diff --git a/test/test_transforms.py b/test/test_transforms.py
index e2232e2491b..21305f5fa8f 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -404,6 +404,12 @@ def test_to_tensor(self):
             expected_output = ndarray.transpose((2, 0, 1))
             assert np.allclose(output.numpy(), expected_output)
 
+        # separate test for mode '1' PIL images
+        input_data = torch.ByteTensor(1, height, width).bernoulli_()
+        img = transforms.ToPILImage()(input_data.mul(255)).convert('1')
+        output = trans(img)
+        assert np.allclose(input_data.numpy(), output.numpy())
+
     @unittest.skipIf(accimage is None, 'accimage not available')
     def test_accimage_to_tensor(self):
         trans = transforms.ToTensor()
diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index b9b7730df94..0b06e059b6c 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -66,9 +66,11 @@ def to_tensor(pic):
         img = torch.from_numpy(np.array(pic, np.int16, copy=False))
     elif pic.mode == 'F':
         img = torch.from_numpy(np.array(pic, np.float32, copy=False))
+    elif pic.mode == '1':
+        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
     else:
         img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
-    # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
+    # PIL image mode: L, P, I, F, RGB, YCbCr, RGBA, CMYK
     if pic.mode == 'YCbCr':
         nchannel = 3
     elif pic.mode == 'I;16':