diff --git a/serotiny/datamodules/__init__.py b/serotiny/datamodules/__init__.py
index 7f2f02c8..4004cdb4 100644
--- a/serotiny/datamodules/__init__.py
+++ b/serotiny/datamodules/__init__.py
@@ -1 +1,2 @@
 from .dataframe import DataframeDatamodule
+from .folder import make_folder_dataloader
diff --git a/serotiny/datamodules/folder.py b/serotiny/datamodules/folder.py
new file mode 100644
index 00000000..0b388dc5
--- /dev/null
+++ b/serotiny/datamodules/folder.py
@@ -0,0 +1,84 @@
+from typing import Union, Sequence, Callable, Optional
+from upath import UPath as Path
+from omegaconf import ListConfig
+from monai.data import Dataset, PersistentDataset, DataLoader
+from monai.transforms import Compose
+
+from serotiny.dataframe.transforms.filter import _filter_columns as filter_filenames
+
+
+def make_folder_dataloader(
+    path: Union[Path, str],
+    transforms: Union[Sequence[Callable], Callable],
+    cache_dir: Optional[Union[Path, str]] = None,
+    regex: Optional[str] = None,
+    startswith: Optional[str] = None,
+    endswith: Optional[str] = None,
+    contains: Optional[str] = None,
+    excludes: Optional[str] = None,
+    **dataloader_kwargs
+):
+    """Create a dataloader based on a folder of samples. If no transforms are
+    applied, each sample is a dictionary with a key "input" containing the
+    corresponding path and a key "orig_fname" containing the original filename
+    (with no extension).
+
+    Files can be filtered out of the list with name-based rules, using `regex`,
+    `startswith`, `endswith`, `contains`, `excludes`.
+
+    Parameters
+    ----------
+    path: Union[Path, str],
+        Path to folder
+
+    transforms: Union[Sequence[Callable], Callable],
+        Transforms to apply to each sample
+
+    cache_dir: Optional[Union[Path, str]] = None
+        Path to a directory in which to store cached transformed inputs, to
+        accelerate batch loading.
+
+    regex: Optional[str] = None
+        A string containing a regular expression to be matched
+
+    startswith: Optional[str] = None
+        A substring the matching columns must start with
+
+    endswith: Optional[str] = None
+        A substring the matching columns must end with
+
+    contains: Optional[str] = None
+        A substring the matching columns must contain
+
+    excludes: Optional[str] = None
+        A substring the matching columns must not contain
+
+    dataloader_kwargs:
+        Additional keyword arguments are passed to the
+        torch.utils.data.DataLoader class when instantiating it (aside from
+        `shuffle` which is only used for the train dataloader).
+        Among these args are `num_workers`, `batch_size`, `shuffle`, etc.
+        See the PyTorch docs for more info on these args:
+        https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
+    """
+
+    if isinstance(transforms, (list, tuple, ListConfig)):
+        transforms = Compose(transforms)
+
+    data = filter_filenames(
+        list(map(str, Path(path).glob("*"))),
+        regex,
+        startswith,
+        endswith,
+        contains,
+        excludes,
+    )
+
+    data = [{"input": path, "orig_fname": Path(path).stem} for path in data]
+
+    if cache_dir is not None:
+        dataset = PersistentDataset(data, transform=transforms, cache_dir=cache_dir)
+    else:
+        dataset = Dataset(data, transform=transforms)
+
+    return DataLoader(dataset, **dataloader_kwargs)