Reference for ultralytics/models/rtdetr/train.py#

class RTDETRTrainer(DetectionTrainer):
    """Trainer class for the RT-DETR model developed by Baidu for real-time object detection.

    This class extends the DetectionTrainer class for YOLO to adapt to the specific features and architecture of
    RT-DETR. The model leverages Vision Transformers and has capabilities like IoU-aware query selection and adaptable
    inference speed.

    Attributes:
        loss_names (tuple): Names of the loss components, derived from the loss dict returned by the criterion.
        data (dict): Dataset configuration containing class count and other parameters.
        args (dict): Training arguments and hyperparameters.
        save_dir (Path): Directory to save training results.
        test_loader (DataLoader): DataLoader for validation/testing data.

    Methods:
        get_model: Initialize and return an RT-DETR model for object detection tasks.
        build_dataset: Build and return an RT-DETR dataset for training or validation.
        get_validator: Return a DetectionValidator suitable for RT-DETR model validation.

    Examples:
        >>> from ultralytics.models.rtdetr.train import RTDETRTrainer
        >>> args = dict(model="rtdetr-l.yaml", data="coco8.yaml", imgsz=640, epochs=3)
        >>> trainer = RTDETRTrainer(overrides=args)
        >>> trainer.train()

    Notes:
        - F.grid_sample used in RT-DETR does not support the `deterministic=True` argument.
        - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
    """

def build_dataset(self, img_path: str, mode: str = "val", batch: int | None = None):
    """Build and return an RT-DETR dataset for training or validation.

    Args:
        img_path (str): Path to the folder containing images.
        mode (str): Dataset mode, either 'train' or 'val'.
        batch (int, optional): Batch size for rectangle training.

    Returns:
        (RTDETRDataset): Dataset object for the specific mode.
    """
    return RTDETRDataset(
        img_path=img_path,
        imgsz=self.args.imgsz,
        batch_size=batch,
        augment=mode == "train",
        hyp=self.args,
        rect=False,
        cache=self.args.cache or None,
        single_cls=self.args.single_cls or False,
        prefix=colorstr(f"{mode}: "),
        classes=self.args.classes,
        data=self.data,
        fraction=self.args.fraction if mode == "train" else 1.0,
    )

def get_model(self, cfg: dict | None = None, weights: str | None = None, verbose: bool = True):
    """Initialize and return an RT-DETR model for object detection tasks.

    Args:
        cfg (dict, optional): Model configuration.
        weights (str, optional): Path to pre-trained model weights.
        verbose (bool): Verbose logging if True.

    Returns:
        (RTDETRDetectionModel): Initialized model.
    """
    model = self.set_model_names_for_load(
        RTDETRDetectionModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
    )
    if weights:
        model.load(weights)
    return model

def get_validator(self):
    """Return an RTDETRValidator suitable for RT-DETR model validation."""
    return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))