diff --git a/icenet/data/dataset.py b/icenet/data/dataset.py
index b0be569..511979c 100644
--- a/icenet/data/dataset.py
+++ b/icenet/data/dataset.py
@@ -3,12 +3,15 @@
 import logging
 import os
 
+import dask
 import numpy as np
+import pandas as pd
 
 from icenet.data.datasets.utils import SplittingMixin
 from icenet.data.loader import IceNetDataLoaderFactory
 from icenet.data.producers import DataCollection
 from icenet.utils import setup_logging
+
 """
 
 
@@ -50,7 +53,7 @@ def __init__(self,
         Args:
             configuration_path: The path to the JSON configuration file.
             *args: Additional positional arguments.
-            batch_size (optional): The batch size for the data loader. Defaults to 4.
+            batch_size (optional): How many samples to load per batch. Defaults to 4.
             path (optional): The path to the directory where the processed tfrecord
                 protocol buffer files will be stored. Defaults to './network_datasets'.
             shuffling (optional): Flag indicating whether to shuffle the data.
@@ -317,6 +320,61 @@ def counts(self):
         return self._config["counts"]
 
 
+try:
+    from torch.utils.data import Dataset
+    class IceNetDataSetPyTorch(IceNetDataSet, Dataset):
+        """Initialises and configures a PyTorch dataset.
+        """
+        def __init__(
+            self,
+            configuration_path: str,
+            mode: str,
+            batch_size: int = 1,
+            shuffling: bool = False,
+        ):
+            """Initialises an instance of the IceNetDataSetPyTorch class.
+
+            Args:
+                configuration_path: The path to the JSON configuration file.
+                mode: The dataset type, i.e. `train`, `val` or `test`.
+                batch_size (optional): How many samples to load per batch. Defaults to 1.
+                shuffling (optional): Flag indicating whether to shuffle the data.
+                    Defaults to False.
+            """
+            super().__init__(configuration_path=configuration_path,
+                             batch_size=batch_size,
+                             shuffling=shuffling)
+            self._dl = self.get_data_loader()
+
+            # check mode option
+            if mode not in ["train", "val", "test"]:
+                raise ValueError("mode must be either 'train', 'val', 'test'")
+            self._mode = mode
+
+            self._dates = self._dl._config["sources"]["osisaf"]["dates"][self._mode]
+
+        def __len__(self):
+            return self._counts[self._mode]
+
+        def __getitem__(self, idx):
+            """Return a sample from the dataloader for given index.
+            """
+            with dask.config.set(scheduler="synchronous"):
+                sample = self._dl.generate_sample(
+                    date=pd.Timestamp(self._dates[idx].replace('_', '-')),
+                    parallel=False,
+                )
+            return sample
+
+        @property
+        def dates(self):
+            return self._dates
+except ModuleNotFoundError:
+    logging.warning("PyTorch module not found - not mandatory if not using PyTorch")
+except ImportError:
+    logging.warning("PyTorch import failed - not mandatory if not using PyTorch")
+
+
 @setup_logging
 def get_args() -> object:
     """Parse command line arguments using the argparse module.