SynthDet: An end-to-end object detection pipeline using synthetic data

Overview

SynthDet is an open source project that demonstrates an end-to-end object detection pipeline using synthetic image data. The project includes all the code and assets for generating a synthetic dataset in Unity. Using recent research, SynthDet utilizes Unity Perception package to generate highly randomized images of 64 common grocery products (example: cereal boxes and candy) and export them along with appropriate labels and annotations (2D bounding boxes). The synthetic dataset generated can then be used to train a deep learning based object detection model. This project is geared towards ML practitioners and enthusiasts who are actively exploring synthetic data or just looking to get started.

GTC 2020: Synthetic Data: An efficient mechanism to train Perception Systems

Contents

• SynthDet - Unity Perception sample project
• 3D Assets - High quality models of 64 commonly found grocery products
• Unity Perception package
• Unity Dataset Insights Python package

Release & Documentation

Getting started with SynthDet

Version	Release Date	Source
V0.1	May 26, 2020	source

Citation

SynthDet was inspired by the following research paper from Google Cloud AI:

Hinterstoisser, S., Pauly, O., Heibel, H., Marek, M., & Bokeloh, M. (2019). An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection.

Support

For general questions or concerns please contact the Perception team at perception@unity3d.com

For feedback, bugs, or other issues please file a github issue and the Perception team will investigate the issue as soon as possible.

License

• License