diff --git a/anomalib/data/folder.py b/anomalib/data/folder.py
index 6f1020b263..1f57bf62e4 100644
--- a/anomalib/data/folder.py
+++ b/anomalib/data/folder.py
@@ -75,7 +75,10 @@ def _prepare_files_labels(
     if extensions is None:
         extensions = IMG_EXTENSIONS
 
-    filenames = [f for f in path.glob(r"**/*") if f.suffix in extensions]
+    if isinstance(extensions, str):
+        extensions = (extensions,)
+
+    filenames = [f for f in path.glob(r"**/*") if f.suffix in extensions and not f.is_dir()]
     if len(filenames) == 0:
         raise RuntimeError(f"Found 0 {path_type} images in {path}")
 
@@ -141,11 +144,10 @@ def make_dataset(
     # If a path to mask is provided, add it to the sample dataframe.
     if mask_dir is not None:
         mask_dir = _check_and_convert_path(mask_dir)
-        normal_gt = ["" for f in samples.loc[samples.label_index == 0]["image_path"]]
-        abnormal_gt = [str(mask_dir / f.name) for f in samples.loc[samples.label_index == 1]["image_path"]]
-        gt_filenames = normal_gt + abnormal_gt
-
-        samples["mask_path"] = gt_filenames
+        samples["mask_path"] = ""
+        for index, row in samples.iterrows():
+            if row.label_index == 1:
+                samples["mask_path"][index] = str(mask_dir / row.image_path.name)
 
     # Ensure the pathlib objects are converted to str.
     # This is because torch dataloader doesn't like pathlib.
@@ -463,6 +465,7 @@ def setup(self, stage: Optional[str] = None) -> None:
             self.train_data = FolderDataset(
                 normal_dir=self.normal_dir,
                 abnormal_dir=self.abnormal_dir,
+                normal_test_dir=self.normal_test,
                 split="train",
                 split_ratio=self.split_ratio,
                 mask_dir=self.mask_dir,
@@ -477,6 +480,7 @@ def setup(self, stage: Optional[str] = None) -> None:
             self.val_data = FolderDataset(
                 normal_dir=self.normal_dir,
                 abnormal_dir=self.abnormal_dir,
+                normal_test_dir=self.normal_test,
                 split="val",
                 split_ratio=self.split_ratio,
                 mask_dir=self.mask_dir,