diff --git a/README.md b/README.md
index 02a8cdd34..696cf258b 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ Hello AI World can be run completely onboard your Jetson, including inferencing
#### Inference
-* [Classification](docs/imagenet-console-2.md)
+* [Image Classification](docs/imagenet-console-2.md)
* [Using the ImageNet Program on Jetson](docs/imagenet-console-2.md)
* [Coding Your Own Image Recognition Program (Python)](docs/imagenet-example-python-2.md)
* [Coding Your Own Image Recognition Program (C++)](docs/imagenet-example-2.md)
diff --git a/docs/imagenet-camera-2.md b/docs/imagenet-camera-2.md
index e8eca2cbb..79623da2c 100644
--- a/docs/imagenet-camera-2.md
+++ b/docs/imagenet-camera-2.md
@@ -1,7 +1,7 @@
Back | Next | Contents
-Image Recognition
+Image Classification
# Running the Live Camera Recognition Demo
diff --git a/docs/imagenet-console-2.md b/docs/imagenet-console-2.md
index 7364865c5..0f56d3d22 100644
--- a/docs/imagenet-console-2.md
+++ b/docs/imagenet-console-2.md
@@ -1,7 +1,7 @@
Back | Next | Contents
-Image Recognition
+Image Classification
# Classifying Images with ImageNet
There are multiple types of deep learning networks available, including recognition, detection/localization, and semantic segmentation. The first deep learning capability we're highlighting in this tutorial is **image recognition**, using classifcation networks that have been trained on large datasets to identify scenes and objects.
diff --git a/docs/imagenet-example-2.md b/docs/imagenet-example-2.md
index 01c02a74e..e545307b8 100644
--- a/docs/imagenet-example-2.md
+++ b/docs/imagenet-example-2.md
@@ -1,7 +1,7 @@
Back | Next | Contents
-Image Recognition
+Image Classification
# Coding Your Own Image Recognition Program (C++)
In the previous step, we ran an application that came with the jetson-inference repo.
diff --git a/docs/imagenet-example-python-2.md b/docs/imagenet-example-python-2.md
index 18ff66a3b..1336ddf85 100644
--- a/docs/imagenet-example-python-2.md
+++ b/docs/imagenet-example-python-2.md
@@ -1,7 +1,7 @@
Back | Next | Contents
-Image Recognition
+Image Classification
# Coding Your Own Image Recognition Program (Python)
In the previous step, we ran a sample application that came with the `jetson-inference` repo.
diff --git a/docs/imagenet-tagging.md b/docs/imagenet-tagging.md
new file mode 100644
index 000000000..47a0ace6b
--- /dev/null
+++ b/docs/imagenet-tagging.md
@@ -0,0 +1,27 @@
+
+Back | Next | Contents
+
+Image Classification
+
+# Multi-Label Classification for Image Tagging
+
+Multi-label classification models are able to recognize multiple object classes simultaneously for performing tasks like image tagging. The multi-label DNNs are almost identical in topology to ordinary single-class models, except they use a sigmoid activation layer as opposed to softmax. There's a pre-trained `resnet18-tagging-voc` multi-label model available that was trained on the Pascal VOC dataset:
+
+
+
+To enable image tagging, you'll want to run imagenet/imagenet.py with `--topK=0` and a `--threshold` of your choosing:
+
+``` bash
+# C++
+$ imagenet --model=resnet18-tagging-voc --topK=0 --threshold=0.25 "images/object_*.jpg" images/test/tagging_%i.jpg"
+
+# Python
+$ imagenet.py --model=resnet18-tagging-voc --topK=0 --threshold=0.25 "images/object_*.jpg" images/test/tagging_%i.jpg"
+```
+
+Using `--topK=0` means that all the classes with a confidence score exceeding the threshold will be returned.
+
+Next | Object Detection
+
+Back | Running the Live Camera Recognition Demo
+© 2016-2023 NVIDIA | Table of Contents