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--- |
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license: mit |
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size_categories: |
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- 10K<n<100K |
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viewer: false |
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--- |
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# Dataset Card for YOLOv8-TO_Data |
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<!-- Provide a quick summary of the dataset. --> |
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This dataset contains the training and testing sets for the YOLOv8-TO paper. |
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## Dataset Description |
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<!-- Provide the basic links for the dataset. --> |
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- **Created by:** Thomas Rochefort-Beaudoin |
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- **License:** MIT |
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- **Datasets** |
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- ***MMC Dataset*** |
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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. |
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Split: 80% training, 10% validation, 10% testing |
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Usage: Model training and evaluation |
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- ***Random Assembly Dataset*** |
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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. |
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Usage: Training only |
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- ***SIMP Dataset*** |
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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. |
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Samples: 2000 |
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Usage: Testing |
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- ***Low Volume Fraction SIMP Dataset (SIMP5%)*** |
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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. |
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Samples: 2000 |
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Usage: Testing |
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- ***Out-of-Distribution (OOD) Dataset*** |
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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. |
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Samples: 4 |
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Usage: Testing |
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### Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** https://github.com/COSIM-Lab/YOLOv8-TO |
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- **Paper:** https://arxiv.org/pdf/2404.18763 |
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- **Demo:** https://huggingface.co/spaces/tomrb/YOLOv8-TO |
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## Dataset Structure |
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<!-- 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. --> |
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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. |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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``` |
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@misc{rochefortbeaudoin2024density, |
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title={From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures}, |
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author={Thomas Rochefort-Beaudoin and Aurelian Vadean and Sofiane Achiche and Niels Aage}, |
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year={2024}, |
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eprint={2404.18763}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
