# IRIS Dataset: Industrial Real-Sim Imagery Set ## Overview The **IRIS Dataset** is a comprehensive real-world dataset designed to study sim-to-real transfer for object detection in industrial robotic environments. This repository provides: 1. **The complete real IRIS dataset**: 508 annotated images of 32 mechanical components captured across four distinct, challenging industrial scenes. 2. **Assets for synthetic data generation**: All necessary 3D models, backgrounds, and materials to run the companion synthetic data generation pipeline. 3. **Example synthetic datasets**: Two fully-annotated synthetic training sets (4000 images each) generated using our pipeline, showcasing different data generation strategies. 4. **Pre-trained model checkpoints**: YOLO11m models trained on the provided synthetic datasets, serving as baselines for sim-to-real transfer experiments. This release accompanies our paper and the open-source synthetic data generation code [SynthRender](https://anonymous.4open.science/r/SynthRender-main). The goal is to provide a complete, reproducible benchmark for evaluating and advancing sim-to-real methods in industrial robotics. ## Citation 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 ## Dataset Statistics **TOTAL DATA**: 508 images, 32 classes **Distribution by instance count**: - 96 single object images - 210 single instance images - 202 double instance images **Scene Breakdown**: | Scene Type | Count | Image Range | | -------------------------- | ----- | ----------- | | Controlled lighting (room) | 101 | 000–100 | | Window sunlight | 67 | 101–167 | | Background diversity | 100 | 168–267 | | Industrial robot scene | 240 | 268–507 |

## Folder Structure ``` IRIS ├── Assets │ ├── CADs │ │ ├── 3DGS │ │ ├── Manual │ │ ├── MeshyAI │ │ └── TRELLIS │ ├── General │ │ ├── backgrounds │ │ ├── distractors │ │ └── plane_materials │ └── 3D_GenAI_Masked_Imgs ├── Checkpoints ├── Real_Test_Set │ ├── annotations │ │ ├── coco │ │ │ ├── by_scene │ │ │ └── full │ │ └── yolo │ │ ├── by_scene │ │ │ ├── 01_control_lighting │ │ │ ├── 02_sunlight_window │ │ │ ├── 03_floor_backgrounds │ │ │ └── 04_robot_scene │ │ └── full │ └── images │ ├── by_scene │ │ ├── 01_control_lighting │ │ │ ├── depth │ │ │ └── rgb │ │ ├── 02_sunlight_window │ │ │ ├── depth │ │ │ └── rgb │ │ ├── 03_floor_backgrounds │ │ │ ├── depth │ │ │ └── rgb │ │ └── 04_robot_scene │ │ ├── depth │ │ └── rgb │ └── full │ ├── depth │ └── rgb └── Synthetic_Train_Sets ├── 4k_Material_Randomized │ ├── coco │ └── yolo │ ├── images │ │ ├── train │ │ └── val │ └── labels │ ├── train │ └── val └── 4K_Physics_Intrinsics_RGB_Exp ├── coco └── yolo ├── images │ ├── train │ └── val └── labels ├── train └── val ``` ## Description of Key Folders ### Assets Contains resources for synthetic data generation and running the pipeline - **CADs**: 3D models of all 32 parts generated via our four methods: Manual (expert moddeling), 3DGS (3D Gaussian Splattin), MeshyAI (texture generation), and TRELLIS (GenAI 3D asset). - **General**: Backgrounds, distractor objects, and plane materials for scene composition. - **3D_GenAI_Masked_Imgs**: Real object images with segmentation masks for GenAI tools.

Comparison between manually modeled synthetic assets (left) and real-world objects (right).

### Real_Test_Set Captured with a Zivid 2 Plus MR60 industrial RGB-D camera. - **annotations/**: COCO and YOLO bounding-box annotations. - **images/**: RGB images and depth data. The real test set is provided in two complementary formats: a **full evaluation set** (`images/full/` and `annotations/full/`) for comprehensive benchmarking across all 508 images, and **per-scene organization** (`images/by_scene/` and `annotations/by_scene/`) organized into 4 distinct industrial scenarios (controlled lighting, window sunlight, background diversity, and robot-mounted views). This dual structure allows researchers to either evaluate overall performance or conduct targeted analysis of specific environmental challenges. ### Synthetic_Train_Sets Images and bounding box annotations of our two best performing configuration synthetic datasets (4000 images each): - **4k_Material_Randomized**: Manual modelled CADs with material randomization - **4K_Physics_Intrinsics_RGB_Exp**: Manual modelled CADs and textures

Manual modelled CADs and textures (left) and randomized materials (right). ### Checkpoints Pre-trained YOLO11m models of our best 2 performing synthetic datasets: - `yolo11m_Material_Randomized.pt`: Trained on *4k_Material_Randomized* dataset - `yolo11m_Physics_Intrinsics_RGB_Exp.pt`: Trained on *4K_Physics_Intrinsics_RGB_Exp* dataset ## Object Classes

| Prefix | Meaning | |--------|----------| | **C** | Custom-Modeled | | **GF** | Global Fastener | | **MM** | McMaster | | **F** | Fath24 |

| Suffix | Meaning | |--------|----------| | **S** | Small | | **M** | Medium | | **L** | Large |

Family / Source	Object/Class Name(s)
Custom-Modeled	C_O_Ring_L, C_O_Ring_M, C_O_Ring_S
	C_Plastic_Washer_L, C_Plastic_Washer_S
	C_Steel_Ball_L, C_Steel_Ball_S
	C_Washer_M5
	C_Washer_M6
FATH GmbH	F_Roll-in_Nut_M5
Festo SE & Co. KG	FestoI
	FestoT
	Festo_Torch
	FestoV
	FestoX
	FestoY
GlobalFastener Inc.	GF_Collar_L, GF_Collar_S
	GF_Slotted_Pin_L, GF_Slotted_Pin_S
	GF_Split_Pin_L, GF_Split_Pin_S
	GF_Cone_Screw_M8
	GF_Hexagon_Nut
	GF_Knurled_Screw_M8
	GF_Plain_Screw_M8
	GF_Screw_M5
McMaster-Carr Supply Co.	MM_Silencer_L, MM_Silencer_S
	MM_Spring
	MM_Wing
	MM_Wood_Screw

## License See LICENSE.txt for terms and conditions. ## Contact For questions, please contact the corresponding authors of the paper.