diff --git a/ultralytics/yolo/engine/model.py b/ultralytics/yolo/engine/model.py
index 728eda7..9c89e9b 100644
--- a/ultralytics/yolo/engine/model.py
+++ b/ultralytics/yolo/engine/model.py
@@ -47,6 +47,7 @@ class YOLO:
         self.trainer = None  # trainer object
         self.task = None  # task type
         self.ckpt = None  # if loaded from *.pt
+        self.ckpt_path = None
         self.cfg = None  # if loaded from *.yaml
         self.overrides = {}  # overrides for trainer object
         self.init_disabled = False  # disable model initialization
@@ -78,6 +79,7 @@ class YOLO:
             weights (str): model checkpoint to be loaded
         """
         self.model = attempt_load_weights(weights)
+        self.ckpt_path = weights
         self.task = self.model.args["task"]
         self.overrides = self.model.args
         self.overrides["device"] = ''  # reset device
@@ -177,8 +179,8 @@ class YOLO:
         """
         if not self.model:
             raise AttributeError("model not initialized. Use .new() or .load()")
-
-        overrides = kwargs
+        overrides = self.overrides.copy()
+        overrides.update(kwargs)
         if kwargs.get("cfg"):
             LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")
             overrides = yaml_load(check_yaml(kwargs["cfg"]))
@@ -187,10 +189,13 @@ class YOLO:
         if not overrides.get("data"):
             raise AttributeError("dataset not provided! Please define `data` in config.yaml or pass as an argument.")
 
+        if overrides.get("resume"):
+            overrides["resume"] = self.ckpt_path
         self.trainer = self.TrainerClass(overrides=overrides)
-        self.trainer.model = self.trainer.load_model(weights=self.model,
-                                                     model_cfg=self.model.yaml if self.task != "classify" else None)
-        self.model = self.trainer.model  # override here to save memory
+        if not overrides.get("resume"):
+            self.trainer.model = self.trainer.load_model(weights=self.model,
+                                                         model_cfg=self.model.yaml if self.task != "classify" else None)
+            self.model = self.trainer.model  # override here to save memory
 
         self.trainer.train()
 
diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py
index f8f146d..487114e 100644
--- a/ultralytics/yolo/v8/segment/train.py
+++ b/ultralytics/yolo/v8/segment/train.py
@@ -17,9 +17,12 @@ from ultralytics.yolo.utils.torch_utils import de_parallel
 class SegmentationTrainer(v8.detect.DetectionTrainer):
 
     def load_model(self, model_cfg=None, weights=None, verbose=True):
-        model = SegmentationModel(model_cfg or weights.yaml, ch=3, nc=self.data["nc"], verbose=verbose)
+        model = SegmentationModel(model_cfg or getattr(weights, 'yaml', None) or weights['model'].yaml,
+                                  ch=3,
+                                  nc=self.data["nc"],
+                                  verbose=verbose)
         if weights:
-            model.load(weights, verbose)
+            model.load(weights['model'] if isinstance(weights, dict) else weights, verbose)
         return model
 
     def get_validator(self):