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Referência para ultralytics/models/yolo/segment/train.py

Nota

Este ficheiro est├í dispon├şvel em https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/models/ yolo/segment/train .py. Se detectares um problema, por favor ajuda a corrigi-lo contribuindo com um Pull Request ­čŤá´ŞĆ. Obrigado ­čÖĆ!



ultralytics.models.yolo.segment.train.SegmentationTrainer

Bases: DetectionTrainer

Uma classe que estende a classe DetectionTrainer para treino baseado num modelo de segmenta├ž├úo.

Exemplo
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()
C├│digo fonte em 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), _callbacks=self.callbacks
        )

    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"],
            masks=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)

Inicializa um objeto SegmentationTrainer com os argumentos fornecidos.

C├│digo fonte em 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)

Retorna SegmentationModel inicializado com a configura├ž├úo e os pesos especificados.

C├│digo fonte em 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()

Devolve uma inst├óncia de SegmentationValidator para valida├ž├úo do modelo YOLO .

C├│digo fonte em 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), _callbacks=self.callbacks
    )

plot_metrics()

Plota as m├ętricas de treino/avalia├ž├úo.

C├│digo fonte em 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)

Cria um gráfico de imagens de amostra de treino com etiquetas e coordenadas de caixa.

C├│digo fonte em 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"],
        masks=batch["masks"],
        paths=batch["im_file"],
        fname=self.save_dir / f"train_batch{ni}.jpg",
        on_plot=self.on_plot,
    )





Criado em 2023-11-12, Atualizado em 2023-11-25
Autores: glenn-jocher (3)