Model

Object Pose Estimation Model

This section contains code for training and evaluating a deep neural network to predict the pose of a single object from RGB images. We provide support for running both locally and with Docker.

This model is a modified implementation of Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World, by Tobin et. al. It is based on the classic VGG-16 backbone architecture, and initialized with weights pre-trained on the ImageNet dataset. The head of the network is replaced with a 3D position prediction head that outputs (x, y, z), and an orientation predicton head that outputs a quaternion (q_x, q_y, q_z, q_w).

Quick Start (Recommended)

We trained this model on sythetic data collected in Unity. To learn how to collect this data and train the model yourself, see our data collection and training tutorial.

Pre-Trained Model

We've provided a pre-trained model, which can be downloaded here.

Setup

• For running on docker
• For running in the cloud
• For running locally with Conda

CLI

This model supports a train and an evaluate command. Both of these have many arguments, which you can examine in cli.py. They will default to the values in config.yaml for convenience, but can be overridden via the command line.

The most important train arguments to be aware of are:

• --data_root: Path to the directory containing your data folders. These directory should include UR3_single_cube_training and UR3_single_cube_validation, containing the training and validation data, respectively.
• --log-dir-system: Path to directory where you'd like to save Tensorboard log files and model checkpoint files.

The most important evaluate arguments to be aware of are:

• --load-dir-checkpoint: Path to model to be evaluated.
• --data_root: Path to the directory containing your data folders. These directory should include UR3_single_cube_training and UR3_single_cube_validation, containing the training and validation data, respectively.

Performance

Below is a description of the model's performance on predicting the pose of a cube. For the loss, we used the L2 norm for the position and orientation in each batch.

However, we used different metrics to evaluate the performance of the model.

• To evaluate translation predictions, we measured the average L2 norm over the dataset.
• To evaluate the orientation predictions, we used the angle between the orientation of the prediction and the orientation of the target, averaged over the dataset (implementation here).

	Training Error	Validation Error
Translation	0.012 (12% of cube's size)	0.01 (10% of cube's size)
Orientation (radian)	0.06	0.05

Note: Data for the above experiment was collected in Unity 2020.2.1f1.

Unit Testing

We use pytest to run tests located under tests/. You can run the tests after following the setup instructions in Running on Local with Conda commands.

You can run the entire test suite with:

python -m pytest

or run individual test files with:

python -m pytest tests/test_average_translation_mean_square_error.py

Resources

• Documentation describing structure of the model code
• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
• Pose Estimation Tutorial