---
license: mit
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card for YOLOv8-TO_Data

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This dataset contains the training and testing sets for the YOLOv8-TO paper.

## Dataset Description
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- **Created by:** Thomas Rochefort-Beaudoin
- **License:** MIT
- **Datasets**
  - ***MMC Dataset***
Description: The MMC (Minimum Compliance) dataset is derived using the MMC method as the basis for the training dataset, where the segmentation labels are generated from black-and-white density projections obtained via a Heaviside projection.
Split: 80% training, 10% validation, 10% testing
Usage: Model training and evaluation
  - ***Random Assembly Dataset***
Description: This dataset consists of assemblies composed of randomly distributed components, generated to allow for cost-effective training data production. The design variables sampled randomly define the segmentation labels for detection and regression tasks.
Usage: Training only
  - ***SIMP Dataset***
Description: Generated using the Solid Isotropic Material with Penalization (SIMP) method, this dataset includes 2000 TO structures, allowing to test the model's capability as a general post-processing tool.
Samples: 2000
Usage: Testing
  - ***Low Volume Fraction SIMP Dataset (SIMP5%)***
Description: Comprising 2000 random SIMP samples with a low volume fraction (5%), this dataset features thin structures that simulate "truss-like" properties suitable for comparison against skeletonization approaches.
Samples: 2000
Usage: Testing
   - ***Out-of-Distribution (OOD) Dataset***
Description: This dataset includes 4 TO structure images from the literature, featuring complex structures like 2D femur structures and cantilever beams optimized under various constraints to test the model's generalization capabilities.
Samples: 4
Usage: Testing

### Dataset Sources

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- **Repository:** https://github.com/COSIM-Lab/YOLOv8-TO
- **Paper:** https://arxiv.org/pdf/2404.18763
- **Demo:** https://huggingface.co/spaces/tomrb/YOLOv8-TO

## Dataset Structure

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IMPORTANT: The dataset currently has the design variables of each component in position 1 to 7 in each row. These design variables are currently ignored by the YOLOv8-TO library and are artifacts of when we were trying to do regression directly on the design variables.


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@misc{rochefortbeaudoin2024density,
      title={From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures}, 
      author={Thomas Rochefort-Beaudoin and Aurelian Vadean and Sofiane Achiche and Niels Aage},
      year={2024},
      eprint={2404.18763},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```