diff --git a/autoPyTorch/datasets/base_dataset.py b/autoPyTorch/datasets/base_dataset.py
index a9a5448..f03801d 100644
--- a/autoPyTorch/datasets/base_dataset.py
+++ b/autoPyTorch/datasets/base_dataset.py
@@ -1,7 +1,7 @@
 from abc import ABCMeta
 from torch.utils.data import Dataset, Subset
 import numpy as np
-from typing import Optional, Tuple, List, Any, Dict
+from typing import Optional, Tuple, List, Any, Dict, Union
 from autoPyTorch.datasets.cross_validation import CROSS_VAL_FN, HOLDOUT_FN, is_stratified
 
 
@@ -20,7 +20,9 @@ def type_check(train_tensors: Tuple[Any, ...], val_tensors: Optional[Tuple[Any,
 
 
 class BaseDataset(Dataset, metaclass=ABCMeta):
-    def __init__(self, train_tensors: Tuple[Any, ...], val_tensors: Optional[Tuple[Any, ...]] = None,
+    def __init__(self,
+                 train_tensors: Union[Tuple[Any, ...]],
+                 val_tensors: Optional[Tuple[Any, ...]] = None,
                  shuffle: Optional[bool] = True, seed: Optional[int] = 42):
         """
         :param train_tensors: A tuple of objects that have a __len__ and a __getitem__ attribute.
@@ -39,7 +41,7 @@ def __getitem__(self, index: int) -> Tuple[np.ndarray, ...]:
         return tuple(t[index] for t in self.train_tensors)
 
     def __len__(self) -> int:
-        return self.train_tensors[0].shape[0]
+        return len(self.train_tensors[0])
 
     def _get_indices(self) -> np.ndarray:
         if self.shuffle:
diff --git a/autoPyTorch/datasets/image_dataset.py b/autoPyTorch/datasets/image_dataset.py
index e07c82c..a453372 100644
--- a/autoPyTorch/datasets/image_dataset.py
+++ b/autoPyTorch/datasets/image_dataset.py
@@ -1,5 +1,7 @@
 import numpy as np
-from torch.utils.data import Dataset
+from torch.utils.data import Dataset, TensorDataset
+import torch
+from PIL import Image
 from autoPyTorch.datasets.base_dataset import BaseDataset
 from typing import Tuple, Optional, Union, List
 from autoPyTorch.datasets.cross_validation import k_fold_cross_validation, \
@@ -11,9 +13,11 @@ class ImageDataset(BaseDataset):
     def __init__(self,
                  train: Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]],
                  val: Optional[Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]]] = None):
-        _type_check(train, "train")
         _check_image_inputs(train=train, val=val)
-        super().__init__(train_tensors=train, val_tensors=val, shuffle=True)
+        train = _create_image_dataset(data=train)
+        if val is not None:
+            val = _create_image_dataset(data=val)
+        super().__init__(train_tensors=(train,), val_tensors=(val,), shuffle=True)
         self.cross_validators.update(
             {"k_fold_cross_validation": k_fold_cross_validation}
         )
@@ -23,21 +27,38 @@ def __init__(self,
         )
 
 
-def _type_check(t: Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]], name: str):
-    # TODO: finish this
-    if isinstance(t, Dataset):
-        raise ValueError("")
-    elif isinstance(t, tuple):
-        if len(t) != 2:
-            raise ValueError("")
-        pass
+def _check_image_inputs(train: Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]],
+                        val: Optional[Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]]]):
+    if not isinstance(train, Dataset):
+        if len(train[0]) != len(train[1]):
+            raise ValueError(
+                f"expected train inputs to have the same length, but got lengths {len(train[0])} and {len(train[1])}")
+        if val is not None:
+            if len(val[0]) != len(val[1]):
+                raise ValueError(
+                    f"expected val inputs to have the same length, but got lengths {len(train[0])} and {len(train[1])}")
+
+
+def _create_image_dataset(data: Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]]) -> Dataset:
+    # if user already provided a dataset, use it
+    if isinstance(data, Dataset):
+        return data
+    # if user provided list of file paths, create a file path dataset
+    if isinstance(data[0], list):
+        return _FilePathDataset(file_paths=data[0], targets=data[1])
+    # if user provided the images as numpy tensors use them directly
     else:
-        raise TypeError(f"expected input `{name}` to be of type torch.utils.data.Dataset, {type(Dataset)}")
+        return TensorDataset(torch.tensor(data[0]), torch.tensor(data[1]))
 
 
-def _check_image_inputs(train: Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]],
-                        val: Optional[Union[Dataset, Tuple[Union[np.ndarray, List[str]], np.ndarray]]]):
-    # TODO: finish this
-    _type_check(train, "train")
-    if val is not None:
-        _type_check(val, "val")
+class _FilePathDataset(Dataset):
+    def __init__(self, file_paths: List[str], targets: np.ndarray):
+        self.file_paths = file_paths
+        self.targets = targets
+
+    def __getitem__(self, index: int):
+        img = Image.open(self.file_paths[index]).convert("RGB")
+        return img, torch.tensor(self.targets[index])
+
+    def __len__(self) -> int:
+        return len(self.file_paths)