diff --git a/cyto_dl/eval.py b/cyto_dl/eval.py
index 9e37b4bd..d437599a 100644
--- a/cyto_dl/eval.py
+++ b/cyto_dl/eval.py
@@ -82,9 +82,11 @@ def evaluate(cfg: DictConfig, data=None) -> Tuple[dict, dict, dict]:
         log.info("Logging hyperparameters!")
         utils.log_hyperparameters(object_dict)
 
+    model, load_params = utils.load_checkpoint(model, cfg.get("checkpoint"))
+
     log.info("Starting testing!")
     method = trainer.test if cfg.get("test", False) else trainer.predict
-    output = method(model=model, dataloaders=data, ckpt_path=cfg.checkpoint.ckpt_path)
+    output = method(model=model, dataloaders=data, ckpt_path=load_params.ckpt_path)
     metric_dict = trainer.callback_metrics
 
     return metric_dict, object_dict, output
diff --git a/cyto_dl/train.py b/cyto_dl/train.py
index a6f64398..ee359a91 100644
--- a/cyto_dl/train.py
+++ b/cyto_dl/train.py
@@ -95,17 +95,7 @@ def train(cfg: DictConfig, data=None) -> Tuple[dict, dict]:
 
     if cfg.get("train"):
         log.info("Starting training!")
-
-        load_params = cfg.get("checkpoint")
-        if load_params.get("weights_only"):
-            assert load_params.get(
-                "ckpt_path"
-            ), "ckpt_path must be provided to with argument weights_only=True"
-            # load model from state dict to get around trainer.max_epochs limit, useful for resuming model training from existing weights
-            state_dict = torch.load(load_params["ckpt_path"])["state_dict"]
-            model.load_state_dict(state_dict, strict=load_params.get("strict", True))
-            load_params["ckpt_path"] = None
-
+        model, load_params = utils.load_checkpoint(model, cfg.get("checkpoint"))
         if isinstance(data, LightningDataModule):
             trainer.fit(model=model, datamodule=data, ckpt_path=load_params.get("ckpt_path"))
         else:
diff --git a/cyto_dl/utils/__init__.py b/cyto_dl/utils/__init__.py
index ac3acaea..371815c0 100644
--- a/cyto_dl/utils/__init__.py
+++ b/cyto_dl/utils/__init__.py
@@ -1,4 +1,5 @@
 from .array import create_dataloader, extract_array_predictions
+from .checkpoint import load_checkpoint
 from .config import kv_to_dict, remove_aux_key
 from .pylogger import get_pylogger
 from .rich_utils import enforce_tags, print_config_tree
diff --git a/cyto_dl/utils/checkpoint.py b/cyto_dl/utils/checkpoint.py
new file mode 100644
index 00000000..7e6e0043
--- /dev/null
+++ b/cyto_dl/utils/checkpoint.py
@@ -0,0 +1,16 @@
+import torch
+
+
+def load_checkpoint(model, load_params):
+    if load_params.get("weights_only"):
+        assert load_params.get(
+            "ckpt_path"
+        ), "ckpt_path must be provided to with argument weights_only=True"
+        # load model from state dict to get around trainer.max_epochs limit, useful for resuming model training from existing weights
+        state_dict = torch.load(load_params["ckpt_path"])["state_dict"]
+        model.load_state_dict(state_dict, strict=load_params.get("strict", True))
+        # set ckpt_path to None to avoid loading checkpoint again with model.fit/model.test
+        load_params["ckpt_path"] = None
+    elif not load_params.get("strict"):
+        raise ValueError("To use `strict=False`, `weights_only` must be set to True")
+    return model, load_params