diff --git a/test/test_transforms.py b/test/test_transforms.py
index a65d848ec92..7346e2c5094 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -298,6 +298,11 @@ def test_random_crop(self):
         self.assertEqual(result.size(1), height + 1)
         self.assertEqual(result.size(2), width + 1)
 
+        t = transforms.RandomCrop(48)
+        img = torch.ones(3, 32, 32)
+        with self.assertRaisesRegex(ValueError, r"Required crop size .+ is larger then input image size .+"):
+            t(img)
+
     def test_pad(self):
         height = random.randint(10, 32) * 2
         width = random.randint(10, 32) * 2
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index 07043b65a56..6a7c6515790 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -532,6 +532,12 @@ def get_params(img: Tensor, output_size: Tuple[int, int]) -> Tuple[int, int, int
         """
         w, h = F._get_image_size(img)
         th, tw = output_size
+
+        if h + 1 < th or w + 1 < tw:
+            raise ValueError(
+                "Required crop size {} is larger then input image size {}".format((th, tw), (h, w))
+            )
+
         if w == tw and h == th:
             return 0, 0, h, w