diff --git a/micro_sam/instance_segmentation.py b/micro_sam/instance_segmentation.py
index c1da9237..f9ba8908 100644
--- a/micro_sam/instance_segmentation.py
+++ b/micro_sam/instance_segmentation.py
@@ -4,6 +4,7 @@
 """
 
 import os
+import warnings
 from abc import ABC
 from copy import deepcopy
 from collections import OrderedDict
@@ -747,6 +748,7 @@ def get_unetr(
     decoder_state: Optional[OrderedDict[str, torch.Tensor]] = None,
     device: Optional[Union[str, torch.device]] = None,
     out_channels: int = 3,
+    flexible_load_checkpoint: bool = False,
 ) -> torch.nn.Module:
     """Get UNETR model for automatic instance segmentation.
 
@@ -756,6 +758,8 @@ def get_unetr(
         decoder_state: Optional decoder state to initialize the weights of the UNETR decoder.
         device: The device.
         out_channels: The number of output channels.
+        flexible_load_checkpoint: Whether to allow reinitialization of parameters
+            which could not be found in the provided decoder state.
 
     Returns:
         The UNETR model.
@@ -775,7 +779,18 @@ def get_unetr(
         unetr_state_dict = unetr.state_dict()
         for k, v in unetr_state_dict.items():
             if not k.startswith("encoder"):
-                unetr_state_dict[k] = decoder_state[k]
+                if flexible_load_checkpoint:  # Whether allow reinitalization of params, if not found.
+                    if k in decoder_state:  # First check whether the key is available in the provided decoder state.
+                        unetr_state_dict[k] = decoder_state[k]
+                    else:  # Otherwise, allow it to initialize it.
+                        warnings.warn(f"Could not find '{k}' in the pretrained state dict. Hence, we reinitialize it.")
+                        unetr_state_dict[k] = v
+
+                else:  # Whether be strict on finding the parameter in the decoder state.
+                    if k not in decoder_state:
+                        raise RuntimeError(f"The parameters for '{k}' could not be found.")
+                    unetr_state_dict[k] = decoder_state[k]
+
         unetr.load_state_dict(unetr_state_dict)
 
     unetr.to(device)