This repository contains four image datasets used in the paper:

Analyzing Feature Relevance Under Occlusion in Small Load Carrier Detection

with the github: https://github.com/TrescherDe/Visualizing-and-Interpreting-Neural-Network-Features/tree/main

Each dataset contains 500 images and is split into train/ and val/ subsets.

• Real: A dataset containing real images of small load carriers and a small storage box with material properties similar to the small load carrier.
• Storage Box: The baseline synthetic dataset containing images generated using Blender and 3D meshes of small load carriers and a small storage box with similar material properties.
• SD-V1: A baseline-extended dataset augmenting the baseline using Stable Diffusion.
• SD-V2: A baseline-extended dataset augmenting the baseline using Stable Diffusion, focusing on photorealism.
• Test video: A dataset containing real images of the small load carrier, a small storage box with similar material properties, and other distracting objects in a possible use case scenario.

If you use these datasets, please cite this repository:

@misc{small-load-carrier-dataset,
  title        = {small-load-carrier-dataset},
  author       = {Trescher, Denis and Haag, Waldemar},
  year         = {2025},
  note         = {Available at https://github.com/TrescherDe/small-load-carrier-dataset}
}

This dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

You are free to share and adapt the material for any purpose, even commercially, as long as you provide proper attribution.