diff --git a/gallery/v2_transforms/plot_transforms_v2.py b/gallery/v2_transforms/plot_transforms_v2.py
index 3058df23444..0f97431da59 100644
--- a/gallery/v2_transforms/plot_transforms_v2.py
+++ b/gallery/v2_transforms/plot_transforms_v2.py
@@ -99,10 +99,9 @@
     format="XYXY", canvas_size=img.shape[-2:])
 
 transforms = v2.Compose([
-    v2.RandomPhotometricDistort(),
-    v2.RandomIoUCrop(),
-    v2.RandomHorizontalFlip(p=0.5),
-    v2.SanitizeBoundingBoxes(),
+    v2.RandomResizedCrop(size=(224, 224), antialias=True),
+    v2.RandomPhotometricDistort(p=1),
+    v2.RandomHorizontalFlip(p=1),
 ])
 out_img, out_bboxes = transforms(img, bboxes)
 
diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index 325d864dc05..982c86d0426 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -1256,6 +1256,20 @@ def test_sanitize_bounding_boxes(min_size, labels_getter, sample_type):
         assert out_labels.tolist() == valid_indices
 
 
+def test_sanitize_bounding_boxes_no_label():
+    # Non-regression test for https://github.com/pytorch/vision/issues/7878
+
+    img = make_image()
+    boxes = make_bounding_boxes()
+
+    with pytest.raises(ValueError, match="or a two-tuple whose second item is a dict"):
+        transforms.SanitizeBoundingBoxes()(img, boxes)
+
+    out_img, out_boxes = transforms.SanitizeBoundingBoxes(labels_getter=None)(img, boxes)
+    assert isinstance(out_img, datapoints.Image)
+    assert isinstance(out_boxes, datapoints.BoundingBoxes)
+
+
 def test_sanitize_bounding_boxes_errors():
 
     good_bbox = datapoints.BoundingBoxes(
diff --git a/torchvision/transforms/v2/_augment.py b/torchvision/transforms/v2/_augment.py
index 130950fee34..a9bad8f9bf7 100644
--- a/torchvision/transforms/v2/_augment.py
+++ b/torchvision/transforms/v2/_augment.py
@@ -229,7 +229,7 @@ class MixUp(_BaseMixUpCutMix):
         alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
         num_classes (int): number of classes in the batch. Used for one-hot-encoding.
         labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
-            By default, this will pick the second parameter a the labels if it's a tensor. This covers the most
+            By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
             common scenario where this transform is called as ``MixUp()(imgs_batch, labels_batch)``.
             It can also be a callable that takes the same input as the transform, and returns the labels.
     """
@@ -279,7 +279,7 @@ class CutMix(_BaseMixUpCutMix):
         alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
         num_classes (int): number of classes in the batch. Used for one-hot-encoding.
         labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
-            By default, this will pick the second parameter a the labels if it's a tensor. This covers the most
+            By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
             common scenario where this transform is called as ``CutMix()(imgs_batch, labels_batch)``.
             It can also be a callable that takes the same input as the transform, and returns the labels.
     """
diff --git a/torchvision/transforms/v2/_utils.py b/torchvision/transforms/v2/_utils.py
index 3c6977fae91..6b327d45c0e 100644
--- a/torchvision/transforms/v2/_utils.py
+++ b/torchvision/transforms/v2/_utils.py
@@ -112,7 +112,7 @@ def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor:
         inputs = inputs[1]
 
     # MixUp, CutMix
-    if isinstance(inputs, torch.Tensor):
+    if is_pure_tensor(inputs):
         return inputs
 
     if not isinstance(inputs, collections.abc.Mapping):