Skip to content

Latest commit

 

History

History
50 lines (31 loc) · 2.67 KB

quick_demo_train.md

File metadata and controls

50 lines (31 loc) · 2.67 KB
Raw

Data Collection: Quick Demo

If you just want to run the completed project in order to collect your training and validation data this section can help do it.

To learn how to build something like this from scratch, see Part 1 and Part 2 of our tutorial.

Table of Contents

Prerequisites

To follow this tutorial you need to clone this repository even if you want to create your Unity project from scratch.

  1. Open a terminal and navigate to the folder where you want to host the repository.
git clone --recurse-submodules https://github.com/Unity-Technologies/Robotics-Object-Pose-Estimation.git

  1. Install Unity 2020.2.*.

  2. Open the completed project. To do so, open Unity Hub, click the Add button, and select PoseEstimationDemoProject from the root Robotics-Object-Pose-Estimation folder.

Setup

  1. Once the project is opened, in the Project tab, go to Assets > Scenes and double click on TutorialPoseEstimation to open the Scene created for this tutorial.

  2. We now need to set the size of the images used. In the Game view, click on the dropdown menu in front of Display 1. Then, click + to create a new preset. Make sure Type is set to Fixed Resolution. Set Width to 650 and Height to 400. The gif below depicts these actions.

Switching to Data Collection Mode

The completed project is set up for inference mode by default, so we must switch it to data collection mode.

  1. Uncheck the ROSObjects GameObject in the Hierarchy tab to disable it.

  2. On the Simulation Scenario GameObject, make sure that Pose Estimation Scenario is enabled, and that its Automatic Iteration property is checked.

  3. On the Main Camera GameObject, check the Perception Camera (Script) component to enable it.

Data Collection

To get started with the data collection, follow the instructions in Part 3: Collect the Training and Validation Data of the tutorial. This section will explain how to set the a random seed for the environment, choose how many training data examples you'd like to collect, and get things running.

If you'd like to move on to training a pose estimation model on the data you've collected, navigate to Part 3: Train the Deep Learning Model.

Have fun!