  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.
