updated docs

README.md

@@ -40,6 +40,7 @@ Hello AI World can be run completely onboard your Jetson, including inferencing
 	* [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)
 	* [Running the Live Camera Recognition Demo](docs/imagenet-camera-2.md)
+	* [Multi-Label Classification for Image Tagging](docs/imagenet-tagging.md)
 * [Object Detection](docs/detectnet-console-2.md)
 	* [Detecting Objects from Images](docs/detectnet-console-2.md#detecting-objects-from-the-command-line)
 	* [Running the Live Camera Detection Demo](docs/detectnet-camera-2.md)

docs/imagenet-camera-2.md

@@ -1,5 +1,5 @@
 <img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
-<p align="right"><sup><a href="imagenet-example-2.md">Back</a> | <a href="detectnet-console-2.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
+<p align="right"><sup><a href="imagenet-example-2.md">Back</a> | <a href="imagenet-tagging.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
 <br/>
 <sup>Image Classification</sup></p>  
 
@@ -47,7 +47,7 @@ The application can recognize up to 1000 different types of objects, since the c
 This concludes this section of the Hello AI World tutorial on image classification.  Next, we're going to start using Object Detection networks, which provide us with the bounding box coordinates of multiple objects per frame.
 
 ##
-<p align="right">Next | <b><a href="detectnet-console-2.md">Locating Object Coordinates with DetectNet</a></b>
+<p align="right">Next | <b><a href="imagenet-tagging.md">Multi-Label Classification for Image Tagging</a></b>
 <br/>
 Back | <b><a href="imagenet-example-2.md">Coding Your Own Image Recognition Program</a></p>
 </b><p align="center"><sup>© 2016-2019 NVIDIA | </sup><a href="../README.md#hello-ai-world"><sup>Table of Contents</sup></a></p>

docs/imagenet-tagging.md

@@ -19,9 +19,9 @@ $ imagenet --model=resnet18-tagging-voc --topK=0 --threshold=0.25 "images/object
 $ 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.
+Using `--topK=0` means that all the classes with a confidence score exceeding the threshold will be returned by the classifier.
 
-<p align="right">Next | <b><a href="detectnet-console-2.md">Object Detection</a></b>
+<p align="right">Next | <b><a href="detectnet-console-2.md">Detecting Objects from Images</a></b>
 <br/>
 Back | <b><a href="imagenet-camera-2.md">Running the Live Camera Recognition Demo</a></p>
 </b><p align="center"><sup>© 2016-2023 NVIDIA | </sup><a href="../README.md#hello-ai-world"><sup>Table of Contents</sup></a></p>