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---
license: mit
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card for YOLOv8-TO_Data
<!-- Provide a quick summary of the dataset. -->
The TopOpt Datasets Collection comprises several datasets utilized to evaluate the performance and generalization capabilities of machine learning models specifically tailored for topology optimization (TO). This collection is designed to test models across various TO methods and structural complexities, from optimized structures to random assemblies and out-of-distribution (OOD) samples.
## Dataset Details
### Dataset Description
- **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 [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/COSIM-Lab/YOLOv8-TO
- **Paper:** https://arxiv.org/pdf/2404.18763
- **Demo:** [WORK IN PROGRESS]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- 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}
}
``` |