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. The goal is to provide a complete, reproducible benchmark for evaluating and advancing sim-to-real methods in industrial robotics.

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	F_Roll-in_Nut_M5
Festo	FestoI
	FestoT
	Festo_Torch
	FestoV
	FestoX
	FestoY
Global Fastener	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	MM_Silencer_L, MM_Silencer_S
	MM_Spring
	MM_Wing
	MM_Wood_Screw

Citation

If you use this dataset in your research, please cite our paper.

License

See LICENSE.txt for terms and conditions.

Contact

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