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---
license: cc-by-nc-4.0
---
# Dataset Card for BBBicycles
## Dataset Summary
Bent & Broken Bicycles (BBBicycles) dataset is a benchmark set for the novel task of **damaged object re-identification**, which aims to identify the same object in multiple images even in the presence of breaks, deformations, and missing parts. You can find an interactive preview [here](https://huggingface.co/spaces/GrainsPolito/BBBicyclesPreview).
## Dataset Structure
The final dataset contains:
- Total of 39,200 image
- 2,800 unique IDs
- 20 models
- 140 IDs for each model
<table border-collapse="collapse">
<tr>
<td><b style="font-size:25px">Information for each ID:</b></td>
<td><b style="font-size:25px">Information for each render:</b></td>
</tr>
<tr>
<td>
<ul>
<li>Model</li>
<li>Type</li>
<li>Texture type</li>
<li>Stickers</li>
</ul>
</td>
<td>
<ul>
<li>Background</li>
<li>Viewing Side</li>
<li>Focal Length</li>
<li>Presence of dirt</li>
</ul>
</td>
</tr>
</table>
### Citation Information
```
@inproceedings{bbb_2022,
title={Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification},
author={Luca Piano, Filippo Gabriele Pratticò, Alessandro Sebastian Russo, Lorenzo Lanari, Lia Morra, Fabrizio Lamberti},
booktitle={2022 IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2022},
organization={IEEE}
}
```
### Credits
The authors gratefully acknowledge the financial support of Reale Mutua Assicurazioni. |