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# YOLOv8-TO (WORK IN PROGRESS)
Code for the article "From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures"
## Table of Contents
- [Overview](#overview)
- [Reference](#reference)
- [Installation](#installation)
- [Prerequisites](#prerequisites)
- [Installing](#installing)
- [Datasets](#datasets)
## Overview
Brief description of what the project does and the problem it solves. Include a link or reference to the original article that inspired or is associated with this implementation.
## Reference
This code aims to reproduce the results presented in the research article:
```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}
}
```
## Installation
### Prerequisites
This package comes with a fork of the ultralytics package in the yolov8-to directory. The fork is necessary to add the functionality of the design variables regression.
### Installing
```bash
git clone https://github.com/COSIM-Lab/YOLOv8-TO.git
cd YOLOv8-TO
pip install -e .
```
## Datasets
Links to the dataset on HuggingFace:
- [YOLOv8-TO_Data](https://huggingface.co/datasets/tomrb/yolov8to_data)
The Huggingface dataset contains the following datasets (see paper for details):
- MMC
- MMC-random
- SIMP
- SIMP_5%
- OOD
If you want to use one of the linked datasets, please unzip it inside of the datasets folder. Training labels are provided for the MMC and MMC-random data. To train on the data, please update the data.yaml file with the correct path to the dataset.
```yaml
path: # dataset root dir
```
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