# An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection

arXiv: https://arxiv.org/abs/1902.09967 · HTML: https://mlsanity.com/paper/1902.09967/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [Unity Perception: Generate Synthetic Data for Computer Vision](https://mlsanity.com/paper/2107.04259/) (2021)
- [NViSII: A Scriptable Tool for Photorealistic Image Generation](https://mlsanity.com/paper/2105.13962/) (2021)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?](https://mlsanity.com/paper/2108.11018/) (2021)
- [Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data](https://mlsanity.com/paper/2203.00734/) (2022)
- [Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology](https://mlsanity.com/paper/2405.19822/) (2024)
- [Synthetic imagery for fuzzy object detection: A comparative study](https://mlsanity.com/paper/2410.01124/) (2024)
- [Technology Readiness Levels for Machine Learning Systems](https://mlsanity.com/paper/2101.03989/) (2021)
- [Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings](https://mlsanity.com/paper/2306.13929/) (2023)
- [Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection](https://mlsanity.com/paper/2307.06688/) (2023)
- [Towards Packaging Unit Detection for Automated Palletizing Tasks](https://mlsanity.com/paper/2308.06306/) (2023)

## Abstract (arXiv)

Deep learning methods typically require vast amounts of training data to reach their full potential. While some publicly available datasets exists, domain specific data always needs to be collected and manually labeled, an expensive, time consuming and error prone process. Training with synthetic data is therefore very lucrative, as dataset creation and labeling comes for free. We propose a novel method for creating purely synthetic training data for object detection. We leverage a large dataset of 3D background models and densely render them using full domain randomization. This yields background images with realistic shapes and texture on top of which we render the objects of interest. During training, the data generation process follows a curriculum strategy guaranteeing that all foreground models are presented to the network equally under all possible poses and conditions with increasing complexity. As a result, we entirely control the underlying statistics and we create optimal training samples at every stage of training. Using a set of 64 retail objects, we demonstrate that our simple approach enables the training of detectors that outperform models trained with real data on a challenging evaluation dataset.