add yolo v10 and modify pipeline

ultralytics/models/yolo/segment/train.py

@@ -26,12 +26,12 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
         """Initialize a SegmentationTrainer object with given arguments."""
         if overrides is None:
             overrides = {}
-        overrides['task'] = 'segment'
+        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)
+        model = SegmentationModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose and RANK == -1)
         if weights:
             model.load(weights)
 
@@ -39,19 +39,23 @@ class SegmentationTrainer(yolo.detect.DetectionTrainer):
 
     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))
+        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'],
-                    batch['masks'],
-                    paths=batch['im_file'],
-                    fname=self.save_dir / f'train_batch{ni}.jpg',
-                    on_plot=self.on_plot)
+        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."""