diff --git a/.github/stale.yml b/.github/stale.yml
new file mode 100644
index 0000000..3b0f14a
--- /dev/null
+++ b/.github/stale.yml
@@ -0,0 +1,17 @@
+# Number of days of inactivity before an issue becomes stale
+daysUntilStale: 60
+# Number of days of inactivity before a stale issue is closed
+daysUntilClose: 7
+# Issues with these labels will never be considered stale
+exemptLabels:
+  - pinned
+  - security
+# Label to use when marking an issue as stale
+staleLabel: stale
+# Comment to post when marking an issue as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Thank you
+  for your contributions.
+# Comment to post when closing a stale issue. Set to `false` to disable
+closeComment: false
\ No newline at end of file
diff --git a/docs/source/install.rst b/docs/source/install.rst
index 066d30d..0ffa4de 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -382,7 +382,7 @@ Installation of the Object Detection API is achieved by installing the ``object_
 
     # From within TensorFlow/models/research/
     cp object_detection/packages/tf2/setup.py .
-    python -m pip install --use-feature=2020-resolver .
+    python -m pip install .
 
 .. note::
 
diff --git a/docs/source/training.rst b/docs/source/training.rst
index 79fd0ba..3e59768 100644
--- a/docs/source/training.rst
+++ b/docs/source/training.rst
@@ -299,7 +299,7 @@ Create Label Map
 
 TensorFlow requires a label map, which namely maps each of the used labels to an integer values. This label map is used both by the training and detection processes.
 
-Below we show an example label map (e.g ``label_map.pbtxt``), assuming that our dataset containes 2 labels, ``dogs`` and ``cats``:
+Below we show an example label map (e.g ``label_map.pbtxt``), assuming that our dataset contains 2 labels, ``dogs`` and ``cats``:
 
 .. code-block:: default
 
@@ -637,7 +637,7 @@ will be later used to perform the object detection. This can be done as follows:
 
 .. code-block:: default
     
-    python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_efficientdet_d1\pipeline.config --trained_checkpoint_dir .\models\my_efficientdet_d1\ --output_directory .\exported-models\my_model
+    python .\exporter_main_v2.py --input_type image_tensor --pipeline_config_path .\models\my_ssd_resnet50_v1_fpn\pipeline.config --trained_checkpoint_dir .\models\my_ssd_resnet50_v1_fpn\ --output_directory .\exported-models\my_model
 
 After the above process has completed, you should find a new folder ``my_model`` under the
 ``training_demo/exported-models``, that has the following structure: