Directory name

Documentation/0_ros_setup.md

@@ -2,7 +2,7 @@
 
 This page provides steps on how to manually set up a catkin workspace for the Pose Estimation tutorial.
 
-1. Navigate to the `object_pose_estimation/` directory of this downloaded repository. This directory will be used as the ROS catkin workspace.
+1. Navigate to the root `Object-Pose-Estimation/` directory of this downloaded repository. This directory will be used as the ROS catkin workspace.
 
 2. Copy or download this directory to your ROS operating system if you are doing ROS operations in another machine, VM, or container.
 

Documentation/3_data_collection_model_training.md

@@ -60,7 +60,7 @@ Now its time to train our deep learning model! We've provided the model training
 
 This step can take a long time if your computer doesn't have GPU support (~5 days on CPU). Even with a GPU, it can take around ~10 hours. We have provided an already trained model as an alternative to waiting for training to complete. If you would like to use this provided model, you can proceed to [Part 4](4_pick_and_place.md).
 
-1. Navigate to the `tutorials/object_pose_estimation/Model` directory.
+1. Navigate to the `Object-Pose-Estimation/Model` directory.
 
 ### Requirements
 
@@ -110,7 +110,7 @@ log_dir_system: /Users/jonathan.leban/Documents/models
 ### Training the model
 Now its time to train our deep learning model!
 
-6. If you are not already in the `tutorials/object_pose_estimation/Model` directory, navigate there. 
+6. If you are not already in the `Object-Pose-Estimation/Model` directory, navigate there. 
 
 7. Enter the following command to start training: 
 ```bash 
@@ -131,7 +131,7 @@ However, first we need to specify a few settings in our config file.
 
 8. In [config.yaml](../Model/config.yaml), under `checkpoint`, you need to set the argument `log_dir_checkpoint` to the path where you have saved your newly trained model.
 
-9. If you are not already in the `tutorials/object_pose_estimation/Model` directory, navigate there.
+9. If you are not already in the `Object-Pose-Estimation/Model` directory, navigate there.
 
 10. To start the evaluation run, enter the following command: 
 ```bash 

Documentation/4_pick_and_place.md

@@ -63,7 +63,7 @@ Building this Docker container will install the necessary packages for this tuto
 <img src="Images/4_docker_daemon.png" height=500/>
 </p>
 
-2. In the terminal, ensure the current location is at the root of the `object_pose_estimation` directory. Build the provided ROS Docker image as follows:
+2. In the terminal, ensure the current location is at the root of the `Object-Pose-Estimation` directory. Build the provided ROS Docker image as follows:
 
 ```bash
 docker build -t unity-robotics:pose-estimation -f docker/Dockerfile .
@@ -90,7 +90,7 @@ source devel/setup.bash
 
 The ROS workspace is now ready to accept commands!
 
->Note: The Docker-related files (Dockerfile, bash scripts for setup) are located in `PATH-TO-object_pose_estimation/docker`. 
+>Note: The Docker-related files (Dockerfile, bash scripts for setup) are located in `Object-Pose-Estimation/docker`. 
 
 ---
 

Documentation/quick_demo_full.md

@@ -26,7 +26,7 @@ git clone --recurse-submodules https://github.com/Unity-Technologies/Object-Pose
 
 ## <a name='setup'>Setup</a>
 
-1. Open the completed project. In the Unity Hub, click the `Add` button, and select `tutorials/object_pose_estimation/PoseEstimationDemoProject` from inside the file location where you cloned this repo. 
+1. Open the completed project. In the Unity Hub, click the `Add` button, and select `Object-Pose-Estimation/PoseEstimationDemoProject` from inside the file location where you cloned this repo. 
 
 2. Open the scene. Go to `Assets > Scenes` and double click on `TutorialPoseEstimation`. 
 
@@ -38,7 +38,7 @@ git clone --recurse-submodules https://github.com/Unity-Technologies/Object-Pose
 
 ## Add the Pose Estimation Model
 
-In your `object_pose_estimation` folder, you should have a `ROS` folder. Inside that folder you should have a `src` folder and inside that one 5 folders: `moveit_msgs`, `robotiq`, `ros_tcp_endpoint`, `universal_robot` and `ur3_moveit`. 
+In your root `Object-Pose-Estimation` folder, you should have a `ROS` folder. Inside that folder you should have a `src` folder and inside that one 5 folders: `moveit_msgs`, `robotiq`, `ros_tcp_endpoint`, `universal_robot` and `ur3_moveit`. 
 
 1. Download the [pose estimation model](https://github.com/Unity-Technologies/Unity-Robotics-Hub/releases/download/Pose-Estimation/UR3_single_cube_model.tar) we have trained.
 
@@ -57,7 +57,7 @@ We have provided a Docker container to get you up and running quickly.
 <img src="Images/4_docker_daemon.png" height=400/>
 </p>
 
-2. In the terminal, ensure the current location is at the root of the `object_pose_estimation` directory. Build the provided ROS Docker image as follows:
+2. In the terminal, ensure the current location is at the root of the `Object-Pose-Estimation` directory. Build the provided ROS Docker image as follows:
 
 ```bash
 docker build -t unity-robotics:pose-estimation -f docker/Dockerfile .

Documentation/quick_demo_train.md

@@ -25,7 +25,7 @@ git clone https://github.com/Unity-Technologies/Unity-Robotics-Hub.git
 
 2. [Install Unity `2020.2.*`.](install_unity.md)
 
-3. Open the completed project. To do so, open Unity Hub, click the `Add` button, and select `PoseEstimationDemoProject` from the `Unity-Robotics-Hub/tutorials/object_pose_estimation/` folder. 
+3. Open the completed project. To do so, open Unity Hub, click the `Add` button, and select `PoseEstimationDemoProject` from the root `Object-Pose-Estimation` folder. 
 
 ## <a name='setup'>Setup</a>
 

Model/documentation/running_on_docker.md

@@ -21,7 +21,7 @@ Before creating the Docker image, you need to be sure your Docker settings are c
 
 The first step is to build the Docker image.
 
-* **Action**: Open a new terminal and navigate to the `Unity-Robotics-Hub/tutorials/object_pose_estimation/Model` folder. Then run the command to build your docker image, and name it `pose_estimation`:
+* **Action**: Open a new terminal and navigate to the `Object-Pose-Estimation/Model` folder. Then run the command to build your docker image, and name it `pose_estimation`:
 ```bash 
 docker build -t pose_estimation .
 ```