to new tasks.

Attributes:

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def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks=None):
    """Initialize a SegmentationTrainer object.

    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.
    """
    if overrides is None:
        overrides = {}
    overrides["task"] = "segment"
    super().__init__(cfg, overrides, _callbacks)

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

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def get_model(self, cfg=None, weights=None, verbose=True):
    """Return YOLOESegModel initialized with specified config and weights for linear probing.

    Args:
        cfg (dict | str, optional): Model configuration dictionary or YAML file path.
        weights (str, optional): Path to pretrained weights file.
        verbose (bool): Whether to display model information.

    Returns:
        (YOLOESegModel): Initialized YOLOE segmentation model configured for linear probing.
    """
    # NOTE: This `nc` here is the max number of different text samples in one image, rather than the actual `nc`.
    # NOTE: Following the official config, nc hard-coded to 80 for now.
    model = YOLOESegModel(
        cfg["yaml_file"] if isinstance(cfg, dict) else cfg,
        ch=self.data["channels"],
        nc=self.data["nc"],
        verbose=verbose and RANK == -1,
    )

    del model.model[-1].savpe

    assert weights is not None, "Pretrained weights must be provided for linear probing."
    if weights:
        model.load(weights)

    model.eval()
    names = list(self.data["names"].values())
    # NOTE: `get_text_pe` related to text model and YOLOEDetect.reprta,
    # it'd get correct results as long as loading proper pretrained weights.
    tpe = model.get_text_pe(names)
    model.set_classes(names, tpe)
    model.model[-1].fuse(model.pe)
    model.model[-1].cv3[0][2] = deepcopy(model.model[-1].cv3[0][2]).requires_grad_(True)
    model.model[-1].cv3[1][2] = deepcopy(model.model[-1].cv3[1][2]).requires_grad_(True)
    model.model[-1].cv3[2][2] = deepcopy(model.model[-1].cv3[2][2]).requires_grad_(True)
    del model.pe
    model.train()

    return model

Bases: YOLOETrainerFromScratch, YOLOESegTrainer

              flowchart TD
              ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch[YOLOESegTrainerFromScratch]
              ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch[YOLOETrainerFromScratch]
              ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer[YOLOESegTrainer]
              ultralytics.models.yolo.yoloe.train.YOLOETrainer[YOLOETrainer]
              ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch[WorldTrainerFromScratch]
              ultralytics.models.yolo.world.train.WorldTrainer[WorldTrainer]
              ultralytics.models.yolo.segment.train.SegmentationTrainer[SegmentationTrainer]
              ultralytics.models.yolo.detect.train.DetectionTrainer[DetectionTrainer]
              ultralytics.engine.trainer.BaseTrainer[BaseTrainer]

                              ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch
                                ultralytics.models.yolo.yoloe.train.YOLOETrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                


                ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch --> ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch
                                ultralytics.models.yolo.world.train.WorldTrainer --> ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.world.train.WorldTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                




                ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch
                                ultralytics.models.yolo.yoloe.train.YOLOETrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                


                ultralytics.models.yolo.segment.train.SegmentationTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.segment.train.SegmentationTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                





              click ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch"
              click ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch href "" "ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch"
              click ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer"
              click ultralytics.models.yolo.yoloe.train.YOLOETrainer href "" "ultralytics.models.yolo.yoloe.train.YOLOETrainer"
              click ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch href "" "ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch"
              click ultralytics.models.yolo.world.train.WorldTrainer href "" "ultralytics.models.yolo.world.train.WorldTrainer"
              click ultralytics.models.yolo.segment.train.SegmentationTrainer href "" "ultralytics.models.yolo.segment.train.SegmentationTrainer"
              click ultralytics.models.yolo.detect.train.DetectionTrainer href "" "ultralytics.models.yolo.detect.train.DetectionTrainer"
              click ultralytics.engine.trainer.BaseTrainer href "" "ultralytics.engine.trainer.BaseTrainer"
            

Trainer for YOLOE segmentation models trained from scratch without pretrained weights.

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def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks=None):
    """Initialize the YOLOE Trainer with specified configurations.

    Args:
        cfg (dict): Configuration dictionary with default training settings from DEFAULT_CFG.
        overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
        _callbacks (list, optional): List of callback functions to be applied during training.
    """
    if overrides is None:
        overrides = {}
    assert not overrides.get("compile"), f"Training with 'model={overrides['model']}' requires 'compile=False'"
    overrides["overlap_mask"] = False
    super().__init__(cfg, overrides, _callbacks)

Bases: YOLOEVPTrainer, YOLOESegTrainerFromScratch

              flowchart TD
              ultralytics.models.yolo.yoloe.train_seg.YOLOESegVPTrainer[YOLOESegVPTrainer]
              ultralytics.models.yolo.yoloe.train.YOLOEVPTrainer[YOLOEVPTrainer]
              ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch[YOLOESegTrainerFromScratch]
              ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch[YOLOETrainerFromScratch]
              ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer[YOLOESegTrainer]
              ultralytics.models.yolo.yoloe.train.YOLOETrainer[YOLOETrainer]
              ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch[WorldTrainerFromScratch]
              ultralytics.models.yolo.world.train.WorldTrainer[WorldTrainer]
              ultralytics.models.yolo.segment.train.SegmentationTrainer[SegmentationTrainer]
              ultralytics.models.yolo.detect.train.DetectionTrainer[DetectionTrainer]
              ultralytics.engine.trainer.BaseTrainer[BaseTrainer]

                              ultralytics.models.yolo.yoloe.train.YOLOEVPTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegVPTrainer
                                ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch --> ultralytics.models.yolo.yoloe.train.YOLOEVPTrainer
                                ultralytics.models.yolo.yoloe.train.YOLOETrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                


                ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch --> ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch
                                ultralytics.models.yolo.world.train.WorldTrainer --> ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.world.train.WorldTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                





                ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegVPTrainer
                                ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch
                                ultralytics.models.yolo.yoloe.train.YOLOETrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                


                ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch --> ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch
                                ultralytics.models.yolo.world.train.WorldTrainer --> ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.world.train.WorldTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                




                ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch
                                ultralytics.models.yolo.yoloe.train.YOLOETrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.yoloe.train.YOLOETrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                


                ultralytics.models.yolo.segment.train.SegmentationTrainer --> ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer
                                ultralytics.models.yolo.detect.train.DetectionTrainer --> ultralytics.models.yolo.segment.train.SegmentationTrainer
                                ultralytics.engine.trainer.BaseTrainer --> ultralytics.models.yolo.detect.train.DetectionTrainer
                






              click ultralytics.models.yolo.yoloe.train_seg.YOLOESegVPTrainer href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOESegVPTrainer"
              click ultralytics.models.yolo.yoloe.train.YOLOEVPTrainer href "" "ultralytics.models.yolo.yoloe.train.YOLOEVPTrainer"
              click ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainerFromScratch"
              click ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch href "" "ultralytics.models.yolo.yoloe.train.YOLOETrainerFromScratch"
              click ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer href "" "ultralytics.models.yolo.yoloe.train_seg.YOLOESegTrainer"
              click ultralytics.models.yolo.yoloe.train.YOLOETrainer href "" "ultralytics.models.yolo.yoloe.train.YOLOETrainer"
              click ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch href "" "ultralytics.models.yolo.world.train_world.WorldTrainerFromScratch"
              click ultralytics.models.yolo.world.train.WorldTrainer href "" "ultralytics.models.yolo.world.train.WorldTrainer"
              click ultralytics.models.yolo.segment.train.SegmentationTrainer href "" "ultralytics.models.yolo.segment.train.SegmentationTrainer"
              click ultralytics.models.yolo.detect.train.DetectionTrainer href "" "ultralytics.models.yolo.detect.train.DetectionTrainer"
              click ultralytics.engine.trainer.BaseTrainer href "" "ultralytics.engine.trainer.BaseTrainer"
            

Trainer for YOLOE segmentation models with Vision Prompt (VP) capabilities.

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def __init__(self, cfg=DEFAULT_CFG, overrides: dict | None = None, _callbacks=None):
    """Initialize the YOLOE Trainer with specified configurations.

    Args:
        cfg (dict): Configuration dictionary with default training settings from DEFAULT_CFG.
        overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
        _callbacks (list, optional): List of callback functions to be applied during training.
    """
    if overrides is None:
        overrides = {}
    assert not overrides.get("compile"), f"Training with 'model={overrides['model']}' requires 'compile=False'"
    overrides["overlap_mask"] = False
    super().__init__(cfg, overrides, _callbacks)