BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design

Lyle Regenwetter, Brent Curry, Faez Ahmed

In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and generally support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset and present the various features provided. We then illustrate the scale, variety, and structure of the data using several unsupervised clustering studies. Next, we explore a variety of data-driven applications. We provide baseline classification performance for 10 algorithms trained on differing amounts of training data. We then contrast classification performance of three deep neural networks using parametric data, image data, and a combination of the two. Using one of the trained classification models, we conduct a Shapley Additive Explanations Analysis to better understand the extent to which certain design parameters impact classification predictions. Next, we test bike reconstruction and design synthesis using two Variational Autoencoders (VAEs) trained on images and parametric data. We furthermore contrast the performance of interpolation and extrapolation tasks in the original parameter space and the latent space of a VAE. Finally, we discuss some exciting possibilities for other applications beyond the few actively explored in this paper and summarize overall strengths and weaknesses of the dataset.

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