README.md

# 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 |

<table>
  <tr>
    <td><img src="./Readme_Media/Real_Examples/viz_00086.png" width="370"></td>
    <td><img src="./Readme_Media/Real_Examples/viz_00154.png" width="370"></td>
    <td><img src="./Readme_Media/Real_Examples/viz_00198.png" width="370"></td>
  </tr>
  <tr>
    <td><img src="./Readme_Media/Real_Examples/viz_00220.png" width="370"></td>
    <td><img src="./Readme_Media/Real_Examples/viz_00254.png" width="370"></td>
    <td><img src="./Readme_Media/Real_Examples/viz_00402.png" width="370"></td>
  </tr>
</table>

<br>


## 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.

<table>
  <tr>
    <td><img src="./Readme_Media/IRIS_Manual.png" width="660"></td>
    <td>
      <div style="height:360px; overflow:hidden;">
        <img src="./Readme_Media/IRIS_Real.jpeg" width="540">
      </div>
    </td>
  </tr>
</table>
<em>Comparison between manually modeled synthetic assets (left) and real-world objects (right).</em>

<br><br>


### 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

<table>
  <tr>
    <td><img src="./Readme_Media/Synthetic_Examples/viz_0.png" width="600"></td>
    <td><img src="./Readme_Media/Synthetic_Examples/viz_245.png" width="600"></td>
  </tr>
</table>
<em>Manual modelled CADs and textures (left) and randomized materials (right).</em>

</div>

### 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
<table>
<tr>
<td>

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

</td>
<td>


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

</td>
</tr>
</table>


<table>
<thead>
<tr>
  <th>Family / Source</th>
  <th>Object/Class Name(s)</th>
</tr>
</thead>

<tbody>

<tr>
  <td rowspan="5"><b>Custom-Modeled</b></td>
  <td>C_O_Ring_L, C_O_Ring_M, C_O_Ring_S</td>
</tr>

<tr><td>C_Plastic_Washer_L, C_Plastic_Washer_S</td></tr>

<tr><td>C_Steel_Ball_L, C_Steel_Ball_S</td></tr>

<tr><td>C_Washer_M5</td></tr>

<tr><td>C_Washer_M6</td></tr>

<tr>
  <td rowspan=""><b>FATH GmbH</b></td>
  <td>F_Roll-in_Nut_M5</td>
</tr>

<tr>
  <td rowspan="6"><b>Festo SE & Co. KG</b></td>
  <td>FestoI</td>
</tr>

<tr><td>FestoT</td></tr>

<tr><td>Festo_Torch</td></tr>

<tr><td>FestoV</td></tr>

<tr><td>FestoX</td></tr>

<tr><td>FestoY</td></tr>

<tr>
  <td rowspan="8"><b>GlobalFastener Inc.</b></td>
  <td>GF_Collar_L, GF_Collar_S</td>
</tr>

<tr><td>GF_Slotted_Pin_L, GF_Slotted_Pin_S</td></tr>

<tr><td>GF_Split_Pin_L, GF_Split_Pin_S</td></tr>

<tr><td>GF_Cone_Screw_M8</td></tr>

<tr><td>GF_Hexagon_Nut</td></tr>

<tr><td>GF_Knurled_Screw_M8</td></tr>

<tr><td>GF_Plain_Screw_M8</td></tr>

<tr><td>GF_Screw_M5</td></tr>

<!-- McMaster Section -->
<tr>
  <td rowspan="4"><b>McMaster-Carr Supply Co.</b></td>
  <td>MM_Silencer_L, MM_Silencer_S</td>
</tr>

<tr><td>MM_Spring</td></tr>

<tr><td>MM_Wing</td></tr>

<tr><td>MM_Wood_Screw</td></tr>

</tbody>
</table>

## License
See LICENSE.txt for terms and conditions.

## Contact
For questions, please contact the corresponding authors of the paper.