add yolo v10 and modify pipeline

ultralytics/models/fastsam/val.py

@@ -5,10 +5,36 @@ 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 SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
+        """
+        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.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)