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
Synthetic data: Simulating myriad possibilities to train robust machine learning models
Use Unity’s perception tools to generate and analyze synthetic data at scale to train your ML models
- SynthDet - Unity Perception sample project
- 3D Assets - High quality models of 64 commonly found grocery products
- Unity Perception package
- Unity Dataset Insights Python package
| Version | Release Date | Source |
|---|---|---|
| V0.1 | May 26, 2020 | source |
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.
For general questions or concerns please contact the Perception team at [email protected]
For feedback, bugs, or other issues please file a github issue and the Perception team will investigate the issue as soon as possible.