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- # Ultralytics YOLO 🚀, AGPL-3.0 license
- from ultralytics.models.yolo.segment import SegmentationValidator
- from ultralytics.utils.metrics import SegmentMetrics
- class FastSAMValidator(SegmentationValidator):
- """
- Custom validation class for fast SAM (Segment Anything Model) segmentation in Ultralytics YOLO framework.
- Extends the SegmentationValidator class, customizing the validation process specifically for fast SAM. This class
- sets the task to 'segment' and uses the SegmentMetrics for evaluation. Additionally, plotting features are disabled
- to avoid errors during validation.
- Attributes:
- dataloader: The data loader object used for validation.
- save_dir (str): The directory where validation results will be saved.
- pbar: A progress bar object.
- args: Additional arguments for customization.
- _callbacks: List of callback functions to be invoked during validation.
- """
- def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
- """
- Initialize the FastSAMValidator class, setting the task to 'segment' and metrics to SegmentMetrics.
- Args:
- dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation.
- save_dir (Path, optional): Directory to save results.
- pbar (tqdm.tqdm): Progress bar for displaying progress.
- args (SimpleNamespace): Configuration for the validator.
- _callbacks (dict): Dictionary to store various callback functions.
- Notes:
- Plots for ConfusionMatrix and other related metrics are disabled in this class to avoid errors.
- """
- super().__init__(dataloader, save_dir, pbar, args, _callbacks)
- self.args.task = "segment"
- self.args.plots = False # disable ConfusionMatrix and other plots to avoid errors
- self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
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