diff --git a/ann_benchmarks/distance.py b/ann_benchmarks/distance.py
index 9d4b483ee..d30d08207 100644
--- a/ann_benchmarks/distance.py
+++ b/ann_benchmarks/distance.py
@@ -115,7 +115,7 @@ def dataset_transform(dataset: h5py.Dataset) -> Tuple[Union[np.ndarray, List[np.
         Tuple[Union[np.ndarray, List[np.ndarray]], Union[np.ndarray, List[np.ndarray]]]: Tuple of training and testing data in conventional format.
     """
     if dataset.attrs.get("type", "dense") != "sparse":
-        return np.array(dataset["train"]), np.array(dataset["test"])
+        return np.asarray(dataset["train"]), np.asarray(dataset["test"])
 
     # we store the dataset as a list of integers, accompanied by a list of lengths in hdf5
     # so we transform it back to the format expected by the algorithms here (array of array of ints)
diff --git a/ann_benchmarks/runner.py b/ann_benchmarks/runner.py
index 1f31e41eb..3bacb3208 100644
--- a/ann_benchmarks/runner.py
+++ b/ann_benchmarks/runner.py
@@ -152,8 +152,8 @@ def load_and_transform_dataset(dataset_name: str) -> Tuple[
         Tuple: Transformed datasets.
     """
     D, dimension = get_dataset(dataset_name)
-    X_train = numpy.array(D["train"])
-    X_test = numpy.array(D["test"])
+    X_train = numpy.asarray(D["train"])
+    X_test = numpy.asarray(D["test"])
     distance = D.attrs["distance"]
 
     print(f"Got a train set of size ({X_train.shape[0]} * {dimension})")