Update README.md

README.md

@@ -1,3 +1,67 @@
 ---
 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)
+
+| | | | |
+|---|---|---|---|
+| ![](720x720/images/train/10.png) | ![](720x720/images/train/1310.png) | ![](720x720/images/train/2000.png) | ![](720x720/images/train/3000.png) |
+
+## 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.