metadata

license: mit
size_categories:
  - 10K<n<100K
viewer: false

Dataset Card for YOLOv8-TO_Data

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]

Repository: https://github.com/COSIM-Lab/YOLOv8-TO
Paper: https://arxiv.org/pdf/2404.18763
Demo: [WORK IN PROGRESS]

Uses

Direct Use

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Dataset Structure

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Dataset Creation

Source Data

Data Collection and Processing

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

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}
}