Datasets:
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license: cc-by-nc-3.0
task_categories:
- object-detection
tags:
- synthetic-data
- sim-to-real
- domain-randomization
- industrial
- robotics
---
# SynthRender_Robotics: Synthetic Training Sets for the Robotics Sim-to-Real Benchmark
This repository hosts the synthetic training sets generated with **SynthRender** and used to benchmark sim-to-real transfer on the public **Robotics** dataset (Horváth et al., 2022), as reported in *"SynthRender and IRIS: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception"* ([arXiv:2602.21141](https://arxiv.org/abs/2602.21141)).
## Sample Images (720x720)
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|---|---|---|---|
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## Dataset Summary
SynthRender is an open-source, scriptable Domain Randomization (DR) engine built on BlenderProc, used in the paper to systematically ablate rendering design choices (physics-based placement, exponential light sampling, RGB lighting, material randomization) and quantify their effect on sim-to-real transfer. The synthetic sets in this repository correspond to the configurations used to train detectors evaluated against the **Robotics** benchmark (10 classes, 190 real test images, 920 annotated instances), on which the proposed framework reached **99.1% mAP@50**.
The repository contains two synthetic datasets, generated at different rendering resolutions:
| Resolution | Total Images | Train (80%) | Validation (20%) |
|------------|-------------|-------------|-------------------|
| 1024x1024 | 4,000 | 3,200 | 800 |
| 720x720 | 4,000 | 3,200 | 800 |
- **Annotation format:** YOLO-style bounding box labels (`class x_center y_center width height`, normalized)
- **Modality:** RGB images with text-based annotation files
- **Classes:** 10, matching the target Robotics benchmark
## Relation to the Paper
The paper's contribution statement notes that multiple synthetic training sets of 4,000 domain-randomized images each were generated to support the ablation studies. This repository provides two such sets, at 1024x1024 and 720x720 resolution, used for the Robotics benchmark specifically. It is a companion resource to the [IRIS dataset](https://huggingface.co/datasets/moiaraya/IRIS) and to the [SynthRender framework](https://github.com/Moiso/SynthRender) itself.
## Intended Use
For research on synthetic-to-real domain transfer, domain randomization ablations, and object detection benchmarking against the public Robotics dataset. Not intended for commercial use.
## License
Released under **CC BY-NC 3.0**. Commercial use is not permitted; attribution is required.
## Citation
```bibtex
@article{araya2026synthrender,
title={SynthRender and IRIS: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception},
author={Araya-Martinez, Jose Moises and Tom, Thushar and Sanchis Reig, Adri{\'a}n and Rey Valiente, Pablo and Lambrecht, Jens and Kr{\"u}ger, J{\"o}rg},
journal={arXiv preprint arXiv:2602.21141},
year={2026}
}
```
J. M. Araya-Martinez, T. Tom, A. S. Reig, P. R. Valiente, J. Lambrecht, and J. Krüger,
“Synthrender and iris: Open-source framework and dataset for bidirectional sim-real transfer in industrial object perception,”
2026. [Online]. Available: https://arxiv.org/abs/2602.21141
## Contact
For questions about the dataset, please open a discussion on this repository. |