diff --git a/intermediate_source/torchvision_tutorial.rst b/intermediate_source/torchvision_tutorial.rst
index 703deeab11c..85da9a45639 100644
--- a/intermediate_source/torchvision_tutorial.rst
+++ b/intermediate_source/torchvision_tutorial.rst
@@ -61,7 +61,7 @@ training and evaluation, and will use the evaluation scripts from
 
   ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI``
 
-One note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class, you should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can define ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has booth classes, your ``labels`` tensor should look like ``[1,2]``.
+One note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class, you should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can define ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has both classes, your ``labels`` tensor should look like ``[1,2]``.
 
 Additionally, if you want to use aspect ratio grouping during training
 (so that each batch only contains images with similar aspect ratio),