updated docs

docs/aux-docker.md

@@ -5,7 +5,7 @@
 
 # Running the Docker Container
 
-Pre-built Docker container images for this project are hosted on [DockerHub](https://hub.docker.com/r/dustynv/jetson-inference/tags).  Alternatively, you can [Build the Project from Source](building-repo-2.md).   
+Pre-built Docker container images for this project are hosted on [DockerHub](https://hub.docker.com/r/dustynv/jetson-inference/tags).  Alternatively, you can [Build the Project ](building-repo-2.md) from source.   
 
 Below are the currently available container tags:
 
@@ -31,9 +31,9 @@ These containers use the [`l4t-pytorch`](https://ngc.nvidia.com/catalog/containe
 Due to various mounts and devices needed to run the container, it's recommended to use the [`docker/run.sh`](../docker/run.sh) script to run the container:
 
 ```bash
-git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference
-cd jetson-inference
-docker/run.sh
+$ git clone --recursive --depth=1 https://github.com/dusty-nv/jetson-inference
+$ cd jetson-inference
+$ docker/run.sh
 ```
 
 > **note:**  because of the Docker scripts used and the data directory structure that gets mounted into the container, you should still clone the project on your host device (i.e. even if not intending to build/install the project natively)
@@ -42,13 +42,13 @@ docker/run.sh
 
 ### ROS Support
 
-This project has the [ros_deep_learning](https://github.com/dusty-nv/ros_deep_learning) package available for ROS/ROS2, and by specifying the `--ros=ROS_DISTRO` option you can start the version of container built with ROS.  Supported ROS distros include Noetic, Foxy, Galactic, Humble, and Iron:
+This project also has the [ros_deep_learning](https://github.com/dusty-nv/ros_deep_learning) package available for ROS/ROS2, and by specifying the `--ros=ROS_DISTRO` option you can start the version of container built with ROS.  Supported ROS distros include Noetic, Foxy, Galactic, Humble, and Iron:
 
 ``` bash
-docker/run.sh --ros=humble   # noetic, foxy, galactic, humble, iron
+$ docker/run.sh --ros=humble   # noetic, foxy, galactic, humble, iron
 ```
 
-The container will automatically source the ROS environment and ros_deep_learning package when started.  For more information, see the [ros_deep_learning](https://github.com/dusty-nv/ros_deep_learning) documentation.
+The container will source the ROS environment and packages when started.  For more information, see the [ros_deep_learning](https://github.com/dusty-nv/ros_deep_learning) documentation.
 
 ### x86 Support
 
@@ -83,10 +83,10 @@ You can specify `--volume` multiple times to mount multiple directories.  For mo
 Once the container is up and running, you can then run example programs from the tutorial like normal inside the container:
 
 ```bash
-# cd build/aarch64/bin
-# ./video-viewer /dev/video0
-# ./imagenet images/jellyfish.jpg images/test/jellyfish.jpg
-# ./detectnet images/peds_0.jpg images/test/peds_0.jpg
+$ cd build/aarch64/bin
+$ ./video-viewer /dev/video0
+$ ./imagenet images/jellyfish.jpg images/test/jellyfish.jpg
+$ ./detectnet images/peds_0.jpg images/test/peds_0.jpg
 # (press Ctrl+D to exit the container)
 ```
 

docs/building-repo-2.md

@@ -73,7 +73,7 @@ $ cmake ../
 > **note**: this command will launch the [`CMakePreBuild.sh`](../CMakePreBuild.sh) script which asks for sudo privileges while installing some prerequisite packages on the Jetson. The script also downloads pre-trained networks from web services.
 
 
-### Installing PyTorch
+## Installing PyTorch
 
 If you are using JetPack 4.2 or newer, another tool will now run that can optionally install PyTorch on your Jetson if you want to re-train networks with [transfer learning](pytorch-transfer-learning.md) later in the tutorial.  This step is optional, and if you don't wish to do the transfer learning steps, you don't need to install PyTorch and can skip this step.
 
@@ -93,7 +93,7 @@ $ ./install-pytorch.sh
 
 Running these commands will prompt you with the same dialog as seen above.
 
-### Compiling the Project
+## Compiling the Project
 
 Make sure you are still in the `jetson-inference/build` directory, created above in step #3.
 
@@ -123,7 +123,7 @@ In the build tree, you can find the binaries residing in `build/aarch64/bin/`, h
 The Python bindings for the [`jetson.inference`](https://rawgit.com/dusty-nv/jetson-inference/master/docs/html/python/jetson.inference.html) and [`jetson.utils`](https://rawgit.com/dusty-nv/jetson-inference/master/docs/html/python/jetson.utils.html) modules also get installed during the `sudo make install` step under `/usr/lib/python*/dist-packages/`.  If you update the code, remember to run it again.
 
 
-### Digging Into the Code
+## Digging Into the Code
 
 See the **[API Reference](../README.md#api-reference)** documentation for the vision primitives available in `libjetson-inference`, including `imageNet` for image recognition, `detectNet` for object localization, and `segNet` for semantic segmentation.  Familiarize yourself with the C++ or Python versions of these objects, depending on which language you prefer to use.